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Added tests, refactored code and changed function signatures
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@raj1701 @ntolley
raj1701 authored and ntolley committed Jan 12, 2024
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1 change: 0 additions & 1 deletion doc/conf.py
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Expand Up @@ -117,7 +117,6 @@
("Examples", "auto_examples/index"),
("GUI", "gui/index"),
("API", "api"),
("Network File", "network_file_desc"),
("Glossary", "glossary"),
("What's new", "whats_new"),
("GitHub", "https://github.com/jonescompneurolab/hnn-core", True)
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320 changes: 162 additions & 158 deletions doc/network_file_desc.rst
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Expand Up @@ -8,189 +8,193 @@ Network File Content

File Content Description
------------------------

object_type : str
Type of object (Network) saved.
N_pyr_x : int
Nr of cells (x).
N_pyr_y : int
Nr of cells (y).
threshold : float
Firing threshold of all cells.
celsius : float
Temperature in degree celsius.
cell_types : dict of Cell Object
key : name of cell type.
value : Cell object.
gid_ranges : dict of dict
key : cell name or drive name.
value : dict.
pos_dict : dict
key : cell type name.
value : All co-ordintes of the cell types.
cell_response : Instance of Cell Response Object
The Cell Response object.
external_drives : dict of dict
key : external drive name.
value : dict.
external_biases : dict of dict
key : external bias name.
value : dict.
connectivity : list of dict
Contains connection info between cells and
cells and external drives.
rec_arrays : dict of Extracellular Arrays
key : extracellular array name.
value : Instance of Extracellular Array object.
delay : float
Synaptic delay in ms.
hdf5 is the file format used for storing the Network object. The network is stored in a layered format. The first layer consists of the network attributes.
The attributes of the network are then broken down until the network can be representated as a collection of key value pairs. For example - cell_types is a network
attribute therefore in the first layer. The description of each cell type is in layer 2. Each cell has various sections. The description of a section is in layer 3.
Below are the attributes of the network.

object_type : str
Type of object (Network) saved.
N_pyr_x : int
Nr of cells (x).
N_pyr_y : int
Nr of cells (y).
threshold : float
Firing threshold of all cells.
celsius : float
Temperature in degree celsius.
cell_types : dict of Cell Object
key : name of cell type.
value : Cell object.
gid_ranges : dict of dict
key : cell name or drive name.
value : dict.
pos_dict : dict
key : cell type name.
value : All co-ordintes of the cell types.
cell_response : Instance of Cell Response Object
The Cell Response object.
external_drives : dict of dict
key : external drive name.
value : dict.
external_biases : dict of dict
key : external bias name.
value : dict.
connectivity : list of dict
Contains connection info between cells and
cells and external drives.
rec_arrays : dict of Extracellular Arrays
key : extracellular array name.
value : Instance of Extracellular Array object.
delay : float
Synaptic delay in ms.

Cell Type Description
---------------------

cell_name : str
Name of the cell.
pos : tuple
The (x, y, z) coordinates.
sections : dict of Section
Dictionary with keys as section name.
synapses : dict of dict
Keys are name of synaptic mechanism. Each synaptic mechanism
has keys for parameters of the mechanism, e.g., 'e', 'tau1',
'tau2'.
cell_tree : dict of list
Stores the tree representation of a cell.
sect_loc : dict of list
Can have keys 'proximal' or 'distal' each containing
names of section locations that are proximal or distal.
gid : int
GID of the cell in a network (or None if not yet assigned).
dipole_pp : list of h.Dipole()
The Dipole objects (see dipole.mod).
vsec : dict
Recording of section specific voltage. Must be enabled
by running simulate_dipole(net, record_vsec=True) or
simulate_dipole(net, record_vsoma=True).
isec : dict
Contains recording of section specific currents indexed
by synapse type (keys can be soma_gabaa, soma_gabab etc.).
Must be enabled by running simulate_dipole(net, record_isec=True)
or simulate_dipole(net, record_isoma=True).
tonic_biases : list of h.IClamp
The current clamps inserted at each section of the cell
for tonic biasing inputs.
cell_name : str
Name of the cell.
pos : tuple
The (x, y, z) coordinates.
sections : dict of Section
Dictionary with keys as section name.
synapses : dict of dict
Keys are name of synaptic mechanism. Each synaptic mechanism
has keys for parameters of the mechanism, e.g., 'e', 'tau1',
'tau2'.
cell_tree : dict of list
Stores the tree representation of a cell.
sect_loc : dict of list
Can have keys 'proximal' or 'distal' each containing
names of section locations that are proximal or distal.
gid : int
GID of the cell in a network (or None if not yet assigned).
dipole_pp : list of h.Dipole()
The Dipole objects (see dipole.mod).
vsec : dict
Recording of section specific voltage. Must be enabled
by running simulate_dipole(net, record_vsec=True) or
simulate_dipole(net, record_vsoma=True).
isec : dict
Contains recording of section specific currents indexed
by synapse type (keys can be soma_gabaa, soma_gabab etc.).
Must be enabled by running simulate_dipole(net, record_isec=True)
or simulate_dipole(net, record_isoma=True).
tonic_biases : list of h.IClamp
The current clamps inserted at each section of the cell
for tonic biasing inputs.

Section Description
-------------------

L : float
length of a section in microns.
diam : float
diameter of a section in microns.
cm : float
membrane capacitance in micro-Farads.
Ra : float
axial resistivity in ohm-cm.
end_pts : list of [x, y, z]
The start and stop points of the section.
syns : list of str
The synaptic mechanisms to add in this section.
mechs : dict
Mechanisms to insert in this section. The keys
are the names of the mechanisms and values
are the properties.
L : float
length of a section in microns.
diam : float
diameter of a section in microns.
cm : float
membrane capacitance in micro-Farads.
Ra : float
axial resistivity in ohm-cm.
end_pts : list of [x, y, z]
The start and stop points of the section.
syns : list of str
The synaptic mechanisms to add in this section.
mechs : dict
Mechanisms to insert in this section. The keys
are the names of the mechanisms and values
are the properties.

Gid Range Description
---------------------

start : int
Start of the gid_range.
stop : int
End of the gid_range.
start : int
Start of the gid_range.
stop : int
End of the gid_range.

External Drive Description
--------------------------

name : str
Unique name for the drive.
dynamics : dict
Parameters describing how the temporal dynamics of spike trains in the
drive. The keys are specific to the type of drive ('evoked', 'bursty',
etc.).
location : str
Target location of synapses.
cell_specific : bool
Whether each artifical drive cell has 1-to-1 (True, default) or
all-to-all (False) connection parameters.
weights_ampa : dict or None
Synaptic weights (in uS) of AMPA receptors on each targeted cell
type (dict keys).
weights_nmda : dict or None
Synaptic weights (in uS) of NMDA receptors on each targeted cell
type (dict keys).
probability : dict or float
Probability of connection between any src-target pair.
synaptic_delays : dict or float
Synaptic delay (in ms) at the column origin, dispersed laterally as
a function of the space_constant. If float, applies to all target
cell types.
event_seed : int
Optional initial seed for random number generator.
conn_seed : int
Optional initial seed for random number generator.
n_drive_cells : int | 'n_cells'
The number of drive cells that each contribute an independently
sampled synaptic spike to the network according to the Gaussian
time distribution (mu, sigma).
events : list
Contains the spike times of exogeneous inputs.
name : str
Unique name for the drive.
dynamics : dict
Parameters describing how the temporal dynamics of spike trains in the
drive. The keys are specific to the type of drive ('evoked', 'bursty',
etc.).
location : str
Target location of synapses.
cell_specific : bool
Whether each artifical drive cell has 1-to-1 (True, default) or
all-to-all (False) connection parameters.
weights_ampa : dict or None
Synaptic weights (in uS) of AMPA receptors on each targeted cell
type (dict keys).
weights_nmda : dict or None
Synaptic weights (in uS) of NMDA receptors on each targeted cell
type (dict keys).
probability : dict or float
Probability of connection between any src-target pair.
synaptic_delays : dict or float
Synaptic delay (in ms) at the column origin, dispersed laterally as
a function of the space_constant. If float, applies to all target
cell types.
event_seed : int
Optional initial seed for random number generator.
conn_seed : int
Optional initial seed for random number generator.
n_drive_cells : int | 'n_cells'
The number of drive cells that each contribute an independently
sampled synaptic spike to the network according to the Gaussian
time distribution (mu, sigma).
events : list
Contains the spike times of exogeneous inputs.

External Bias Description
-------------------------

cell_type : str
The cell type whose cells will get the tonic input.
amplitude : float
The amplitude of the input.
t0 : float
The start time of tonic input (in ms).
tstop : float
The end time of tonic input (in ms).
cell_type : str
The cell type whose cells will get the tonic input.
amplitude : float
The amplitude of the input.
t0 : float
The start time of tonic input (in ms).
tstop : float
The end time of tonic input (in ms).

Connection Description
----------------------

target_types : str
Cell type of target gids.
target_gids : list of int
Identifer for targets of source cells.
num_targets : int
Number of unique target gids.
src_type : str
Cell type of source gids.
src_gids : list of int
Identifier for source cells.
num_srcs : int
Number of unique source gids.
gid_pairs : dict
dict indexed by src gids.
loc : str
Target location of synapses.
receptor : str
Synaptic receptor of connection.
nc_dict : dict
Contains information about delay, weight, lamtha etc.
allow_autapses : bool
If True, allow connecting neuron to itself.
probability : float
Probability of connection between any src-target pair.
target_types : str
Cell type of target gids.
target_gids : list of int
Identifer for targets of source cells.
num_targets : int
Number of unique target gids.
src_type : str
Cell type of source gids.
src_gids : list of int
Identifier for source cells.
num_srcs : int
Number of unique source gids.
gid_pairs : dict
dict indexed by src gids.
loc : str
Target location of synapses.
receptor : str
Synaptic receptor of connection.
nc_dict : dict
Contains information about delay, weight, lamtha etc.
allow_autapses : bool
If True, allow connecting neuron to itself.
probability : float
Probability of connection between any src-target pair.

Extracellular Array Description
-------------------------------

positions : tuple | list of tuple
The (x, y, z) coordinates (in um) of the extracellular electrodes.
conductivity : float
Extracellular conductivity, in S/m.
method : str
Approximation to use.
positions : tuple | list of tuple
The (x, y, z) coordinates (in um) of the extracellular electrodes.
conductivity : float
Extracellular conductivity, in S/m.
method : str
Approximation to use.
12 changes: 12 additions & 0 deletions hnn_core/cell.py
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Expand Up @@ -254,6 +254,12 @@ def __eq__(self, other):
return True

def to_dict(self):
"""Converts an object of Section class to a dictionary.
Returns
-------
dictionary form of an object of Section class.
"""
section_data = dict()
section_data['L'] = self.L
section_data['diam'] = self.diam
Expand Down Expand Up @@ -426,6 +432,12 @@ def __eq__(self, other):
return True

def to_dict(self):
"""Converts an object of Cell class to a dictionary.
Returns
-------
dictionary form of an object of Cell class.
"""
cell_data = dict()
cell_data['name'] = self.name
cell_data['pos'] = self.pos
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