Several updates

jlubo · Mar 3, 2021 · 147af4d · 147af4d
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diff --git a/README.md b/README.md
@@ -12,7 +12,8 @@ Furthermore, the package contains Python scripts to analyze the data produced by
 
 If you make use of the code or the binaries, please cite our paper:
 
-	Luboeinski, J. and Tetzlaff, C. (2020). Memory consolidation and improvement by synaptic tagging and capture in recurrent neural networks. bioRxiv preprint.
+	Luboeinski, J., Tetzlaff, C. Memory consolidation and improvement by synaptic tagging and capture in recurrent neural networks. Commun. Biol. 4, 275 (2021). 
+	https://doi.org/10.1038/s42003-021-01778-y
 
 The paper presents the model that underlies the simulation code provided here, as well as findings derived from the model.
 However, the code contains additional features that have not been used in publications yet. Please feel free
@@ -54,7 +55,7 @@ The directory 'simulation-bin/' contains the following sample shell scripts:
 * 'run3' - learn a memory representation, save the network state, and recall after 10 seconds; load the network state, apply intermediate stimulation, let the memory representation consolidate, and recall after 8 hours
 * 'run_2N1S' - reproduce single-synapse data resulting from basic induction protocols for synaptic plasticity
 
-The file 'connections.txt' contains the default connectivity matrix used in Luboeinski and Tetzlaff, bioRxiv, 2020. If this file is absent, the simulation program will automatically generate a new network structure.
+The file 'connections.txt' contains the default connectivity matrix used in Luboeinski and Tetzlaff, Commun. Biol., 2020. If this file is absent, the simulation program will automatically generate a new network structure.
 
 
 ## Analysis scripts

diff --git a/analysis/adjacencyFunctions.py b/analysis/adjacencyFunctions.py
@@ -16,6 +16,7 @@
 core_norecall = core[np.logical_not(np.in1d(core, core_recall))]
 
 Nl = 40 # number of excitatory neurons in one line (Nl^2 is the total number of excitatory neurons)
+h_0 = 0.420075 # nC, initial synaptic weight and normalization factor for z
 
 all = np.arange(Nl**2)
 noncore = all[np.logical_not(np.in1d(all, core))]
@@ -64,7 +65,7 @@ def loadWeightMatrix(filename):
             if i < Nl and j < Nl:
                 v[i][j] = float(value0[j])
             h[i][j] = float(value1[j])
-            z[i][j] = float(value2[j])
+            z[i][j] = h_0*float(value2[j])
 
     f.close()
     adj = (h > epsilon)
@@ -80,6 +81,7 @@ def loadAdjacencyMatrix(filename):
 # getConnectivity
 # Computes and prints the connectivity within the adjacency matrix
 # pr [optional]: specifies if result is printed
+# return: the connectivity as a value between 0 and 1
 def getConnectivity(pr = True):
 	nconn = np.sum(adj > 0)
 	line = adj.shape[0]
@@ -91,6 +93,7 @@ def getConnectivity(pr = True):
 # getConnectivityInCore
 # Computes and prints the connectivity within the core
 # pr [optional]: specifies if result is printed
+# return: the connectivity as a value between 0 and 1
 def getConnectivityInCore(pr = True):
 	connections_within_core = 0
 	N_core = len(core)
@@ -110,6 +113,7 @@ def getConnectivityInCore(pr = True):
 # i: neuron index
 # j: neuron index
 # pr [optional]: specifies if result is printed
+# return: true or false
 def areConnected(i, j, pr = True):
 	global adj
 
@@ -126,6 +130,7 @@ def areConnected(i, j, pr = True):
 # Prints and returns all the neurons from which neuron i receives inputs
 # i: neuron index
 # pr [optional]: specifies if result is printed
+# return: array of neuron numbers
 def incomingConnections(i, pr = True):
 	global adj
 	inc = np.where(adj[:, i] == 1)[0]
@@ -138,6 +143,7 @@ def incomingConnections(i, pr = True):
 # Prints and returns all the neurons to which neuron i provides input
 # i: neuron index
 # pr [optional]: specifies if result is printed
+# return: array of neuron numbers
 def outgoingConnections(i, pr = True):
 	global adj
 	out = np.where(adj[i, :] == 1)[0]
@@ -150,6 +156,7 @@ def outgoingConnections(i, pr = True):
 # Prints and returns all the early-phase synaptic weights incoming to neuron i
 # i: neuron index
 # pr [optional]: specifies if result is printed
+# return: array of early-phase weights in units of nC
 def incomingEarlyPhaseWeights(i, pr = True):
 	global adj
 	global h
@@ -165,6 +172,7 @@ def incomingEarlyPhaseWeights(i, pr = True):
 # Prints and returns all the early-phase synaptic weights outgoing from neuron i
 # i: neuron index
 # pr [optional]: specifies if result is printed
+# return: array of early-phase weights in units of nC
 def outgoingEarlyPhaseWeights(i, pr = True):
 	global adj
 	global h
@@ -180,6 +188,7 @@ def outgoingEarlyPhaseWeights(i, pr = True):
 # Prints and returns all the core neurons from which neuron i receives inputs
 # i: neuron index
 # pr [optional]: specifies if result is printed
+# return: array of neuron numbers
 def connectionsFromCore(i, pr = True):
 	global adj
 	cfc = core[np.in1d(core, incomingConnections(i, False))].flatten()
@@ -192,6 +201,7 @@ def connectionsFromCore(i, pr = True):
 # Prints and returns all the recall-stimulated core neurons from which neuron i receives inputs
 # i: neuron index
 # pr [optional]: specifies if result is printed
+# return: array of neuron numbers
 def connectionsFromRSCore(i, pr = True):
 	global adj
 	cfrsc = core_recall[np.in1d(core_recall, incomingConnections(i, False))].flatten()
@@ -204,6 +214,7 @@ def connectionsFromRSCore(i, pr = True):
 # Prints and returns all the early-phase synaptic weights incoming to neuron i from a given set of neurons
 # i: neuron index
 # set: the set of presynaptic neurons
+# return: array of early-phase weights in units of nC
 def earlyPhaseWeightsFromSet(i, set):
 	global adj
 	global h
@@ -217,6 +228,7 @@ def earlyPhaseWeightsFromSet(i, set):
 # Prints and returns all the early-phase synaptic weights incoming to neuron i from core neurons
 # i: neuron index
 # pr [optional]: specifies if result is printed
+# return: array of early-phase weights in units of nC
 def earlyPhaseWeightsFromCore(i, pr = True):
 	inc_h = earlyPhaseWeightsFromSet(i, core)
 	if pr:
@@ -228,6 +240,7 @@ def earlyPhaseWeightsFromCore(i, pr = True):
 # Prints and returns all the early-phase synaptic weights incoming to neuron i from recall-stimulated core neurons
 # i: neuron index
 # pr [optional]: specifies if result is printed
+# return: array of early-phase weights in units of nC
 def earlyPhaseWeightsFromRSCore(i, pr = True):
 	inc_h = earlyPhaseWeightsFromSet(i, core_recall)
 	if pr:
@@ -239,6 +252,7 @@ def earlyPhaseWeightsFromRSCore(i, pr = True):
 # Prints and returns all the early-phase synaptic weights outgoing to core neurons from neuron i
 # i: neuron index
 # pr [optional]: specifies if result is printed
+# return: array of early-phase weights in units of nC
 def earlyPhaseWeightsToCore(i, pr = True):
 	global adj
 	global h
@@ -254,6 +268,7 @@ def earlyPhaseWeightsToCore(i, pr = True):
 # Prints and returns all the late-phase synaptic weights incoming to neuron i from a given set of neurons
 # i: neuron index
 # set: the set of presynaptic neurons
+# return: array of late-phase weights in units of nC
 def latePhaseWeightsFromSet(i, set):
 	global adj
 	global z
@@ -267,6 +282,7 @@ def latePhaseWeightsFromSet(i, set):
 # Prints and returns all the late-phase synaptic weights incoming to neuron i from core neurons
 # i: neuron index
 # pr [optional]: specifies if result is printed
+# return: array of late-phase weights in units of nC
 def latePhaseWeightsFromCore(i, pr = True):
 	inc_z = latePhaseWeightsFromSet(i, core)
 	if pr:
@@ -278,6 +294,7 @@ def latePhaseWeightsFromCore(i, pr = True):
 # Prints and returns all the late-phase synaptic weights outgoing to core neurons from neuron i
 # i: neuron index
 # pr [optional]: specifies if result is printed
+# return: array of late-phase weights in units of nC
 def latePhaseWeightsToCore(i, pr = True):
 	global adj
 	global z
@@ -295,6 +312,7 @@ def latePhaseWeightsToCore(i, pr = True):
 # set: the first set of neurons (presynaptic)
 # set2 [optional]: the seconds set of neurons (postsynaptic); if not specified, connections within "set" are considered
 # pr [optional]: specifies if result shall be printed
+# return: early-phase weight in units of nC
 def meanEarlyPhaseWeight(set, set2 = [], pr = True):
 	summed_weight = 0
 	connection_num = 0
@@ -326,6 +344,7 @@ def meanEarlyPhaseWeight(set, set2 = [], pr = True):
 # set: the first set of neurons (presynaptic)
 # set2 [optional]: the seconds set of neurons (postsynaptic); if not specified, connections within "set" are considered
 # pr [optional]: specifies if result shall be printed
+# return: early-phase weight in units of nC
 def sdEarlyPhaseWeight(set, set2 = [], pr = True):
 	mean = meanEarlyPhaseWeight(set, set2, False)
 	summed_qu_dev = 0
@@ -352,6 +371,7 @@ def sdEarlyPhaseWeight(set, set2 = [], pr = True):
 # set: the first set of neurons (presynaptic)
 # set2 [optional]: the seconds set of neurons (postsynaptic); if not specified, connections within "set" are considered
 # pr [optional]: specifies if result shall be printed
+# return: late-phase weight in units of nC
 def meanLatePhaseWeight(set, set2 = [], pr = True):
 	summed_weight = 0
 	connection_num = 0
@@ -383,6 +403,7 @@ def meanLatePhaseWeight(set, set2 = [], pr = True):
 # set: the first set of neurons (presynaptic)
 # set2 [optional]: the seconds set of neurons (postsynaptic); if not specified, connections within "set" are considered
 # pr [optional]: specifies if result shall be printed
+# return: late-phase weight in units of nC
 def sdLatePhaseWeight(set, set2 = [], pr = True):
 	mean = meanLatePhaseWeight(set, set2, False)
 	summed_qu_dev = 0
@@ -408,6 +429,7 @@ def sdLatePhaseWeight(set, set2 = [], pr = True):
 # Prints and returns all the core neurons to which neuron i provides input
 # i: neuron index
 # pr [optional]: specifies if result is printed
+# return: array of neuron numbers
 def connectionsToCore(i, pr = True):
 	global adj
 	ctc = core[np.in1d(core, outgoingConnections(i, False))].flatten()
@@ -420,6 +442,7 @@ def connectionsToCore(i, pr = True):
 # Prints and returns all the recall-stimulated core neurons to which neuron i provides input
 # i: neuron index
 # pr [optional]: specifies if result is printed
+# return: array of neuron numbers
 def connectionsToRSCore(i, pr = True):
 	global adj
 	ctrsc = core_recall[np.in1d(core_recall, outgoingConnections(i, False))].flatten()
@@ -432,6 +455,7 @@ def connectionsToRSCore(i, pr = True):
 # Prints and returns all the non-core neurons to which neuron i provides input
 # i: neuron index
 # pr [optional]: specifies if result is printed
+# return: array of neuron numbers
 def connectionsToNonCore(i, pr = True):
 	global adj
 	outg = outgoingConnections(i, False)
@@ -458,7 +482,7 @@ def setRhombCore(core_center, core_radius):
 			core = np.append(core, np.array([core_center+i*Nl+j]))
 
 # printMeanWeights
-# Prints mean and standard deviation of CA, outgoing, incoming, and control weight
+# Prints mean and standard deviation of CA, outgoing, incoming, and control weight in units of nC
 def printMeanWeights():
 	print("--------------------------------")
 	print("Core -> core ('CA'):")