diff --git a/SpryAssets/SpryAccordion.css b/SpryAssets/SpryAccordion.css deleted file mode 100644 index 604a3c6..0000000 --- a/SpryAssets/SpryAccordion.css +++ /dev/null @@ -1,131 +0,0 @@ -@charset "UTF-8"; - -/* SpryAccordion.css - version 0.5 - Spry Pre-Release 1.6.1 */ - -/* Copyright (c) 2006. Adobe Systems Incorporated. All rights reserved. */ - -/* This is the selector for the main Accordion container. For our default style, - * we draw borders on the left, right, and bottom. The top border of the Accordion - * will be rendered by the first AccordionPanelTab which never moves. - * - * If you want to constrain the width of the Accordion widget, set a width on - * the Accordion container. By default, our accordion expands horizontally to fill - * up available space. - * - * The name of the class ("Accordion") used in this selector is not necessary - * to make the widget function. You can use any class name you want to style the - * Accordion container. - */ -.Accordion { - border-left: solid 1px gray; - border-right: solid 1px black; - border-bottom: solid 1px gray; - overflow: hidden; -} - -/* This is the selector for the AccordionPanel container which houses the - * panel tab and a panel content area. It doesn't render visually, but we - * make sure that it has zero margin and padding. - * - * The name of the class ("AccordionPanel") used in this selector is not necessary - * to make the widget function. You can use any class name you want to style an - * accordion panel container. -*/ -.AccordionPanel { - margin: 0px; - padding: 0px; -} - -/* This is the selector for the AccordionPanelTab. This container houses - * the title for the panel. This is also the container that the user clicks - * on to open a specific panel. - * - * The name of the class ("AccordionPanelTab") used in this selector is not necessary - * to make the widget function. You can use any class name you want to style an - * accordion panel tab container. - * - * NOTE: - * This rule uses -moz-user-select and -khtml-user-select properties to prevent the - * user from selecting the text in the AccordionPanelTab. These are proprietary browser - * properties that only work in Mozilla based browsers (like FireFox) and KHTML based - * browsers (like Safari), so they will not pass W3C validation. If you want your documents to - * validate, and don't care if the user can select the text within an AccordionPanelTab, - * you can safely remove those properties without affecting the functionality of the widget. - */ -.AccordionPanelTab { - background-color: #CCCCCC; - border-top: solid 1px black; - border-bottom: solid 1px gray; - margin: 0px; - padding: 2px; - cursor: pointer; - -moz-user-select: none; - -khtml-user-select: none; -} - -/* This is the selector for a Panel's Content area. It's important to note that - * you should never put any padding on the panel's content area if you plan to - * use the Accordions panel animations. Placing a non-zero padding on the content - * area can cause the accordion to abruptly grow in height while the panels animate. - * - * Anyone who styles an Accordion *MUST* specify a height on the Accordion Panel - * Content container. - * - * The name of the class ("AccordionPanelContent") used in this selector is not necessary - * to make the widget function. You can use any class name you want to style an - * accordion panel content container. - */ -.AccordionPanelContent { - overflow: auto; - margin: 0px; - padding: 0px; - height: 200px; -} - -/* This is an example of how to change the appearance of the panel tab that is - * currently open. The class "AccordionPanelOpen" is programatically added and removed - * from panels as the user clicks on the tabs within the Accordion. - */ -.AccordionPanelOpen .AccordionPanelTab { - background-color: #EEEEEE; -} - -/* This is an example of how to change the appearance of the panel tab as the - * mouse hovers over it. The class "AccordionPanelTabHover" is programatically added - * and removed from panel tab containers as the mouse enters and exits the tab container. - */ -.AccordionPanelTabHover { - color: #555555; -} -.AccordionPanelOpen .AccordionPanelTabHover { - color: #555555; -} - -/* This is an example of how to change the appearance of all the panel tabs when the - * Accordion has focus. The "AccordionFocused" class is programatically added and removed - * whenever the Accordion gains or loses keyboard focus. - */ -.AccordionFocused .AccordionPanelTab { - background-color: #3399FF; -} - -/* This is an example of how to change the appearance of the panel tab that is - * currently open when the Accordion has focus. - */ -.AccordionFocused .AccordionPanelOpen .AccordionPanelTab { - background-color: #33CCFF; -} -/* Rules for Printing */ - -@media print { - - .Accordion { - overflow: visible !important; - } - - .AccordionPanelContent { - display: block !important; - overflow: visible !important; - height: auto !important; - } -} \ No newline at end of file diff --git a/SpryAssets/SpryAccordion.js b/SpryAssets/SpryAccordion.js deleted file mode 100644 index 3fafa99..0000000 --- a/SpryAssets/SpryAccordion.js +++ /dev/null @@ -1,561 +0,0 @@ -// SpryAccordion.js - version 0.17 - Spry Pre-Release 1.6.1 -// -// Copyright (c) 2006. Adobe Systems Incorporated. -// All rights reserved. -// -// Redistribution and use in source and binary forms, with or without -// modification, are permitted provided that the following conditions are met: -// -// * Redistributions of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// * Redistributions in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// * Neither the name of Adobe Systems Incorporated nor the names of its -// contributors may be used to endorse or promote products derived from this -// software without specific prior written permission. -// -// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE -// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE -// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF -// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS -// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN -// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) -// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE -// POSSIBILITY OF SUCH DAMAGE. - -(function() { // BeginSpryComponent - -if (typeof Spry == "undefined") window.Spry = {}; if (!Spry.Widget) Spry.Widget = {}; - -Spry.Widget.Accordion = function(element, opts) -{ - this.element = this.getElement(element); - this.defaultPanel = 0; - this.hoverClass = "AccordionPanelTabHover"; - this.openClass = "AccordionPanelOpen"; - this.closedClass = "AccordionPanelClosed"; - this.focusedClass = "AccordionFocused"; - this.enableAnimation = true; - this.enableKeyboardNavigation = true; - this.currentPanel = null; - this.animator = null; - this.hasFocus = null; - - this.previousPanelKeyCode = Spry.Widget.Accordion.KEY_UP; - this.nextPanelKeyCode = Spry.Widget.Accordion.KEY_DOWN; - - this.useFixedPanelHeights = true; - this.fixedPanelHeight = 0; - - Spry.Widget.Accordion.setOptions(this, opts, true); - - if (this.element) - this.attachBehaviors(); -}; - -Spry.Widget.Accordion.prototype.getElement = function(ele) -{ - if (ele && typeof ele == "string") - return document.getElementById(ele); - return ele; -}; - -Spry.Widget.Accordion.prototype.addClassName = function(ele, className) -{ - if (!ele || !className || (ele.className && ele.className.search(new RegExp("\\b" + className + "\\b")) != -1)) - return; - ele.className += (ele.className ? " " : "") + className; -}; - -Spry.Widget.Accordion.prototype.removeClassName = function(ele, className) -{ - if (!ele || !className || (ele.className && ele.className.search(new RegExp("\\b" + className + "\\b")) == -1)) - return; - ele.className = ele.className.replace(new RegExp("\\s*\\b" + className + "\\b", "g"), ""); -}; - -Spry.Widget.Accordion.setOptions = function(obj, optionsObj, ignoreUndefinedProps) -{ - if (!optionsObj) - return; - for (var optionName in optionsObj) - { - if (ignoreUndefinedProps && optionsObj[optionName] == undefined) - continue; - obj[optionName] = optionsObj[optionName]; - } -}; - -Spry.Widget.Accordion.prototype.onPanelTabMouseOver = function(e, panel) -{ - if (panel) - this.addClassName(this.getPanelTab(panel), this.hoverClass); - return false; -}; - -Spry.Widget.Accordion.prototype.onPanelTabMouseOut = function(e, panel) -{ - if (panel) - this.removeClassName(this.getPanelTab(panel), this.hoverClass); - return false; -}; - -Spry.Widget.Accordion.prototype.openPanel = function(elementOrIndex) -{ - var panelA = this.currentPanel; - var panelB; - - if (typeof elementOrIndex == "number") - panelB = this.getPanels()[elementOrIndex]; - else - panelB = this.getElement(elementOrIndex); - - if (!panelB || panelA == panelB) - return null; - - var contentA = panelA ? this.getPanelContent(panelA) : null; - var contentB = this.getPanelContent(panelB); - - if (!contentB) - return null; - - if (this.useFixedPanelHeights && !this.fixedPanelHeight) - this.fixedPanelHeight = (contentA.offsetHeight) ? contentA.offsetHeight : contentA.scrollHeight; - - if (this.enableAnimation) - { - if (this.animator) - this.animator.stop(); - this.animator = new Spry.Widget.Accordion.PanelAnimator(this, panelB, { duration: this.duration, fps: this.fps, transition: this.transition }); - this.animator.start(); - } - else - { - if(contentA) - { - contentA.style.display = "none"; - contentA.style.height = "0px"; - } - contentB.style.display = "block"; - contentB.style.height = this.useFixedPanelHeights ? this.fixedPanelHeight + "px" : "auto"; - } - - if(panelA) - { - this.removeClassName(panelA, this.openClass); - this.addClassName(panelA, this.closedClass); - } - - this.removeClassName(panelB, this.closedClass); - this.addClassName(panelB, this.openClass); - - this.currentPanel = panelB; - - return panelB; -}; - -Spry.Widget.Accordion.prototype.closePanel = function() -{ - // The accordion can only ever have one panel open at any - // give time, so this method only closes the current panel. - // If the accordion is in fixed panel heights mode, this - // method does nothing. - - if (!this.useFixedPanelHeights && this.currentPanel) - { - var panel = this.currentPanel; - var content = this.getPanelContent(panel); - if (content) - { - if (this.enableAnimation) - { - if (this.animator) - this.animator.stop(); - this.animator = new Spry.Widget.Accordion.PanelAnimator(this, null, { duration: this.duration, fps: this.fps, transition: this.transition }); - this.animator.start(); - } - else - { - content.style.display = "none"; - content.style.height = "0px"; - } - } - this.removeClassName(panel, this.openClass); - this.addClassName(panel, this.closedClass); - this.currentPanel = null; - } -}; - -Spry.Widget.Accordion.prototype.openNextPanel = function() -{ - return this.openPanel(this.getCurrentPanelIndex() + 1); -}; - -Spry.Widget.Accordion.prototype.openPreviousPanel = function() -{ - return this.openPanel(this.getCurrentPanelIndex() - 1); -}; - -Spry.Widget.Accordion.prototype.openFirstPanel = function() -{ - return this.openPanel(0); -}; - -Spry.Widget.Accordion.prototype.openLastPanel = function() -{ - var panels = this.getPanels(); - return this.openPanel(panels[panels.length - 1]); -}; - -Spry.Widget.Accordion.prototype.onPanelTabClick = function(e, panel) -{ - if (panel != this.currentPanel) - this.openPanel(panel); - else - this.closePanel(); - - if (this.enableKeyboardNavigation) - this.focus(); - - if (e.preventDefault) e.preventDefault(); - else e.returnValue = false; - if (e.stopPropagation) e.stopPropagation(); - else e.cancelBubble = true; - - return false; -}; - -Spry.Widget.Accordion.prototype.onFocus = function(e) -{ - this.hasFocus = true; - this.addClassName(this.element, this.focusedClass); - return false; -}; - -Spry.Widget.Accordion.prototype.onBlur = function(e) -{ - this.hasFocus = false; - this.removeClassName(this.element, this.focusedClass); - return false; -}; - -Spry.Widget.Accordion.KEY_UP = 38; -Spry.Widget.Accordion.KEY_DOWN = 40; - -Spry.Widget.Accordion.prototype.onKeyDown = function(e) -{ - var key = e.keyCode; - if (!this.hasFocus || (key != this.previousPanelKeyCode && key != this.nextPanelKeyCode)) - return true; - - var panels = this.getPanels(); - if (!panels || panels.length < 1) - return false; - var currentPanel = this.currentPanel ? this.currentPanel : panels[0]; - var nextPanel = (key == this.nextPanelKeyCode) ? currentPanel.nextSibling : currentPanel.previousSibling; - - while (nextPanel) - { - if (nextPanel.nodeType == 1 /* Node.ELEMENT_NODE */) - break; - nextPanel = (key == this.nextPanelKeyCode) ? nextPanel.nextSibling : nextPanel.previousSibling; - } - - if (nextPanel && currentPanel != nextPanel) - this.openPanel(nextPanel); - - if (e.preventDefault) e.preventDefault(); - else e.returnValue = false; - if (e.stopPropagation) e.stopPropagation(); - else e.cancelBubble = true; - - return false; -}; - -Spry.Widget.Accordion.prototype.attachPanelHandlers = function(panel) -{ - if (!panel) - return; - - var tab = this.getPanelTab(panel); - - if (tab) - { - var self = this; - Spry.Widget.Accordion.addEventListener(tab, "click", function(e) { return self.onPanelTabClick(e, panel); }, false); - Spry.Widget.Accordion.addEventListener(tab, "mouseover", function(e) { return self.onPanelTabMouseOver(e, panel); }, false); - Spry.Widget.Accordion.addEventListener(tab, "mouseout", function(e) { return self.onPanelTabMouseOut(e, panel); }, false); - } -}; - -Spry.Widget.Accordion.addEventListener = function(element, eventType, handler, capture) -{ - try - { - if (element.addEventListener) - element.addEventListener(eventType, handler, capture); - else if (element.attachEvent) - element.attachEvent("on" + eventType, handler); - } - catch (e) {} -}; - -Spry.Widget.Accordion.prototype.initPanel = function(panel, isDefault) -{ - var content = this.getPanelContent(panel); - if (isDefault) - { - this.currentPanel = panel; - this.removeClassName(panel, this.closedClass); - this.addClassName(panel, this.openClass); - - // Attempt to set up the height of the default panel. We don't want to - // do any dynamic panel height calculations here because our accordion - // or one of its parent containers may be display:none. - - if (content) - { - if (this.useFixedPanelHeights) - { - // We are in fixed panel height mode and the user passed in - // a panel height for us to use. - - if (this.fixedPanelHeight) - content.style.height = this.fixedPanelHeight + "px"; - } - else - { - // We are in variable panel height mode, but since we can't - // calculate the panel height here, we just set the height to - // auto so that it expands to show all of its content. - - content.style.height = "auto"; - } - } - } - else - { - this.removeClassName(panel, this.openClass); - this.addClassName(panel, this.closedClass); - - if (content) - { - content.style.height = "0px"; - content.style.display = "none"; - } - } - - this.attachPanelHandlers(panel); -}; - -Spry.Widget.Accordion.prototype.attachBehaviors = function() -{ - var panels = this.getPanels(); - for (var i = 0; i < panels.length; i++) - this.initPanel(panels[i], i == this.defaultPanel); - - // Advanced keyboard navigation requires the tabindex attribute - // on the top-level element. - - this.enableKeyboardNavigation = (this.enableKeyboardNavigation && this.element.attributes.getNamedItem("tabindex")); - if (this.enableKeyboardNavigation) - { - var self = this; - Spry.Widget.Accordion.addEventListener(this.element, "focus", function(e) { return self.onFocus(e); }, false); - Spry.Widget.Accordion.addEventListener(this.element, "blur", function(e) { return self.onBlur(e); }, false); - Spry.Widget.Accordion.addEventListener(this.element, "keydown", function(e) { return self.onKeyDown(e); }, false); - } -}; - -Spry.Widget.Accordion.prototype.getPanels = function() -{ - return this.getElementChildren(this.element); -}; - -Spry.Widget.Accordion.prototype.getCurrentPanel = function() -{ - return this.currentPanel; -}; - -Spry.Widget.Accordion.prototype.getPanelIndex = function(panel) -{ - var panels = this.getPanels(); - for( var i = 0 ; i < panels.length; i++ ) - { - if( panel == panels[i] ) - return i; - } - return -1; -}; - -Spry.Widget.Accordion.prototype.getCurrentPanelIndex = function() -{ - return this.getPanelIndex(this.currentPanel); -}; - -Spry.Widget.Accordion.prototype.getPanelTab = function(panel) -{ - if (!panel) - return null; - return this.getElementChildren(panel)[0]; -}; - -Spry.Widget.Accordion.prototype.getPanelContent = function(panel) -{ - if (!panel) - return null; - return this.getElementChildren(panel)[1]; -}; - -Spry.Widget.Accordion.prototype.getElementChildren = function(element) -{ - var children = []; - var child = element.firstChild; - while (child) - { - if (child.nodeType == 1 /* Node.ELEMENT_NODE */) - children.push(child); - child = child.nextSibling; - } - return children; -}; - -Spry.Widget.Accordion.prototype.focus = function() -{ - if (this.element && this.element.focus) - this.element.focus(); -}; - -Spry.Widget.Accordion.prototype.blur = function() -{ - if (this.element && this.element.blur) - this.element.blur(); -}; - -///////////////////////////////////////////////////// - -Spry.Widget.Accordion.PanelAnimator = function(accordion, panel, opts) -{ - this.timer = null; - this.interval = 0; - - this.fps = 60; - this.duration = 500; - this.startTime = 0; - - this.transition = Spry.Widget.Accordion.PanelAnimator.defaultTransition; - - this.onComplete = null; - - this.panel = panel; - this.panelToOpen = accordion.getElement(panel); - this.panelData = []; - this.useFixedPanelHeights = accordion.useFixedPanelHeights; - - Spry.Widget.Accordion.setOptions(this, opts, true); - - this.interval = Math.floor(1000 / this.fps); - - // Set up the array of panels we want to animate. - - var panels = accordion.getPanels(); - for (var i = 0; i < panels.length; i++) - { - var p = panels[i]; - var c = accordion.getPanelContent(p); - if (c) - { - var h = c.offsetHeight; - if (h == undefined) - h = 0; - - if (p == panel && h == 0) - c.style.display = "block"; - - if (p == panel || h > 0) - { - var obj = new Object; - obj.panel = p; - obj.content = c; - obj.fromHeight = h; - obj.toHeight = (p == panel) ? (accordion.useFixedPanelHeights ? accordion.fixedPanelHeight : c.scrollHeight) : 0; - obj.distance = obj.toHeight - obj.fromHeight; - obj.overflow = c.style.overflow; - this.panelData.push(obj); - - c.style.overflow = "hidden"; - c.style.height = h + "px"; - } - } - } -}; - -Spry.Widget.Accordion.PanelAnimator.defaultTransition = function(time, begin, finish, duration) { time /= duration; return begin + ((2 - time) * time * finish); }; - -Spry.Widget.Accordion.PanelAnimator.prototype.start = function() -{ - var self = this; - this.startTime = (new Date).getTime(); - this.timer = setTimeout(function() { self.stepAnimation(); }, this.interval); -}; - -Spry.Widget.Accordion.PanelAnimator.prototype.stop = function() -{ - if (this.timer) - { - clearTimeout(this.timer); - - // If we're killing the timer, restore the overflow - // properties on the panels we were animating! - - for (i = 0; i < this.panelData.length; i++) - { - obj = this.panelData[i]; - obj.content.style.overflow = obj.overflow; - } - } - - this.timer = null; -}; - -Spry.Widget.Accordion.PanelAnimator.prototype.stepAnimation = function() -{ - var curTime = (new Date).getTime(); - var elapsedTime = curTime - this.startTime; - - var i, obj; - - if (elapsedTime >= this.duration) - { - for (i = 0; i < this.panelData.length; i++) - { - obj = this.panelData[i]; - if (obj.panel != this.panel) - { - obj.content.style.display = "none"; - obj.content.style.height = "0px"; - } - obj.content.style.overflow = obj.overflow; - obj.content.style.height = (this.useFixedPanelHeights || obj.toHeight == 0) ? obj.toHeight + "px" : "auto"; - } - if (this.onComplete) - this.onComplete(); - return; - } - - for (i = 0; i < this.panelData.length; i++) - { - obj = this.panelData[i]; - var ht = this.transition(elapsedTime, obj.fromHeight, obj.distance, this.duration); - obj.content.style.height = ((ht < 0) ? 0 : ht) + "px"; - } - - var self = this; - this.timer = setTimeout(function() { self.stepAnimation(); }, this.interval); -}; - -})(); // EndSpryComponent diff --git a/SpryAssets/SpryCollapsiblePanel.css b/SpryAssets/SpryCollapsiblePanel.css deleted file mode 100644 index a4bef40..0000000 --- a/SpryAssets/SpryCollapsiblePanel.css +++ /dev/null @@ -1,108 +0,0 @@ -@charset "UTF-8"; - -/* SpryCollapsiblePanel.css - version 0.5 - Spry Pre-Release 1.6.1 */ - -/* Copyright (c) 2006. Adobe Systems Incorporated. All rights reserved. */ - -/* This is the selector for the main CollapsiblePanel container. For our - * default style, the CollapsiblePanel is responsible for drawing the borders - * around the widget. - * - * If you want to constrain the width of the CollapsiblePanel widget, set a width on - * the CollapsiblePanel container. By default, our CollapsiblePanel expands horizontally to fill - * up available space. - * - * The name of the class ("CollapsiblePanel") used in this selector is not necessary - * to make the widget function. You can use any class name you want to style the - * CollapsiblePanel container. - */ -.CollapsiblePanel { - margin: 0px; - padding: 0px; - border-left: solid 1px #CCC; - border-right: solid 1px #999; - border-top: solid 1px #999; - border-bottom: solid 1px #CCC; -} - -/* This is the selector for the CollapsiblePanelTab. This container houses - * the title for the panel. This is also the container that the user clicks - * on to open or close the panel. - * - * The name of the class ("CollapsiblePanelTab") used in this selector is not necessary - * to make the widget function. You can use any class name you want to style an - * CollapsiblePanel panel tab container. - */ -.CollapsiblePanelTab { - font: bold 0.7em sans-serif; - background-color: #EEE; - border-bottom: solid 1px #CCC; - margin: 0px; - padding: 2px; - cursor: pointer; - -moz-user-select: none; - -khtml-user-select: none; -} - -/* This is the selector for a CollapsiblePanel's Content area. It's important to note that - * you should never put any padding on the content area element if you plan to - * use the CollapsiblePanel's open/close animations. Placing a non-zero padding on the content - * element can cause the CollapsiblePanel to abruptly grow in height while the panels animate. - * - * The name of the class ("CollapsiblePanelContent") used in this selector is not necessary - * to make the widget function. You can use any class name you want to style a - * CollapsiblePanel content container. - */ -.CollapsiblePanelContent { - margin: 5px 0px 0px 0px; - padding: 0px; -} - -/* An anchor tag can be used inside of a CollapsiblePanelTab so that the - * keyboard focus ring appears *inside* the tab instead of around the tab. - * This is an example of how to make the text within the anchor tag look - * like non-anchor (normal) text. - */ -.CollapsiblePanelTab a { - color: black; - text-decoration: none; -} - -/* This is an example of how to change the appearance of the panel tab that is - * currently open. The class "CollapsiblePanelOpen" is programatically added and removed - * from panels as the user clicks on the tabs within the CollapsiblePanel. - */ -.CollapsiblePanelOpen .CollapsiblePanelTab { - background-color: #EEE; -} - -/* This is an example of how to change the appearance of the panel tab when the - * CollapsiblePanel is closed. The "CollapsiblePanelClosed" class is programatically added and removed - * whenever the CollapsiblePanel is closed. - */ - -.CollapsiblePanelClosed .CollapsiblePanelTab { - /* background-color: #EFEFEF */ -} - -/* This is an example of how to change the appearance of the panel tab as the - * mouse hovers over it. The class "CollapsiblePanelTabHover" is programatically added - * and removed from panel tab containers as the mouse enters and exits the tab container. - */ -.CollapsiblePanelTabHover, .CollapsiblePanelOpen .CollapsiblePanelTabHover { - background-color: #CCC; -} - -/* This is an example of how to change the appearance of all the panel tabs when the - * CollapsiblePanel has focus. The "CollapsiblePanelFocused" class is programatically added and removed - * whenever the CollapsiblePanel gains or loses keyboard focus. - */ -.CollapsiblePanelFocused .CollapsiblePanelTab { - background-color: #CCC; -} -.CollapsiblePanelContent h5 { - margin-top: 10px; - margin-right: 0px; - margin-bottom: 15px; - margin-left: 0px; -} diff --git a/SpryAssets/SpryCollapsiblePanel.js b/SpryAssets/SpryCollapsiblePanel.js deleted file mode 100644 index ff4d49e..0000000 --- a/SpryAssets/SpryCollapsiblePanel.js +++ /dev/null @@ -1,535 +0,0 @@ -// SpryCollapsiblePanel.js - version 0.8 - Spry Pre-Release 1.6.1 -// -// Copyright (c) 2006. Adobe Systems Incorporated. -// All rights reserved. -// -// Redistribution and use in source and binary forms, with or without -// modification, are permitted provided that the following conditions are met: -// -// * Redistributions of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// * Redistributions in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// * Neither the name of Adobe Systems Incorporated nor the names of its -// contributors may be used to endorse or promote products derived from this -// software without specific prior written permission. -// -// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE -// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE -// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF -// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS -// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN -// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) -// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE -// POSSIBILITY OF SUCH DAMAGE. - -(function() { // BeginSpryComponent - -if (typeof Spry == "undefined") window.Spry = {}; if (!Spry.Widget) Spry.Widget = {}; - -Spry.Widget.CollapsiblePanel = function(element, opts) -{ - this.element = this.getElement(element); - this.focusElement = null; - this.hoverClass = "CollapsiblePanelTabHover"; - this.openClass = "CollapsiblePanelOpen"; - this.closedClass = "CollapsiblePanelClosed"; - this.focusedClass = "CollapsiblePanelFocused"; - this.enableAnimation = true; - this.enableKeyboardNavigation = true; - this.animator = null; - this.hasFocus = false; - this.contentIsOpen = true; - - this.openPanelKeyCode = Spry.Widget.CollapsiblePanel.KEY_DOWN; - this.closePanelKeyCode = Spry.Widget.CollapsiblePanel.KEY_UP; - - Spry.Widget.CollapsiblePanel.setOptions(this, opts); - - this.attachBehaviors(); -}; - -Spry.Widget.CollapsiblePanel.prototype.getElement = function(ele) -{ - if (ele && typeof ele == "string") - return document.getElementById(ele); - return ele; -}; - -Spry.Widget.CollapsiblePanel.prototype.addClassName = function(ele, className) -{ - if (!ele || !className || (ele.className && ele.className.search(new RegExp("\\b" + className + "\\b")) != -1)) - return; - ele.className += (ele.className ? " " : "") + className; -}; - -Spry.Widget.CollapsiblePanel.prototype.removeClassName = function(ele, className) -{ - if (!ele || !className || (ele.className && ele.className.search(new RegExp("\\b" + className + "\\b")) == -1)) - return; - ele.className = ele.className.replace(new RegExp("\\s*\\b" + className + "\\b", "g"), ""); -}; - -Spry.Widget.CollapsiblePanel.prototype.hasClassName = function(ele, className) -{ - if (!ele || !className || !ele.className || ele.className.search(new RegExp("\\b" + className + "\\b")) == -1) - return false; - return true; -}; - -Spry.Widget.CollapsiblePanel.prototype.setDisplay = function(ele, display) -{ - if( ele ) - ele.style.display = display; -}; - -Spry.Widget.CollapsiblePanel.setOptions = function(obj, optionsObj, ignoreUndefinedProps) -{ - if (!optionsObj) - return; - for (var optionName in optionsObj) - { - if (ignoreUndefinedProps && optionsObj[optionName] == undefined) - continue; - obj[optionName] = optionsObj[optionName]; - } -}; - -Spry.Widget.CollapsiblePanel.prototype.onTabMouseOver = function(e) -{ - this.addClassName(this.getTab(), this.hoverClass); - return false; -}; - -Spry.Widget.CollapsiblePanel.prototype.onTabMouseOut = function(e) -{ - this.removeClassName(this.getTab(), this.hoverClass); - return false; -}; - -Spry.Widget.CollapsiblePanel.prototype.open = function() -{ - this.contentIsOpen = true; - if (this.enableAnimation) - { - if (this.animator) - this.animator.stop(); - this.animator = new Spry.Widget.CollapsiblePanel.PanelAnimator(this, true, { duration: this.duration, fps: this.fps, transition: this.transition }); - this.animator.start(); - } - else - this.setDisplay(this.getContent(), "block"); - - this.removeClassName(this.element, this.closedClass); - this.addClassName(this.element, this.openClass); -}; - -Spry.Widget.CollapsiblePanel.prototype.close = function() -{ - this.contentIsOpen = false; - if (this.enableAnimation) - { - if (this.animator) - this.animator.stop(); - this.animator = new Spry.Widget.CollapsiblePanel.PanelAnimator(this, false, { duration: this.duration, fps: this.fps, transition: this.transition }); - this.animator.start(); - } - else - this.setDisplay(this.getContent(), "none"); - - this.removeClassName(this.element, this.openClass); - this.addClassName(this.element, this.closedClass); -}; - -Spry.Widget.CollapsiblePanel.prototype.onTabClick = function(e) -{ - if (this.isOpen()) - this.close(); - else - this.open(); - - this.focus(); - - return this.stopPropagation(e); -}; - -Spry.Widget.CollapsiblePanel.prototype.onFocus = function(e) -{ - this.hasFocus = true; - this.addClassName(this.element, this.focusedClass); - return false; -}; - -Spry.Widget.CollapsiblePanel.prototype.onBlur = function(e) -{ - this.hasFocus = false; - this.removeClassName(this.element, this.focusedClass); - return false; -}; - -Spry.Widget.CollapsiblePanel.KEY_UP = 38; -Spry.Widget.CollapsiblePanel.KEY_DOWN = 40; - -Spry.Widget.CollapsiblePanel.prototype.onKeyDown = function(e) -{ - var key = e.keyCode; - if (!this.hasFocus || (key != this.openPanelKeyCode && key != this.closePanelKeyCode)) - return true; - - if (this.isOpen() && key == this.closePanelKeyCode) - this.close(); - else if ( key == this.openPanelKeyCode) - this.open(); - - return this.stopPropagation(e); -}; - -Spry.Widget.CollapsiblePanel.prototype.stopPropagation = function(e) -{ - if (e.preventDefault) e.preventDefault(); - else e.returnValue = false; - if (e.stopPropagation) e.stopPropagation(); - else e.cancelBubble = true; - return false; -}; - -Spry.Widget.CollapsiblePanel.prototype.attachPanelHandlers = function() -{ - var tab = this.getTab(); - if (!tab) - return; - - var self = this; - Spry.Widget.CollapsiblePanel.addEventListener(tab, "click", function(e) { return self.onTabClick(e); }, false); - Spry.Widget.CollapsiblePanel.addEventListener(tab, "mouseover", function(e) { return self.onTabMouseOver(e); }, false); - Spry.Widget.CollapsiblePanel.addEventListener(tab, "mouseout", function(e) { return self.onTabMouseOut(e); }, false); - - if (this.enableKeyboardNavigation) - { - // XXX: IE doesn't allow the setting of tabindex dynamically. This means we can't - // rely on adding the tabindex attribute if it is missing to enable keyboard navigation - // by default. - - // Find the first element within the tab container that has a tabindex or the first - // anchor tag. - - var tabIndexEle = null; - var tabAnchorEle = null; - - this.preorderTraversal(tab, function(node) { - if (node.nodeType == 1 /* NODE.ELEMENT_NODE */) - { - var tabIndexAttr = tab.attributes.getNamedItem("tabindex"); - if (tabIndexAttr) - { - tabIndexEle = node; - return true; - } - if (!tabAnchorEle && node.nodeName.toLowerCase() == "a") - tabAnchorEle = node; - } - return false; - }); - - if (tabIndexEle) - this.focusElement = tabIndexEle; - else if (tabAnchorEle) - this.focusElement = tabAnchorEle; - - if (this.focusElement) - { - Spry.Widget.CollapsiblePanel.addEventListener(this.focusElement, "focus", function(e) { return self.onFocus(e); }, false); - Spry.Widget.CollapsiblePanel.addEventListener(this.focusElement, "blur", function(e) { return self.onBlur(e); }, false); - Spry.Widget.CollapsiblePanel.addEventListener(this.focusElement, "keydown", function(e) { return self.onKeyDown(e); }, false); - } - } -}; - -Spry.Widget.CollapsiblePanel.addEventListener = function(element, eventType, handler, capture) -{ - try - { - if (element.addEventListener) - element.addEventListener(eventType, handler, capture); - else if (element.attachEvent) - element.attachEvent("on" + eventType, handler); - } - catch (e) {} -}; - -Spry.Widget.CollapsiblePanel.prototype.preorderTraversal = function(root, func) -{ - var stopTraversal = false; - if (root) - { - stopTraversal = func(root); - if (root.hasChildNodes()) - { - var child = root.firstChild; - while (!stopTraversal && child) - { - stopTraversal = this.preorderTraversal(child, func); - try { child = child.nextSibling; } catch (e) { child = null; } - } - } - } - return stopTraversal; -}; - -Spry.Widget.CollapsiblePanel.prototype.attachBehaviors = function() -{ - var panel = this.element; - var tab = this.getTab(); - var content = this.getContent(); - - if (this.contentIsOpen || this.hasClassName(panel, this.openClass)) - { - this.addClassName(panel, this.openClass); - this.removeClassName(panel, this.closedClass); - this.setDisplay(content, "block"); - this.contentIsOpen = true; - } - else - { - this.removeClassName(panel, this.openClass); - this.addClassName(panel, this.closedClass); - this.setDisplay(content, "none"); - this.contentIsOpen = false; - } - - this.attachPanelHandlers(); -}; - -Spry.Widget.CollapsiblePanel.prototype.getTab = function() -{ - return this.getElementChildren(this.element)[0]; -}; - -Spry.Widget.CollapsiblePanel.prototype.getContent = function() -{ - return this.getElementChildren(this.element)[1]; -}; - -Spry.Widget.CollapsiblePanel.prototype.isOpen = function() -{ - return this.contentIsOpen; -}; - -Spry.Widget.CollapsiblePanel.prototype.getElementChildren = function(element) -{ - var children = []; - var child = element.firstChild; - while (child) - { - if (child.nodeType == 1 /* Node.ELEMENT_NODE */) - children.push(child); - child = child.nextSibling; - } - return children; -}; - -Spry.Widget.CollapsiblePanel.prototype.focus = function() -{ - if (this.focusElement && this.focusElement.focus) - this.focusElement.focus(); -}; - -///////////////////////////////////////////////////// - -Spry.Widget.CollapsiblePanel.PanelAnimator = function(panel, doOpen, opts) -{ - this.timer = null; - this.interval = 0; - - this.fps = 60; - this.duration = 500; - this.startTime = 0; - - this.transition = Spry.Widget.CollapsiblePanel.PanelAnimator.defaultTransition; - - this.onComplete = null; - - this.panel = panel; - this.content = panel.getContent(); - this.doOpen = doOpen; - - Spry.Widget.CollapsiblePanel.setOptions(this, opts, true); - - this.interval = Math.floor(1000 / this.fps); - - var c = this.content; - - var curHeight = c.offsetHeight ? c.offsetHeight : 0; - this.fromHeight = (doOpen && c.style.display == "none") ? 0 : curHeight; - - if (!doOpen) - this.toHeight = 0; - else - { - if (c.style.display == "none") - { - // The content area is not displayed so in order to calculate the extent - // of the content inside it, we have to set its display to block. - - c.style.visibility = "hidden"; - c.style.display = "block"; - } - - // Clear the height property so we can calculate - // the full height of the content we are going to show. - - c.style.height = ""; - this.toHeight = c.offsetHeight; - } - - this.distance = this.toHeight - this.fromHeight; - this.overflow = c.style.overflow; - - c.style.height = this.fromHeight + "px"; - c.style.visibility = "visible"; - c.style.overflow = "hidden"; - c.style.display = "block"; -}; - -Spry.Widget.CollapsiblePanel.PanelAnimator.defaultTransition = function(time, begin, finish, duration) { time /= duration; return begin + ((2 - time) * time * finish); }; - -Spry.Widget.CollapsiblePanel.PanelAnimator.prototype.start = function() -{ - var self = this; - this.startTime = (new Date).getTime(); - this.timer = setTimeout(function() { self.stepAnimation(); }, this.interval); -}; - -Spry.Widget.CollapsiblePanel.PanelAnimator.prototype.stop = function() -{ - if (this.timer) - { - clearTimeout(this.timer); - - // If we're killing the timer, restore the overflow property. - - this.content.style.overflow = this.overflow; - } - - this.timer = null; -}; - -Spry.Widget.CollapsiblePanel.PanelAnimator.prototype.stepAnimation = function() -{ - var curTime = (new Date).getTime(); - var elapsedTime = curTime - this.startTime; - - if (elapsedTime >= this.duration) - { - if (!this.doOpen) - this.content.style.display = "none"; - this.content.style.overflow = this.overflow; - this.content.style.height = this.toHeight + "px"; - if (this.onComplete) - this.onComplete(); - return; - } - - var ht = this.transition(elapsedTime, this.fromHeight, this.distance, this.duration); - - this.content.style.height = ((ht < 0) ? 0 : ht) + "px"; - - var self = this; - this.timer = setTimeout(function() { self.stepAnimation(); }, this.interval); -}; - -Spry.Widget.CollapsiblePanelGroup = function(element, opts) -{ - this.element = this.getElement(element); - this.opts = opts; - - this.attachBehaviors(); -}; - -Spry.Widget.CollapsiblePanelGroup.prototype.setOptions = Spry.Widget.CollapsiblePanel.prototype.setOptions; -Spry.Widget.CollapsiblePanelGroup.prototype.getElement = Spry.Widget.CollapsiblePanel.prototype.getElement; -Spry.Widget.CollapsiblePanelGroup.prototype.getElementChildren = Spry.Widget.CollapsiblePanel.prototype.getElementChildren; - -Spry.Widget.CollapsiblePanelGroup.prototype.setElementWidget = function(element, widget) -{ - if (!element || !widget) - return; - if (!element.spry) - element.spry = new Object; - element.spry.collapsiblePanel = widget; -}; - -Spry.Widget.CollapsiblePanelGroup.prototype.getElementWidget = function(element) -{ - return (element && element.spry && element.spry.collapsiblePanel) ? element.spry.collapsiblePanel : null; -}; - -Spry.Widget.CollapsiblePanelGroup.prototype.getPanels = function() -{ - if (!this.element) - return []; - return this.getElementChildren(this.element); -}; - -Spry.Widget.CollapsiblePanelGroup.prototype.getPanel = function(panelIndex) -{ - return this.getPanels()[panelIndex]; -}; - -Spry.Widget.CollapsiblePanelGroup.prototype.attachBehaviors = function() -{ - if (!this.element) - return; - - var cpanels = this.getPanels(); - var numCPanels = cpanels.length; - for (var i = 0; i < numCPanels; i++) - { - var cpanel = cpanels[i]; - this.setElementWidget(cpanel, new Spry.Widget.CollapsiblePanel(cpanel, this.opts)); - } -}; - -Spry.Widget.CollapsiblePanelGroup.prototype.openPanel = function(panelIndex) -{ - var w = this.getElementWidget(this.getPanel(panelIndex)); - if (w && !w.isOpen()) - w.open(); -}; - -Spry.Widget.CollapsiblePanelGroup.prototype.closePanel = function(panelIndex) -{ - var w = this.getElementWidget(this.getPanel(panelIndex)); - if (w && w.isOpen()) - w.close(); -}; - -Spry.Widget.CollapsiblePanelGroup.prototype.openAllPanels = function() -{ - var cpanels = this.getPanels(); - var numCPanels = cpanels.length; - for (var i = 0; i < numCPanels; i++) - { - var w = this.getElementWidget(cpanels[i]); - if (w && !w.isOpen()) - w.open(); - } -}; - -Spry.Widget.CollapsiblePanelGroup.prototype.closeAllPanels = function() -{ - var cpanels = this.getPanels(); - var numCPanels = cpanels.length; - for (var i = 0; i < numCPanels; i++) - { - var w = this.getElementWidget(cpanels[i]); - if (w && w.isOpen()) - w.close(); - } -}; - -})(); // EndSpryComponent diff --git a/SpryAssets/SpryMenuBar.js b/SpryAssets/SpryMenuBar.js deleted file mode 100644 index c6af890..0000000 --- a/SpryAssets/SpryMenuBar.js +++ /dev/null @@ -1,763 +0,0 @@ -// SpryMenuBar.js - version 0.13 - Spry Pre-Release 1.6.1 -// -// Copyright (c) 2006. Adobe Systems Incorporated. -// All rights reserved. -// -// Redistribution and use in source and binary forms, with or without -// modification, are permitted provided that the following conditions are met: -// -// * Redistributions of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// * Redistributions in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// * Neither the name of Adobe Systems Incorporated nor the names of its -// contributors may be used to endorse or promote products derived from this -// software without specific prior written permission. -// -// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE -// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE -// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF -// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS -// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN -// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) -// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE -// POSSIBILITY OF SUCH DAMAGE. - -/******************************************************************************* - - SpryMenuBar.js - This file handles the JavaScript for Spry Menu Bar. You should have no need - to edit this file. Some highlights of the MenuBar object is that timers are - used to keep submenus from showing up until the user has hovered over the parent - menu item for some time, as well as a timer for when they leave a submenu to keep - showing that submenu until the timer fires. - - *******************************************************************************/ - -(function() { // BeginSpryComponent - -if (typeof Spry == "undefined") window.Spry = {}; if (!Spry.Widget) Spry.Widget = {}; - -Spry.BrowserSniff = function() -{ - var b = navigator.appName.toString(); - var up = navigator.platform.toString(); - var ua = navigator.userAgent.toString(); - - this.mozilla = this.ie = this.opera = this.safari = false; - var re_opera = /Opera.([0-9\.]*)/i; - var re_msie = /MSIE.([0-9\.]*)/i; - var re_gecko = /gecko/i; - var re_safari = /(applewebkit|safari)\/([\d\.]*)/i; - var r = false; - - if ( (r = ua.match(re_opera))) { - this.opera = true; - this.version = parseFloat(r[1]); - } else if ( (r = ua.match(re_msie))) { - this.ie = true; - this.version = parseFloat(r[1]); - } else if ( (r = ua.match(re_safari))) { - this.safari = true; - this.version = parseFloat(r[2]); - } else if (ua.match(re_gecko)) { - var re_gecko_version = /rv:\s*([0-9\.]+)/i; - r = ua.match(re_gecko_version); - this.mozilla = true; - this.version = parseFloat(r[1]); - } - this.windows = this.mac = this.linux = false; - - this.Platform = ua.match(/windows/i) ? "windows" : - (ua.match(/linux/i) ? "linux" : - (ua.match(/mac/i) ? "mac" : - ua.match(/unix/i)? "unix" : "unknown")); - this[this.Platform] = true; - this.v = this.version; - - if (this.safari && this.mac && this.mozilla) { - this.mozilla = false; - } -}; - -Spry.is = new Spry.BrowserSniff(); - -// Constructor for Menu Bar -// element should be an ID of an unordered list ( + + +###Kamal Sen +####Boston University + + Website + How do neurons in the brain encode complex natural sounds? What are the neural substrates of selectivity for and discrimination of different categories of natural sounds? Are these substrates innate or shaped by learning? + Our laboratory investigates these questions in the model system of the songbird. Electrophysiological techniques are used to record neural responses from hierarchical stages of auditory processing. Theoretical methods from areas such as statistical signal processing, systems theory, probability theory, information theory and pattern recognition are applied to characterize how neurons in the brain encode natural sounds. Computational models are constructed to understand the processing of natural sounds both at the single neuron and the network level, to model neural selectivity and discrimination, and to explore the role of learning in shaping the neural code. + + +###Barbara Shinn-Cunningham +####Boston University + + Website + Research in the Auditory Neuroscience Laboratory addresses how listeners communicate and make sense of sounds in everyday settings. We study everything from basic perceptual sensitivity to the ways in which different brain regions coordinate their activity during complex tasks. We use a range of approaches to explore these issues, including human behavioral experiments, human neuroelectric imaging, computational modeling, and, in collaboration with other laboratories, fMRI, animal behavioral experiments, and animal neurophysiology. + + +###Kevin Spencer +####Harvard Medical School + + +###Steven Stufflebeam +####MGH/Harvard Medical School/Martinos Imaging Center + + Website + Dr. Stufflebeam's goal is to develop and translate advanced technology at the Martinos Center into clinical practice. Currently, he is using MEG/EEG, fMRI, and optical imaging to understand how the brain processes neural information. He applies multiple imaging technologies to understand epilepsy, schizophrenia, and brain neoplasms. He is also setting up a clinical MEG service for New England. + + +###Lucia Vaina +####Boston University + + Website + Coming Soon. + + +###Miles Whittington +####Newcastle Universtity, UK + + Website + Dr. Whittington's group has a major interest in mechanisms that generate oscillatory activity with neural networks, how this activity is sustained and how is modulated in various normal and pathological conditions. + + +###Matt Wilson +####MIT + + Website + Research in the Wilson laboratory focuses on the study of information representation across large populations of neurons in the mammalian nervous system, as well as on the mechanisms that underlie formation and maintenance of distributed memories in freely behaving animals. To study the basis of these processes, the lab employs a combination of molecular genetic, electrophysiological, pharmacological, behavioral, and computational approaches. Using techniques that allow the simultaneous activity of ensembles of hundreds of single neurons to be examined in freely behaving animals, the lab examines how memories of places and events are encoded across networks of cells within the hippocampus ¬ a region of the brain long implicated in the processes underlying learning and memory. + + These studies of learning and memory in awake, behaving animals have led to the exploration of the nature of sleep and its role in memory. Previous theories have suggested that sleep states may be involved in the process of memory consolidation, in which memories are transferred from short to longer-term stores and possibly reorganized into more efficient forms. Recent evidence has shown that ensembles of neurons within the hippocampus, which had been activated during behavior are reactivated during periods of dreaming. By reconstructing the content of these states, specific memories can be tracked during the course of the consolidation process. + + Combining the measurement of ongoing neuronal activity with manipulation of molecular genetic targets has allowed the study of how specific cellular mechanisms regulate neural function to produce learning and memory at the behavioral level. Pharmacological blockage of these receptors has allowed the study of their involvement in the rapid changes that occur during both waking and sleeping states. Simultaneous monitoring of areas in the hippocampus and neocortex have allowed study of the downstream effects of activation. + + Taken together, these approaches contribute to the overall research objective: to understand the link from cellular/subcellular mechanisms of plasticity, to neural ensemble representations and interactions, to learning, memory, behavior, and cognition. + diff --git a/people/positions/index.md b/people/positions/index.md new file mode 100644 index 0000000..fdda8bd --- /dev/null +++ b/people/positions/index.md @@ -0,0 +1,31 @@ +--- +layout: subpage +title: "Positions" +--- + +### Postdoctoral Fellowship at the Martinos Center for Biomedical Imaging and the Psychiatric Neuroimaging Division of the Psychiatry Department at Massachusetts General Hospital, Charlestown, MA + +Project: Development of accelerated diffusion and functional MRI scans with real-time motion tracking for children with autism +PI: Dara S. Manoach, Ph.D. +[Manoach Lab](http://nmr.mgh.harvard.edu/manoachlab) + +Our team is developing several technical innovations that will significantly reduce the impact of head motion on fMRI and diffusion data. We are seeking a candidate to work with us to improve data analysis by making fMRI and diffusion analysis motion-aware using both the in-image data and the motion tracking data. + +The Research Fellow will be expected to: + +1. Assist with setting up the data acquisition to ensure that the protocols are well-designed for the analysis questions; +2. Build motion-aware processing tools that work in concert with existing software packages to improve the analysis of fMRI and DWI data; +3. Assist in the processing, interpretation and analysis of the results of both phantom and human studies; and +4. Apply these methods to address clinical research questions in autism. + +Our ideal candidate has a PhD in Computer Science, Electrical Engineering, or related fields, and has experience in signal processing and numerical methods. Candidates with experience in image processing and time series analysis, or specifically in fMRI and diffusion data analysis will be preferred. This position requires strong programming skills, and the candidate is expected to have experience working in C++, scripting languages (e.g., Python), and rapid-prototyping languages for numerical algorithms (e.g., Matlab, Mathematica). Strong communication skills are essential, as the position involves working with an interdisciplinary team of scientists in both MRI physics/engineering and psychology/neuroscience in addition to research coordinators and MRI technologists. Background in cognitive neuroscience and an interest in clinical applications are advantageous. Training in clinical research will be provided. + +Position available immediately. Please send: + +* CV +* statement of post-doctoral and career goals +* writing sample (e.g., a published manuscript) +* letters and/or contact information for three references + +to Dara Manoach at *dara at nmr.mgh.harvard.edu*. + diff --git a/people/postdoc/index.md b/people/postdoc/index.md index bbf51e1..2bdceb1 100644 --- a/people/postdoc/index.md +++ b/people/postdoc/index.md @@ -1,266 +1,105 @@ --- -layout: default -title: "People - Postdoc" +layout: subpage +title: "People - Postdocs" --- -
- People: Postdocs -
-

- Click on a name to view or hide details. -

-

- Last update: 1 October 2012 -

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- Natalie Adams -
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- My interests lie broadly in understanding how the brain represents perceptual information and why - this may differ in pathological states related to autism and schizophrenia. I am tackling this using - in vitro electrophysiological techniques in collaboration with mathematical modellers to look at - mechanisms of network rhythm generation, modulation and interaction in the anterior cingulate - region of prefrontal cortex. -

-

- Currently, my research is focussed on exploring aspects of frontal cortical function that facilitate - learning of sequences of sensory events (e.g. Siegel, M., et al 2009). I am interested in how precise - spike timing in individual neurons or small sub-populations relates to the local field oscillation as a - marker of the overall average of event timing relevant to a given stimulus. -

-

- So far my work has revealed that many neurons in the anterior cingulate cortex possess the ability - to intrinsically oscillate at sub-threshold levels. With varying degrees of tonic excitation these sub- - threshold oscillations (STOs) exist at a variety of frequencies up to c.30Hz. The anterior cingulate - cortex is known to have multiple mechanisms for the generation of gamma rhythms associated with - cognitive function and I will look at how these rhythms interact with cellular STOs to affect spike/ - phase relationships and perhaps code for sequences: The working hypothesis is that assemblies - of cells receiving higher levels of excitation increase the drive to the kinetics responsible for STOs, - ultimately leading to a spike phase-advance on each period of field gamma. -

-

- This work could uncover a substrate for the stable, computationally useful, temporal separation of - concurrently active sensory representations. -

-
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- Mikio Aoi -
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- My work has been focused on two aspects of the analysis of neural rhythms. The first project, which I have been working on with Uri Eden, Mark Kramer, and Kyle Lepage, has focused on the spectral analysis of spike trains including coherence between signals when at least one of those signals is a spike train. I use point process theory to derive properties of point process spectra and the estimators, and try to understand how those properties may help or hinder our understanding of the underlying neural system. The second project is in collaboration with Timothy Gardner and Uri Eden in which we are investigating methods of multi-scale time-frequency analysis based on an object-based signal representation. This method will allow us to extract signal information using multiple times scales simultaneously. This method may help to construct sharper spectral representations than are currently possible and we believe this operation may help to understand the phenomenology of human auditory perception. -

-
-
+####Click on a name to view or hide details. Last update: 1 October 2012 -
-
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- Justin Kinney -
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-

- Recording of neuronal spiking activity in distributed brain circuits - requires a scalable design for massively parallel recording of - extracellular field potentials. We are inventing such a system and - implementing a proof-of-concept instantiation. In this system, - multi-electrode arrays are used, which minimize tissue damage and help - with spike sorting, and time domain multiplexing of analog field - potential acquisition reduces interconnect. Channel data is then - relayed to a custom-designed terabyte capacity storage network via - custom digital circuitry. The storage network is designed to enable - neural data to be analyzed in flexible ways, including the evaluation - of spike sorting methods. -

-

- On the technical side, I am solely responsible for the design and implementation of the ethernet network and high-speed data storage software. In addition, I provide leadership to the project by staying well-versed in all aspects of the system design and maintaining open lines of communication between all technology developers, as well as organizing and documenting the design of the system. -

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+ ## Natalie Adams + + My interests lie broadly in understanding how the brain represents perceptual information and why + this may differ in pathological states related to autism and schizophrenia. I am tackling this using + in vitro electrophysiological techniques in collaboration with mathematical modellers to look at + mechanisms of network rhythm generation, modulation and interaction in the anterior cingulate + region of prefrontal cortex. -
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- Jung Lee -
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- Jung has been working with Kopell and Whittington on several modeling projects. The main one concerns the effects of top-down beta rhythms on attention; Jung showed that such signals resonate with cells in the deep cortical layers, producing gain control and more gamma rhythms in the superficial layers; a paper is almost complete. This work is highly relevant to work done by Miller on top-down attention, and further collaborations are planned. The work also has relevance to aspects of schizophrenia, and conversations are beginning with the group of Kevin Spencer. A second project concerns multiple inhibitory cell types in the rat auditory cortex. See Schizophrenia. -

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+ Currently, my research is focussed on exploring aspects of frontal cortical function that facilitate + learning of sequences of sensory events (e.g. Siegel, M., et al 2009). I am interested in how precise + spike timing in individual neurons or small sub-populations relates to the local field oscillation as a + marker of the overall average of event timing relevant to a given stimulus. -
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- Kyle Lepage -
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- Kyle has been one of the most active members of the data analyis group. In the past year, he has been involved in CRC related activity involving three main subjects and two more tertiary ones. One primary project was a collaboration with the Kramer, Eden and Desimone groups on spike-field association (statistical procedures used to infer relations between a rhythm in a time series, such as a local field potential recording, and the firing activity of single neuron. Mikio Aoi is also involved. There is now a preprint. A second major project is a collaboration with the Eichenbaum and Eden groups on cells that measure time. More technically, the project deals with the development of statistical procedures to separate the relative influence of covariates of interest such as time and rodent position upon neural activity. There are two papers and several popular press articles about this work. The third major project is a collaboration with the Kramer lab, also involving postdoc ShiNung Ching; it is motivated by techniques used in MEG and EEG experiments to find functionally connected networks, as in the Human Connectome. This work deals with principled estimation of the statistical connectivity between nodes in an evoked network. In this paradigm a stimulus is repeatedly applied to network nodes, one at a time, and evoked activity at nodes is used to infer a statistical relation between node activity. There is a preprint. A smaller project with the group of Shinn-Cunningham concerns MEG eigensource. In this work local bias in MEG source estimates is traded for decreased non-spatially local bias due to unavoidable inverse-problem source localization limitation. A final project, with Kramer, deals with removing bias in EEG measurements due to activity present on either an EEG reference electrode or present in a "re-referencing method" -

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+ So far my work has revealed that many neurons in the anterior cingulate cortex possess the ability + to intrinsically oscillate at sub-threshold levels. With varying degrees of tonic excitation these sub- + threshold oscillations (STOs) exist at a variety of frequencies up to c.30Hz. The anterior cingulate + cortex is known to have multiple mechanisms for the generation of gamma rhythms associated with + cognitive function and I will look at how these rhythms interact with cellular STOs to affect spike/ + phase relationships and perhaps code for sequences: The working hypothesis is that assemblies + of cells receiving higher levels of excitation increase the drive to the kinetics responsible for STOs, + ultimately leading to a spike phase-advance on each period of field gamma. -
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- Martin Luessi -
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+ This work could uncover a substrate for the stable, computationally useful, temporal separation of + concurrently active sensory representations. + + ## Mikio Aoi -
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- Morteza Moazami -
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- Morteza is a postdoc in the lab of Miller. He is interested in the functional circuitry for memory and context formation between and within the prefrontal cortex (PFC) and the medial temporal lobe (MTL). PFC neurons reflect the associative relations between stimuli, task instructions, behavioral responses, rewards, etc. Interestingly, MTL neurons show similar properties. Neurophysiological studies have been focused on either the MTL or PFC and the interaction between the MTL and PFC is still unclear. He will simultaneously record - with many electrodes- from the PFC and MTL areas while monkey perform a task that temporally separates neuronal information related to context, sample, and recall of the correct choice. He will investigate the modulation of oscillatory neuronal dynamics between and within the PFC and MTL, during different stage of the task. The second project of Morteza concerns the oscillatory neuronal dynamics of categorization in the PFC. Modulations of neuronal oscillations in the PFC with cognitive demands may regulate whether PFC neurons function as multitaskers. The data from these projects are essential to understanding central questions about the roles of rhythms in cognition, and will provide the basis for modeling efforts. In addition, Morteza helps to run the physiology working group. -

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-
+ My work has been focused on two aspects of the analysis of neural rhythms. The first project, which I have been working on with Uri Eden, Mark Kramer, and Kyle Lepage, has focused on the spectral analysis of spike trains including coherence between signals when at least one of those signals is a spike train. I use point process theory to derive properties of point process spectra and the estimators, and try to understand how those properties may help or hinder our understanding of the underlying neural system. The second project is in collaboration with Timothy Gardner and Uri Eden in which we are investigating methods of multi-scale time-frequency analysis based on an object-based signal representation. This method will allow us to extract signal information using multiple times scales simultaneously. This method may help to construct sharper spectral representations than are currently possible and we believe this operation may help to understand the phenomenology of human auditory perception. + + ## Justin Kinney -
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- Lara Rangel -
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- Lara is a new postdoc in the lab of Eichebaum (BU). Her work was described above. She interacts frequently with members of the statistics and modeling groups (Eden, Kramer, Kopell). She also talks frequently with members of other CRC labs (Boyden lab, MIT : Annabelle Singer; Wilson lab, MIT : Greg Hale, Sage Chen, and Stuart Layton) to compare data, methodology, and analysis techniques. She is planning further interactions with Omar Ahmed of the Cash lab (MGH) on hippocampal oscillatory activity in humans, the Miller lab (MIT) on beta frequency oscillations, and the Jiamin Zhuo and Nick James of the Han lab (BU), on dentate gyrus function and beta rhythms, respectively. She has also interacted with Whittington at CRC events. She speaks frequently with postdocs Annabelle Singer, Justin Kinney, Omar Ahmed, and Kyle Lepage as well as graduate students Caroline Moore-Kochlacs, Greg Hale, and all the members of the Eichenbaum lab. Lara has already written a grant proposal based on this work: NIH F-32 Individual Postdoctoral Research Grant -

-
-
+ Recording of neuronal spiking activity in distributed brain circuits + requires a scalable design for massively parallel recording of + extracellular field potentials. We are inventing such a system and + implementing a proof-of-concept instantiation. In this system, + multi-electrode arrays are used, which minimize tissue damage and help + with spike sorting, and time domain multiplexing of analog field + potential acquisition reduces interconnect. Channel data is then + relayed to a custom-designed terabyte capacity storage network via + custom digital circuitry. The storage network is designed to enable + neural data to be analyzed in flexible ways, including the evaluation + of spike sorting methods. -
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- Wei Tang -
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- Wei studies large-scale networks in the resting human brain with data - from non-invasive imaging techniques. Under the hypothesis that - particular sets of brain regions interact with each other to maintain - an active yet stable intrinsic state, the goal of her work is to - uncover both the structure and dynamics of such intrinsic networks, in - the hope that knowledge of the resting state will lead to further - understanding of how neural electrophysiology gives rise to cognitive - phenomena. -

-

- Her current project involves collaborations between several - laboratories. With MEG data acquired by Stufflebeam’s group, Wei and - Steve are looking at seed-based Granger-causality maps, assessing - their spatiotemporal and spectral properties, to explore their - relationship with the proposed default-mode hypothesis. They will - later extend the analysis to task data from the same subjects and see - how the networks change undergoing different cognitive processes. - Meanwhile, supervised by Matti Hamalainen and Uri Eden, Wei interacts - with Patrick Purdon’s group at the Martinos Center, developing a - state-space model based approach to identify the full - source-connectivity matrix of the MEG signal and monitor its change - over time. Efforts are being made to advance the methodology dealing - with high-dimensionality of the data and make the full-network - tractable. The third collaboration is with Mark Kramer, aiming at - finding plausible biophysical models that can explain the observed - network properties. This is an open area of exploration and may serve - further modeling studies on brain disease such as epilepsy. Results - from these collaborations together may provide a comprehensive picture - of how the brain works at different levels. -

-
-
+ On the technical side, I am solely responsible for the design and implementation of the ethernet network and high-speed data storage software. In addition, I provide leadership to the project by staying well-versed in all aspects of the system design and maintaining open lines of communication between all technology developers, as well as organizing and documenting the design of the system. + + ## Jung Lee -
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- Sujith Vijayan -
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- Sujith was a former student of Wilson, now working mostly with Cash and Kopell on aspects of the alpha rhythm and sleep. Details are above. He is the organizer of the Alpha Working Group. -

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-
+ Jung has been working with Kopell and Whittington on several modeling projects. The main one concerns the effects of top-down beta rhythms on attention; Jung showed that such signals resonate with cells in the deep cortical layers, producing gain control and more gamma rhythms in the superficial layers; a paper is almost complete. This work is highly relevant to work done by Miller on top-down attention, and further collaborations are planned. The work also has relevance to aspects of schizophrenia, and conversations are beginning with the group of Kevin Spencer. A second project concerns multiple inhibitory cell types in the rat auditory cortex. + + ## Kyle Lepage - - - - - - - - - - + Kyle has been one of the most active members of the data analyis group. In the past year, he has been involved in CRC related activity involving three main subjects and two more tertiary ones. One primary project was a collaboration with the Kramer, Eden and Desimone groups on spike-field association (statistical procedures used to infer relations between a rhythm in a time series, such as a local field potential recording, and the firing activity of single neuron. Mikio Aoi is also involved. There is now a preprint. A second major project is a collaboration with the Eichenbaum and Eden groups on cells that measure time. More technically, the project deals with the development of statistical procedures to separate the relative influence of covariates of interest such as time and rodent position upon neural activity. There are two papers and several popular press articles about this work. The third major project is a collaboration with the Kramer lab, also involving postdoc ShiNung Ching; it is motivated by techniques used in MEG and EEG experiments to find functionally connected networks, as in the Human Connectome. This work deals with principled estimation of the statistical connectivity between nodes in an evoked network. In this paradigm a stimulus is repeatedly applied to network nodes, one at a time, and evoked activity at nodes is used to infer a statistical relation between node activity. There is a preprint. A smaller project with the group of Shinn-Cunningham concerns MEG eigensource. In this work local bias in MEG source estimates is traded for decreased non-spatially local bias due to unavoidable inverse-problem source localization limitation. A final project, with Kramer, deals with removing bias in EEG measurements due to activity present on either an EEG reference electrode or present in a "re-referencing method" + + ## Martin Luessi + + ## Morteza Moazami + + Morteza is a postdoc in the lab of Miller. He is interested in the functional circuitry for memory and context formation between and within the prefrontal cortex (PFC) and the medial temporal lobe (MTL). PFC neurons reflect the associative relations between stimuli, task instructions, behavioral responses, rewards, etc. Interestingly, MTL neurons show similar properties. Neurophysiological studies have been focused on either the MTL or PFC and the interaction between the MTL and PFC is still unclear. He will simultaneously record - with many electrodes- from the PFC and MTL areas while monkey perform a task that temporally separates neuronal information related to context, sample, and recall of the correct choice. He will investigate the modulation of oscillatory neuronal dynamics between and within the PFC and MTL, during different stage of the task. The second project of Morteza concerns the oscillatory neuronal dynamics of categorization in the PFC. Modulations of neuronal oscillations in the PFC with cognitive demands may regulate whether PFC neurons function as multitaskers. The data from these projects are essential to understanding central questions about the roles of rhythms in cognition, and will provide the basis for modeling efforts. In addition, Morteza helps to run the physiology working group. + + ## Lara Rangel + + Lara is a new postdoc in the lab of Eichebaum (BU). Her work was described above. She interacts frequently with members of the statistics and modeling groups (Eden, Kramer, Kopell). She also talks frequently with members of other CRC labs (Boyden lab, MIT : Annabelle Singer; Wilson lab, MIT : Greg Hale, Sage Chen, and Stuart Layton) to compare data, methodology, and analysis techniques. She is planning further interactions with Omar Ahmed of the Cash lab (MGH) on hippocampal oscillatory activity in humans, the Miller lab (MIT) on beta frequency oscillations, and the Jiamin Zhuo and Nick James of the Han lab (BU), on dentate gyrus function and beta rhythms, respectively. She has also interacted with Whittington at CRC events. She speaks frequently with postdocs Annabelle Singer, Justin Kinney, Omar Ahmed, and Kyle Lepage as well as graduate students Caroline Moore-Kochlacs, Greg Hale, and all the members of the Eichenbaum lab. Lara has already written a grant proposal based on this work: NIH F-32 Individual Postdoctoral Research Grant + + ## Wei Tang + + Wei studies large-scale networks in the resting human brain with data + from non-invasive imaging techniques. Under the hypothesis that + particular sets of brain regions interact with each other to maintain + an active yet stable intrinsic state, the goal of her work is to + uncover both the structure and dynamics of such intrinsic networks, in + the hope that knowledge of the resting state will lead to further + understanding of how neural electrophysiology gives rise to cognitive + phenomena. + + Her current project involves collaborations between several + laboratories. With MEG data acquired by Stufflebeam’s group, Wei and + Steve are looking at seed-based Granger-causality maps, assessing + their spatiotemporal and spectral properties, to explore their + relationship with the proposed default-mode hypothesis. They will + later extend the analysis to task data from the same subjects and see + how the networks change undergoing different cognitive processes. + Meanwhile, supervised by Matti Hamalainen and Uri Eden, Wei interacts + with Patrick Purdon’s group at the Martinos Center, developing a + state-space model based approach to identify the full + source-connectivity matrix of the MEG signal and monitor its change + over time. Efforts are being made to advance the methodology dealing + with high-dimensionality of the data and make the full-network + tractable. The third collaboration is with Mark Kramer, aiming at + finding plausible biophysical models that can explain the observed + network properties. This is an open area of exploration and may serve + further modeling studies on brain disease such as epilepsy. Results + from these collaborations together may provide a comprehensive picture + of how the brain works at different levels. + +## Sujith Vijayan + +Sujith was a former student of Wilson, now working mostly with Cash and Kopell on aspects of the alpha rhythm and sleep. Details are above. He is the organizer of the Alpha Working Group. + diff --git a/people_faculty.htm b/people_faculty.htm deleted file mode 100644 index 6703391..0000000 --- a/people_faculty.htm +++ /dev/null @@ -1,609 +0,0 @@ - - - - - - Cognitive Rhythms Collaborative - - - - - - - - - - - - - - - - - - - - -
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People: Faculty
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Last update: 10 December 2012

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Director

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Nancy Kopell
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Boston University
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Website and Publications -

My major interest is dynamics of the nervous system, especially brain -rhythms associated with cognition. The central questions are: what are the -networks and physiology that produce these rhythms; how do the -physiological properties of those networks affect the use of the dynamics -in cognition; can changes in the rhythms in disease give insights into the -nature and treatments of diseases? I'm currently working on projects -relating to physiology and interaction of rhythms, attention, Parkinson's -disease, schizophrenia and anesthesia; with collaborators, the work -involves in vivo and in vitro experiments, dynamical systems modeling and -simulation, and geometric singular perturbations.

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Executive Committee

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Ed Boyden
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MIT
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Website -

We are inventing new tools for analyzing and engineering brain -circuits. For example, we have devised 'optogenetic' tools, which -enable the activation and silencing of neural circuit elements with -light, to understand their causal contribution to normal and -pathological neural computations, as well as to support the discovery -and repair of neural circuit targets in a therapeutic context. We are -using our inventions to enable systematic approaches to neuroscience, -revealing how neural circuits operate to generate behavior, and -empowering new therapeutic strategies for neurological and psychiatric -disorders.

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Uri Eden
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Boston University
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Website -

My research focuses on developing mathematical and statistical methods to analyze neural spiking activity. I have worked to integrate methodologies related to model identification, statistical inference, signal processing, and stochastic estimation and control, and expand these methodologies to incorporate point process observation models, making them more appropriate for modeling the dynamics of neural systems observed through spike train data. This research can be divided into two categories; first, a methodological component, focused on developing a statistical framework for relating neural activity to biological and behavioral signals and developing estimation algorithms, goodness-of-fit analyses, and mathematical theory that can be applied to any neural spiking system; second, an application component, wherein these methods are applied to spiking observations in real neural systems to dynamically model the spiking properties of individual neurons, to characterize how ensembles maintain representations of associated biological and behavioral signals, and to reconstruct these signals in real time.

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Matti Hämäläinen
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MGH/Harvard Medical School/Martinos Imaging Center
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-Website -

The Athinoula A. Martinos Center at the Massachusetts General Hospital has a twofold mission to advance the development of imaging technologies and -their integration with complementary technologies, and to apply these technologies to support basic -science and translational research that is driven by an overarching interest in the continuous long-term -improvement of clinical care. Martinos Center investigators are innovating in the areas of anatomical -and functional magnetic resonance imaging (MRI) and spectroscopy (MRS), magnetoencephalography -(MEG) and electroencephalography (EEG), near infrared spectroscopy (NIRS) and diffuse optical -tomography (DOT), and positron emission tomography (PET) as well as cutting-edge tools for -computational image analysis. The Center supports over 200 PHS-funded research projects at the -MGH and other Boston-areas institutions, as well as other institutions in the United States and abroad. -Research activities at the Martinos Center are supported institutionally as well as by Federal and -foundation grants. Martinos Center investigators and their broad network of colleagues are at the -forefront of developing advanced imaging technologies, integrating those technologies for multimodality -acquisition, and deriving novel acquisition and analysis methods for the rich body of imaging -data now acquired with these technologies. Funded by a P41 Regional Resource grant, from National -Center for Research Resources, the Martinos Center and its Center for Functional Neuroimaging -Technologies is a region-wide resource, broadly used by basic and clinical scientists who employ the -full range of imaging technologies available at the Center to address questions of fundamental -importance in fields ranging from neurovascular, neurological, and psychiatric disorders to cognitive -neuroscience to cancer and cardiovascular function.

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Stephanie Jones
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Brown University
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Dr. Jones uses her background in dynamical systems theory mathematics and computational neural modeling to study neural dynamics in health and disease. She is trained in MEG/EEG imaging and currently uses computational modeling techniques to bridge the critical gap between the non-invasive imaging observables and the underlying microscopic cellular and network level mechanisms. Her current projects and interest include:

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  • Investigating the neural dynamics underlying normal development in children ages 0-6 as well as neural abnormalities in children with encephalopathy of -prematurity (EP). In collaboration with Drs. Ellen Grant and Yoshio Okada at CHB, we are studying development with a powerful combination of techniques including mathematical modeling, MR diffusion tensor imaging, and MEG imaging.
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  • Studying the mechanisms and functions of neural rhythms including their role in sensory perception, attentional processes, and healthy aging. We are also investigating the source of disruption in brain rhythms in diseases such as Parkinson's Disease, Obsessive Compulsive Disorder, and Attention Deficit Disorder.
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  • Investigating plasticity induced by training in perceptual attention. In collaboration with Dr. Cathy Kerr at HMS we are studying neurodynamics underlying Mindfulness Medidation Practice.
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  • Combing computational modeling and optogenetic techniques, in collaboration with Dr. Chris Moore at Brown University, to study neural dynamics. We are currently delineating the role of specific cell types in controlling neocortical rhythmicity and investigating the impact of these rhythms on sensory perception.
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Earl Miller
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MIT
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Website -

The Miller Lab uses experimental and theoretical approaches to study the neural basis of the high-level cognitive functions that underlie complex goal-directed behavior. The focus is on the frontal lobe, the region of the brain most elaborated in humans and linked to neuropsychiatric disorders. They have provided insights into how categories, concepts, and rules are learned, how attention is focused, and how the brain coordinates thought and action. To this end, the Miller Lab has innovated techniques for studying the activity of many neurons in multiple brain areas simultaneously, which has provided insight into how interactions within local and global networks of neurons interact and collaborate. This work has established a foundation upon which to construct more detailed, mechanistic accounts of how executive control is implemented in the brain and its dysfunction in diseases such as autism, schizophrenia and attention deficit disorder.

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Affiliated Faculty

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Seppo P. Ahlfors
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MGH/Harvard Medical School/Martinos Imaging Center
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Website -

My research concerns spatiotemporal imaging of human brain function. I have applied integrated magnetoencephalography (MEG), electroencephalography (EEG), and functional magnetic resonance imaging (fMRI) to studies of cortical processing of visual information. My research involves development of techniques for the analysis of multimodal biomedical imaging data, including the use of fMRI data to inform the source estimation (inverse problem) of MEG and EEG. Currently I am studying computationally the characteristics of the sensitivity of MEG and EEG sensor arrays as well as the relationship of MEG and EEG signals to the cortical anatomy and physiology. Collaborative work focuses on the application of MEG, EEG, and fMRI techniques to reveal neural activation patterns related to cognitive processing in normal and clinical populations.

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Christoph Börgers
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Tufts University
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Most of my work is on designing and analyzing computational models in neuroscience. Current projects concern the role of different populations of inhibitory cells in gamma oscillations, modeling of the impact of astrocytes on neuronal activity, and synchronization via gap junctions. I also have one current research project unrelated to neuroscience, on numerical methods for linear Boltzmann equations.

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Syd Cash
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MGH
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Website -

Current research in the lab is, broadly speaking, dedicated to trying to understand normal and abnormal brain activity, particularly oscillations, using multi-modal and multi-scalar approaches. Specifically, we are combining novel microelectrode approaches with non-invasive techniques such as electroencephalography and magnetoencephalography to record directly from both human and animal cortex and subcortical structures. One part of the lab studies the neurophysiology of epilepsy; trying to understand how seizures start and stop and how they might be predicted and terminated. These questions overlap with investigations into the mechanisms of sleep, normal language, auditory, and other cognitive processing.

-All of these projects are built on a foundation of combined microelectrode, macroelectrode and non-invasive recording techniques that span information from the level of single action potentials to aggregate activity of millions of neurons. Intensive signal processing and computational techniques are employed to analyze these data sets. Collaborative activities involving neural modeling are aimed at relating these multi-scalar data. Ultimately, all of these projects aim toward the creation of both invasive and non-invasive mechanisms for restoring damaged neuronal function. -

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Ming Cheng
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Brown University
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Dr. Cheng is a neurosurgeon whose laboratory studies the neural basis of different cognitive processes that underlie diseases such as Parkinson's and other movement disorders, pain, epilepsy, depression, and other more rare neurological conditions. The goal of his laboratory is neurorestoration, the idea that therapies can be devised to restore the original function of the brain and spinal cord.

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Our primary techniques include the use of psychopharmacology, electrical stimulation and reversible lesioning to enhance or alter brain, spine and peripheral nerve function. We measure the results in our subjects with behavioral tasks coupled with intraoperative and extraoperative electrophysiology in human subjects: EEG, electrocorticography (ECoG), extracellular field potential recordings, and single unit microelectrode recordings from multiple brain and spine structures (subthalamic nucleus, caudate, globus pallidus, thalamus, nucleus accumbens, substantia nigra, neocortical areas, dorsal columns, the dorsal horn, etc.). We also use these electrophysiological tools to complement molecular techniques in the study of animal models of hydrocephalus and neurodegenerative disease. We are ultimately interested in applying our findings to the creation of open and closed loop stimulatory devices that can help human patients with neurological and psychiatric diseases. Ultimately, the information we learn will help us create brain and spine machine interfaces to fight disease and change human interaction with the external world. -

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Robert Desimone
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MIT
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Neural basis for attention and executive control
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A complex visual scene will typically contain many different objects, few of which are currently relevant to behavior. Thus, attentional mechanisms are needed to select the relevant objects from the scene and to reject the irrelevant ones. Neurophysiological studies in our own and other labs have identified some of the neural mechanisms of attentional selection within the ventral, "object recognition", stream of the cortex. At each stage along this stream, attended, or behaviorally relevant, stimuli are processed preferentially compared to irrelevant distracters. In recent years, we have found that the top-down attentional bias is expressed, at least in part, in visual cortex through an increase in high-frequency (gamma) synchronization of neurons carrying critical information about the location or features of the behaviorally relevant stimulus. Increases in gamma synchrony are found during both spatial attention and featural attention engaged during visual search, and the presence of synchrony predicts faster responses in visual tasks. Recent evidence shows that inputs from the frontal eye fields (FEF) in prefrontal cortex initiates coupled gamma-frequency oscillations between FEF and area V4 during attention, and these oscillations are shifted in time across the two areas to allow for maximally effective communication. Cross-area synchrony may be a general mechanism for regulating information flow through the brain and for regulating spike-timing dependent plasticity. -

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Howard Eichenbaum
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Boston University
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Website -

The research program of this laboratory is focused on four closely related projects that seek to understand the brain circuity that supports memory. This research is guided by the hypothesis that our ability to remember specific experiences relies on an organization of memories about objects and the events in the context in which they occurred. We believe that associations between objects and context is accomplished through the circuitry of the medial temporal lobe, in which parallel pathways represent information about objects and about context, and these streams of information converge within the hippocampus. A project central to this goal seeks to characterize how neurons in key components of the medial temporal lobe encode these different types of information and how components of this brain system interact with one another. Another project explores how the hippocampus is initially critical to the associations between objects and context but eventually these associations consolidate in cortical areas with which the hippocampus is connected. Another project explores how the prefrontal cortex controls the retrieval of memories as they bear on ongoing cognitive processes. And yet another project explores how hippocampal networks represent objects in the spacial and temporal context in which they occur. Together these projects will provide new insights into how memories are organized within the medial temporal lobe memory system and how memories are retrieved when we recall our daily experiences. -

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Oded Ghitza
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Boston University
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Website -
Cortical and Computational Decoding of Speech
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We conduct a tightly integrated computational and experimental research program across three sites (BU, NYU, Columbia) to study spoken language recognition from the psychophysical, neurophysiological, and engineering perspectives. The program proceeds in four fronts:

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  • Psychophysics (Ghitza, BU). We measure and model the results of human performance in tasks designed to gain a better understanding on the interplay between neuronal oscillators in different frequency bands, and between the oscillations and the speech syllabic structure;
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  • Human Neuroimaging. We formulate the intra-relationship among theta, beta and gamma oscillations, using MEG (David Poeppel, NYU) and ECoG (Charles Schroeder, Columbia) data recorded while subjects perform intelligibility tasks;
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  • Monkey Electrophysiology (Charles Schroeder, Columbia). If the emerging cortical computation principles are fundamental, they must generalize across mammalian species. We are using high-resolution physiological methods to measure the intra-relationship among oscillations using multi-electrode recordings in monkeys listening to stimuli specifically designed to capture the rhythmic aspects of natural speech and music;
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  • Automatic Speech Recognition (Ghitza, BU). We explore a new perspective to the development of ASR systems that incorporates the insights from the behavioral and brain sciences, specifically rhythmic brain activity. We ascertain whether the proposed cortical computation principle could be used as an adjunct to conventional features used in ASR systems, e.g. in lattice re-scoring of n-best lists – and ultimately result in a decrease in word error rate.
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Stan Goldin
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Harvard Medical School
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Dr. Stan Goldin has been developing some new ideas on a carrier wave function for brain oscillations in discrete frequency bands. His early stage collaboration with BSF Fellow Prof. Ed Boyden of MIT is exploring new ways to design multichannel arrays for automated, rapid, delivery of pharmacological agents to key distributed locations in nerve networks, to help elucidate network function. His collaboration with Dr. Newton Howard of MIT on neural coding mechanisms has revealed novel photonic signaling pathways in the brain, powered by light-generating neuronal redox reactions and employing novel photon absorbing rhodopsin-like proteins discovered within the mammalian brain.

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Dr. Goldin’s laboratory originated research techniques and devices that have been employed to elucidate molecular mechanisms underlying brain waves and brain oscillation circuitry--processes now known to play an important role in long term brain changes (neuroplasticity) and memory formation. He is now finishing a book, Ascent of the Human Brain—An Expanded view of Human Evolution, on modern neuroscience research’s impact on our understanding of human consciousness, selfhood & spirituality.

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Ann Graybiel
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MIT
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Website -

Ann Graybiel studies the basal ganglia, forebrain structures that are profoundly important for normal brain function but are also implicated in Parkinson's disease, Huntington's disease, obsessive-compulsive disorder, and addiction. Graybiel's work is uncovering neural deficits related to these disorders, as well as the role the basal ganglia play in guiding normal behavior. -

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Xue Han
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Boston University
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Brain disorders represent the biggest unmet medical need, with many disorders being untreatable, and most treatments presenting serious side effects. Accordingly, we are discovering design principles for novel neuromodulation therapies. We invent and apply a variety of genetic, molecular, pharmacological, optical, and electrical tools to correct neural circuits that go awry within the brain. As an example, we have pioneered several technologies for silencing specific cells in the brain using pulses of light. We have also recently participated the first pre-clinical testing of a novel neurotechnology, optical neural modulation. Using these novel neurotechnologies and classical ones such as deep brain stimulation (DBS), we modulate the function of neural circuits to establish causal links between neural dynamics and behavioral phenomena (e.g., movement, attention, memory, and decision making). One of our current interests is the investigation of how neural synchrony arises within and across brain regions, and how synchronous activity contributes to normal cognition and pathology. -

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Mike Hasselmo
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Boston University
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Don Katz
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Brandeis
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Bernat Kocsis
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Harvard Medical School
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Subcortical regulation of forebrain activity in the sleep-wake cycle.
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The central focus of my research is the subcortical regulation of hippocampal function and is guided by the general hypothesis that the role of this regulation is to build dynamic associations between several limbic structures that are synchronized by oscillatory population activity. Phasic and rhythmic synchronization of neuronal activity is critical to control the concerted action of spatially separated structures in the brain. The general state and background activity of various brain structures determine how these structures will respond to different specific inputs and how they establish dynamical connections to perform complex functions. An important constituent of these states is the pattern of population activity including coherent oscillations in anatomically scattered structures which can establish functional networks during specific behaviors. Theta synchrony provides an excellent model to study these cooperations and the way in which they differ in specific behavioral states, such as waking exploration and REM sleep.

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Oscillatory processes in cardiovascular control.
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Another model we use to study rhythmic synchronization among neural networks is the autonomic nervous system which is capable of generating different patterns of activity that control the response of the cardiovascular system to changes in the environment (e.g. chemoregulation, thermoregulation, etc.) and different behavioral states (e.g. defense reaction, eating, sleep, etc.). Our guiding hypothesis in this research is that sympathetic rhythm is generated by multiple oscillators and we study the changes in the relationship between these oscillators under different conditions of health and disease. -

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Mark Kramer
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Boston University
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We study mathematical neuroscience, with particular emphasis on neural rhythms, brain diseases, dynamical systems, and data analysis. All of the research involves interdisciplinary collaborations with experimentalists and clinicians. We are currently focused on analysis and modeling of multiscale data recorded in vivo from human subjects, and the construction of computational models of multiscale neuronal activity. We are also interested in techniques to infer and analyze functional connectivity networks from multivariate time series data, and how neuroscience can motivate new research questions in mathematics. -

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Chris Moore
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Brown University
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Christopher Moore studies brain dynamics and how they change can change perception from moment to moment. -The brain's ability to shift the way it processes information—to shift its 'state'—is crucial to surviving in an ever-changing world. Dysregulation of these dynamics are a hallmark of neurologic and psychiatric disease. The laboratory is studying the mechanisms responsible for generating brain states, how they impact the representation of a sensory input, and how, ultimately, they change conscious perception. -

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Daniel Polley
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Mass. Eye & Ear/Harvard Medical School
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Our work focuses on the role of sensory experience in the development and -maintenance of functional circuits in the auditory cortex. The auditory -cortex is powerfully influenced by experience during finite windows of -development known as critical periods, after which time significant changes -can only be brought about through learned associations between sounds and -behaviorally relevant consequences. We study the mechanisms and perceptual -correlates of cortical plasticity across the lifespan using a variety of -neurophysiological, genetic, behavioral and computational approaches. We -also record from subcortical auditory nuclei such as the inferior colliculus -and auditory thalamus to understand more about features that are relayed to -the cortex versus constructed there de novo. We believe this class of study -will contribute towards a richer understanding of normal function, but might -also hold the key for remediating abnormal auditory signal following a -history of degraded hearing or deafness in early life. A major goal for our -group is to apply what we've learned about the dynamic interplay between -plasticity and stability in animal models towards improving auditory -processing in humans that have been reconnected to the auditory world -following a period of prolonged hearing loss. -

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Jason Ritt
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Boston University
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The Ritt lab concentrates on how organisms gather and use information from their environment, through processes of active sensing and sensory decision making. Current projects employ electrophysiological, behavioral, optogenetic and theoretical methods applied to the rodent whisker system, a highly refined tactile sensory system. Experiments combine multi-electrode recording of brain activity; high speed videography of behavior and development of automated image analysis algorithms; and optical stimulation of specific cell types (e.g., excitatory vs. inhibitory neurons) using genetically targeted expression of light sensitive ion channels. Parallel modeling uses tools from dynamical systems, control theory and decision theory. Augmenting experiments with model-driven, real-time feedback forms a basis for development of brain machine interfaces, with an emphasis on sensory neural prosthetics, in addition to providing state of the art tools to address basic questions of neural function. -

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Robert Sekuler
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Brandeis University
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Sekuler lab members are currently studying

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Kamal Sen
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Boston University
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How do neurons in the brain encode complex natural sounds? What are the neural substrates of selectivity for and discrimination of different categories of natural sounds? Are these substrates innate or shaped by learning? -Our laboratory investigates these questions in the model system of the songbird. Electrophysiological techniques are used to record neural responses from hierarchical stages of auditory processing. Theoretical methods from areas such as statistical signal processing, systems theory, probability theory, information theory and pattern recognition are applied to characterize how neurons in the brain encode natural sounds. Computational models are constructed to understand the processing of natural sounds both at the single neuron and the network level, to model neural selectivity and discrimination, and to explore the role of learning in shaping the neural code. -

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Barbara Shinn-Cunningham
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Boston University
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Research in the Auditory Neuroscience Laboratory addresses how listeners communicate and make sense of sounds in everyday settings. We study everything from basic perceptual sensitivity to the ways in which different brain regions coordinate their activity during complex tasks. We use a range of approaches to explore these issues, including human behavioral experiments, human neuroelectric imaging, computational modeling, and, in collaboration with other laboratories, fMRI, animal behavioral experiments, and animal neurophysiology. -

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Kevin Spencer
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Harvard Medical School
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Steven Stufflebeam
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MGH/Harvard Medical School/Martinos Imaging Center
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Dr. Stufflebeam's goal is to develop and translate advanced technology at the Martinos Center into clinical practice. Currently, he is using MEG/EEG, fMRI, and optical imaging to understand how the brain processes neural information. He applies multiple imaging technologies to understand epilepsy, schizophrenia, and brain neoplasms. He is also setting up a clinical MEG service for New England. -

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Lucia Vaina
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Boston University
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Miles Whittington
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Newcastle Universtity, UK
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Dr. Whittington's group has a major interest in mechanisms that generate oscillatory activity with neural networks, how this activity is sustained and how is modulated in various normal and pathological conditions. -

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Matt Wilson
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MIT
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Research in the Wilson laboratory focuses on the study of information representation across large populations of neurons in the mammalian nervous system, as well as on the mechanisms that underlie formation and maintenance of distributed memories in freely behaving animals. To study the basis of these processes, the lab employs a combination of molecular genetic, electrophysiological, pharmacological, behavioral, and computational approaches. Using techniques that allow the simultaneous activity of ensembles of hundreds of single neurons to be examined in freely behaving animals, the lab examines how memories of places and events are encoded across networks of cells within the hippocampus ¬ a region of the brain long implicated in the processes underlying learning and memory. -

-These studies of learning and memory in awake, behaving animals have led to the exploration of the nature of sleep and its role in memory. Previous theories have suggested that sleep states may be involved in the process of memory consolidation, in which memories are transferred from short to longer-term stores and possibly reorganized into more efficient forms. Recent evidence has shown that ensembles of neurons within the hippocampus, which had been activated during behavior are reactivated during periods of dreaming. By reconstructing the content of these states, specific memories can be tracked during the course of the consolidation process. -

-Combining the measurement of ongoing neuronal activity with manipulation of molecular genetic targets has allowed the study of how specific cellular mechanisms regulate neural function to produce learning and memory at the behavioral level. Pharmacological blockage of these receptors has allowed the study of their involvement in the rapid changes that occur during both waking and sleeping states. Simultaneous monitoring of areas in the hippocampus and neocortex have allowed study of the downstream effects of activation. -

-Taken together, these approaches contribute to the overall research objective: to understand the link from cellular/subcellular mechanisms of plasticity, to neural ensemble representations and interactions, to learning, memory, behavior, and cognition. -

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- Natalie Adams -
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- My interests lie broadly in understanding how the brain represents perceptual information and why - this may differ in pathological states related to autism and schizophrenia. I am tackling this using - in vitro electrophysiological techniques in collaboration with mathematical modellers to look at - mechanisms of network rhythm generation, modulation and interaction in the anterior cingulate - region of prefrontal cortex. -

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- Currently, my research is focussed on exploring aspects of frontal cortical function that facilitate - learning of sequences of sensory events (e.g. Siegel, M., et al 2009). I am interested in how precise - spike timing in individual neurons or small sub-populations relates to the local field oscillation as a - marker of the overall average of event timing relevant to a given stimulus. -

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- So far my work has revealed that many neurons in the anterior cingulate cortex possess the ability - to intrinsically oscillate at sub-threshold levels. With varying degrees of tonic excitation these sub- - threshold oscillations (STOs) exist at a variety of frequencies up to c.30Hz. The anterior cingulate - cortex is known to have multiple mechanisms for the generation of gamma rhythms associated with - cognitive function and I will look at how these rhythms interact with cellular STOs to affect spike/ - phase relationships and perhaps code for sequences: The working hypothesis is that assemblies - of cells receiving higher levels of excitation increase the drive to the kinetics responsible for STOs, - ultimately leading to a spike phase-advance on each period of field gamma. -

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- This work could uncover a substrate for the stable, computationally useful, temporal separation of - concurrently active sensory representations. -

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- My work has been focused on two aspects of the analysis of neural rhythms. The first project, which I have been working on with Uri Eden, Mark Kramer, and Kyle Lepage, has focused on the spectral analysis of spike trains including coherence between signals when at least one of those signals is a spike train. I use point process theory to derive properties of point process spectra and the estimators, and try to understand how those properties may help or hinder our understanding of the underlying neural system. The second project is in collaboration with Timothy Gardner and Uri Eden in which we are investigating methods of multi-scale time-frequency analysis based on an object-based signal representation. This method will allow us to extract signal information using multiple times scales simultaneously. This method may help to construct sharper spectral representations than are currently possible and we believe this operation may help to understand the phenomenology of human auditory perception. -

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- Recording of neuronal spiking activity in distributed brain circuits - requires a scalable design for massively parallel recording of - extracellular field potentials. We are inventing such a system and - implementing a proof-of-concept instantiation. In this system, - multi-electrode arrays are used, which minimize tissue damage and help - with spike sorting, and time domain multiplexing of analog field - potential acquisition reduces interconnect. Channel data is then - relayed to a custom-designed terabyte capacity storage network via - custom digital circuitry. The storage network is designed to enable - neural data to be analyzed in flexible ways, including the evaluation - of spike sorting methods. -

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- On the technical side, I am solely responsible for the design and implementation of the ethernet network and high-speed data storage software. In addition, I provide leadership to the project by staying well-versed in all aspects of the system design and maintaining open lines of communication between all technology developers, as well as organizing and documenting the design of the system. -

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- Jung has been working with Kopell and Whittington on several modeling projects. The main one concerns the effects of top-down beta rhythms on attention; Jung showed that such signals resonate with cells in the deep cortical layers, producing gain control and more gamma rhythms in the superficial layers; a paper is almost complete. This work is highly relevant to work done by Miller on top-down attention, and further collaborations are planned. The work also has relevance to aspects of schizophrenia, and conversations are beginning with the group of Kevin Spencer. A second project concerns multiple inhibitory cell types in the rat auditory cortex. See Schizophrenia. -

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- Kyle has been one of the most active members of the data analyis group. In the past year, he has been involved in CRC related activity involving three main subjects and two more tertiary ones. One primary project was a collaboration with the Kramer, Eden and Desimone groups on spike-field association (statistical procedures used to infer relations between a rhythm in a time series, such as a local field potential recording, and the firing activity of single neuron. Mikio Aoi is also involved. There is now a preprint. A second major project is a collaboration with the Eichenbaum and Eden groups on cells that measure time. More technically, the project deals with the development of statistical procedures to separate the relative influence of covariates of interest such as time and rodent position upon neural activity. There are two papers and several popular press articles about this work. The third major project is a collaboration with the Kramer lab, also involving postdoc ShiNung Ching; it is motivated by techniques used in MEG and EEG experiments to find functionally connected networks, as in the Human Connectome. This work deals with principled estimation of the statistical connectivity between nodes in an evoked network. In this paradigm a stimulus is repeatedly applied to network nodes, one at a time, and evoked activity at nodes is used to infer a statistical relation between node activity. There is a preprint. A smaller project with the group of Shinn-Cunningham concerns MEG eigensource. In this work local bias in MEG source estimates is traded for decreased non-spatially local bias due to unavoidable inverse-problem source localization limitation. A final project, with Kramer, deals with removing bias in EEG measurements due to activity present on either an EEG reference electrode or present in a "re-referencing method" -

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- Morteza is a postdoc in the lab of Miller. He is interested in the functional circuitry for memory and context formation between and within the prefrontal cortex (PFC) and the medial temporal lobe (MTL). PFC neurons reflect the associative relations between stimuli, task instructions, behavioral responses, rewards, etc. Interestingly, MTL neurons show similar properties. Neurophysiological studies have been focused on either the MTL or PFC and the interaction between the MTL and PFC is still unclear. He will simultaneously record - with many electrodes- from the PFC and MTL areas while monkey perform a task that temporally separates neuronal information related to context, sample, and recall of the correct choice. He will investigate the modulation of oscillatory neuronal dynamics between and within the PFC and MTL, during different stage of the task. The second project of Morteza concerns the oscillatory neuronal dynamics of categorization in the PFC. Modulations of neuronal oscillations in the PFC with cognitive demands may regulate whether PFC neurons function as multitaskers. The data from these projects are essential to understanding central questions about the roles of rhythms in cognition, and will provide the basis for modeling efforts. In addition, Morteza helps to run the physiology working group. -

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- Lara is a new postdoc in the lab of Eichebaum (BU). Her work was described above. She interacts frequently with members of the statistics and modeling groups (Eden, Kramer, Kopell). She also talks frequently with members of other CRC labs (Boyden lab, MIT : Annabelle Singer; Wilson lab, MIT : Greg Hale, Sage Chen, and Stuart Layton) to compare data, methodology, and analysis techniques. She is planning further interactions with Omar Ahmed of the Cash lab (MGH) on hippocampal oscillatory activity in humans, the Miller lab (MIT) on beta frequency oscillations, and the Jiamin Zhuo and Nick James of the Han lab (BU), on dentate gyrus function and beta rhythms, respectively. She has also interacted with Whittington at CRC events. She speaks frequently with postdocs Annabelle Singer, Justin Kinney, Omar Ahmed, and Kyle Lepage as well as graduate students Caroline Moore-Kochlacs, Greg Hale, and all the members of the Eichenbaum lab. Lara has already written a grant proposal based on this work: NIH F-32 Individual Postdoctoral Research Grant -

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- Wei studies large-scale networks in the resting human brain with data - from non-invasive imaging techniques. Under the hypothesis that - particular sets of brain regions interact with each other to maintain - an active yet stable intrinsic state, the goal of her work is to - uncover both the structure and dynamics of such intrinsic networks, in - the hope that knowledge of the resting state will lead to further - understanding of how neural electrophysiology gives rise to cognitive - phenomena. -

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- Her current project involves collaborations between several - laboratories. With MEG data acquired by Stufflebeam’s group, Wei and - Steve are looking at seed-based Granger-causality maps, assessing - their spatiotemporal and spectral properties, to explore their - relationship with the proposed default-mode hypothesis. They will - later extend the analysis to task data from the same subjects and see - how the networks change undergoing different cognitive processes. - Meanwhile, supervised by Matti Hamalainen and Uri Eden, Wei interacts - with Patrick Purdon’s group at the Martinos Center, developing a - state-space model based approach to identify the full - source-connectivity matrix of the MEG signal and monitor its change - over time. Efforts are being made to advance the methodology dealing - with high-dimensionality of the data and make the full-network - tractable. The third collaboration is with Mark Kramer, aiming at - finding plausible biophysical models that can explain the observed - network properties. This is an open area of exploration and may serve - further modeling studies on brain disease such as epilepsy. Results - from these collaborations together may provide a comprehensive picture - of how the brain works at different levels. -

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- Sujith was a former student of Wilson, now working mostly with Cash and Kopell on aspects of the alpha rhythm and sleep. Details are above. He is the organizer of the Alpha Working Group. -

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Postdoctoral Fellowship at the Martinos Center for Biomedical Imaging and the Psychiatric Neuroimaging Division of the Psychiatry Department at Massachusetts General Hospital, Charlestown, MA

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    Our team is developing several technical innovations that will significantly reduce the impact of head motion on fMRI and diffusion data. We are seeking a candidate to work with us to improve data analysis by making fMRI and diffusion analysis motion-aware using both the in-image data and the motion tracking data.

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    Our ideal candidate has a PhD in Computer Science, Electrical Engineering, or related fields, and has experience in signal processing and numerical methods. Candidates with experience in image processing and time series analysis, or specifically in fMRI and diffusion data analysis will be preferred. This position requires strong programming skills, and the candidate is expected to have experience working in C++, scripting languages (e.g., Python), and rapid-prototyping languages for numerical algorithms (e.g., Matlab, Mathematica). Strong communication skills are essential, as the position involves working with an interdisciplinary team of scientists in both MRI physics/engineering and psychology/neuroscience in addition to research coordinators and MRI technologists. Background in cognitive neuroscience and an interest in clinical applications are advantageous. Training in clinical research will be provided.

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    Position available immediately. Please send -

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Postdoctoral Fellowship at the Martinos Center for Biomedical Imaging and the Psychiatric Neuroimaging Division of the Psychiatry Department at Massachusetts General Hospital, Charlestown, MA

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"Wired" project

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Boyden, Kopell, Wilson, Desimone, Eichenbaum, Graybiel, Miller, Eden, Kramer, Brown

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Optogenetic perturbation of brain rhythms

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Boyden, Han, Kopell, Desimone, Brown, Borgers, Kramer, Ritt, Eden

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Neural Data Analysis

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Desimone, Kramer, Eden, Eichenbaum, Whittington, Ritt, Cheng, Sarma, Kopell, Purdon, Cash, Stufflbeam, Hamalainen, Vaina

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Origins and propagation of rhythms

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Moore, Jones, Kopell, Miller, Borgers, Boyden

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Rhythms and cognition

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Kopell, Jones, Moore, Whittington, Eichenbaum, Han, Miller, Wilson, Brown, Graybiel, Polley

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Dynamics of anesthesia and sleep

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Brown, Kopell, Purdon, Wilson, Cash

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Parkinson's Disease

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Han, Boyden, Kopell, Eden, Cheng, Jones, Ritt

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Schizophrenia

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Kopell, Le Beau, Whittington, Kocsis

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Epilepsy

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Kramer, Cash, Moore, Eden, Jones, Stufflebeam, Hamalainen, Kopell, Borgers

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Pediatric abnormalities

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Jones, Hamalainen, Eden, Kramer

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