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Seqsynhandler with addspike
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‎snippets/seq_response_graphs.py‎

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#!/usr/bin/env python
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# coding: utf-8
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# In[1]:
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#This program sets up an LIF neuron that is sequence selective temporally.
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#Currently it will give the maximum response for a reverse sequence and
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#both forward as well as any scrambled sequence will not give as amplified a response, allowing us
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#to pick out the reverse sequence from the rest.
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#A sequence of 4 is given for each of the cases. The sequence selecting threshold in this case is -0.04 mV.
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#The connections are set up such that the first synapse is activated at the time value of the first spike,
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#second synapse is activated at the time value of the second spike and so on.
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#Author: Upinder Bhalla, Prakriti Parthasarathy
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#Date: Friday, July 22nd 2022
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# This program is free software; you can redistribute it and/or
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# modify it under the terms of the GNU General Public License as
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# published by the Free Software Foundation; either version 3, or
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# (at your option) any later version.
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#
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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# General Public License for more details.
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#
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# You should have received a copy of the GNU General Public License
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# along with this program; see the file COPYING. If not, write to
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# the Free Software Foundation, Inc., 51 Franklin Street, Fifth
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# Floor, Boston, MA 02110-1301, USA.
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import numpy as np
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import matplotlib.pyplot as plt
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import moose
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from itertools import permutations
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RUNTIME = 30.0
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SYN_WEIGHT = 0.01
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# Creating a LIF neuron that processes sequential input:
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def makeSeqCell():
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seq = moose.LIF( '/seq' )
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seq.Rm = 1e8
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seq.Cm = 1e-9
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syn = moose.SeqSynHandler( '/seq/syn' )
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syn.historyTime = 5
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syn.kernelEquation = "(x==t)*10 + (x<t)*-10 + (x>t)*-10"
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syn.kernelWidth = 5
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syn.synapse.num = 10
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for ii in range( syn.synapse.num ):
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syn.synapse[ii].weight = SYN_WEIGHT
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syn.synapse[ii].delay = 0
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syn.seqDt = 1
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syn.baseScale = 0.0
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syn.sequenceScale = 1.0
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syn.plasticityScale = 0.0
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syn.synapseOrderOption = -1
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nk = np.array( syn.kernel )
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nk.shape = ( len(nk)//syn.kernelWidth, syn.kernelWidth )
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print( nk, "\n") #Printing out the kernel
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moose.connect( syn, 'activationOut', seq, 'activation' )
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plots = moose.Neutral( '/plots' )
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plot2 = moose.Table( '/plots/p2' )
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plot3 = moose.Table( '/plots/p3' )
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moose.connect( plot2, 'requestOut', seq, 'getVm' )
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moose.connect( plot3, 'requestOut', syn, 'getSeqActivation' )
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def runSeq( seqDt, stimTimes, runtime ):
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moose.reinit()
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stimTimes = [ [1+ii*seqDt, 20-ii*seqDt] for ii in range(4)]
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print( stimTimes )
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syn = moose.element( '/seq/syn' )
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syn.seqDt = seqDt
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for idx, vv in enumerate( stimTimes ):
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for tt in vv:
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syn.synapse[idx].addSpike( tt )
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moose.start( runtime )
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vec = np.array( moose.element( '/plots/p2' ).vector )
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middle = len( vec ) // 2
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return max(vec[:middle]), max( vec[middle:] )
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def main():
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stimTimes = [[1, 18], [2, 17], [3, 16], [4, 15]] #sequence of 4 is given to 4 synapses in both forward and reverse order
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dts = [ 0.1, 0.2, 0.4, 0.6, 0.8, 0.9, 1.0, 1.1, 1.2, 1.4, 1.6, 1.8, 2, 2.2, 2.4 ]
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makeSeqCell()
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h1 = []
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h2 = []
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for seqDt in dts:
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r1, r2 = runSeq( seqDt, stimTimes, RUNTIME )
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h1.append( r1 )
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h2.append( r2 )
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moose.start( RUNTIME )
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plt.figure()
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plt.plot( dts, h1, 'g' )
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plt.title( "Forward sequences" )
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plt.xlabel( "seqDt (s)" )
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plt.ylabel( "max Vm (mV)" )
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plt.ylim( -0.07, -0.0003 )
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plt.figure()
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plt.plot( dts, h2, 'g' )
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plt.title( "Reverse sequences" )
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plt.xlabel( "seqDt (s)" )
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plt.ylabel( "max Vm (mV)" )
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plt.ylim( -0.07, -0.0003 )
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plt.show()
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if __name__ == "__main__":
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main()
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‎snippets/seqsynhandler_lif_neuron.py‎

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#!/usr/bin/env python
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# coding: utf-8
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# In[1]:
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#This program creates a sequence detecting LIF neuron.
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#We can see the shapes of the membrane potential produced by various kernels.
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#The connections are set up such that the first synapse receives a spike at 3s, second at 4s and third at 5s.
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#The connections, kernel and seqDt can be varied to obtain different results.
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#Author: Upinder Bhalla, Prakriti Parthasarathy
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#Date: Friday, July 22nd 2022
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# This program is free software; you can redistribute it and/or
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# modify it under the terms of the GNU General Public License as
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# published by the Free Software Foundation; either version 3, or
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# (at your option) any later version.
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#
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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# General Public License for more details.
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#
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# You should have received a copy of the GNU General Public License
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# along with this program; see the file COPYING. If not, write to
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# the Free Software Foundation, Inc., 51 Franklin Street, Fifth
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# Floor, Boston, MA 02110-1301, USA.
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import sys
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import os
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import moose
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import matplotlib.pyplot as plt
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import numpy as np
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RUNTIME = 30
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SYN_WEIGHT = 0.01
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#creating the LIF neuron
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def seqCell(stimTimes):
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model = moose.Neutral('/model')
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seq = moose.LIF( '/model/seq' )
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seq.Rm = 1e8
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seq.Cm = 1e-9
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synh = moose.SeqSynHandler( '/model/seq/synh' )
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synh.historyTime = 5
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synh.kernelEquation = "x==t"#"(10 - 5*max((x-t), 0))"
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synh.kernelWidth = 5
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synh.synapse.num = 10
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for ii in range( synh.synapse.num ):
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synh.synapse[ii].weight = SYN_WEIGHT
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synh.synapse[ii].delay = 0
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synh.seqDt = 1
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synh.baseScale = 0.0
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synh.sequenceScale = 1.0
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synh.plasticityScale = 0.0
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synh.synapseOrderOption = -1
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nk = np.array( synh.kernel )
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nk.shape = ( len(nk)//synh.kernelWidth, synh.kernelWidth )
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print( nk, "\n")
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moose.connect( synh, 'activationOut', seq, 'activation' )
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plots = moose.Neutral( '/plots' )
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plot2 = moose.Table( '/plots/p2' )
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plot3 = moose.Table( '/plots/p3' )
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moose.connect( plot2, 'requestOut', seq, 'getVm' ) #Vm of seq
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moose.connect( plot3, 'requestOut', synh, 'getSeqActivation' ) #synapse activation of seq due to 1st input
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moose.reinit()
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for idx, vv in enumerate( stimTimes ):
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for tt in vv:
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synh.synapse[idx].addSpike( tt )
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print("idx", idx, "tt", tt)
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moose.start( RUNTIME )
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def main():
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seqCell([[3], [4], [5]])
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plot2 = moose.element( '/plots/p2' )
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plot3 = moose.element( '/plots/p3' )
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dt = plot2.dt
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plt.figure()
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t = np.linspace( 0, RUNTIME, len( plot2.vector ) )
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plt.plot( t, plot2.vector )
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plt.title( "SeqSynHandler- Membrane potential" )
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plt.xlabel( "time (s)" )
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plt.ylabel( "Vm (mV)" )
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plt.figure()
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plt.title( "SeqSynHandler- Synaptic Activation" )
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plt.xlabel( "time (s)" )
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plt.ylabel( "synapse activation (arb units)" )
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plt.plot( t, plot3.vector )
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plt.show()
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if __name__ == "__main__":
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main()
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