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mdPul_two_columns.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Dec 4 11:35:36 2019
@author: aaussel
"""
from cells.RE_mdPul import *
from cells.TC_mdPul import *
from cells.HTC_buffer_mdPul_Destxhe_tests import *
from mdPul_one_column import *
from scipy import signal
prefs.codegen.target = 'numpy'
defaultclock.dt = 0.01*ms
runtime=1*second
start_scope()
def create_mdPul(N_HTC,N_TC,N_RE,condition,in_mode,theta_phase):
all_neuronsA,all_synapsesA,all_gap_junctionsA,all_monitorsA=create_mdPul_column(N_HTC,N_TC,N_RE,condition,in_mode,theta_phase)
all_neuronsB,all_synapsesB,all_gap_junctionsB,all_monitorsB=create_mdPul_column(N_HTC,N_TC,N_RE,condition,in_mode,theta_phase)
HTC_A=all_neuronsA[0]
HTC_B=all_neuronsB[0]
RE_A=NeuronGroup(N_RE,eq_RE_mdPul,threshold='V>0*mvolt',refractory=3*ms,method='rk4')
RE_A.V = '-70*mvolt+20*mvolt*rand()'
RE_A.J = '600 * nA * cmeter ** -2'
RE_A.Ca_i = '1e-7 * mole * metre**-3'
RE_B=NeuronGroup(N_RE,eq_RE_mdPul,threshold='V>0*mvolt',refractory=3*ms,method='rk4')
RE_B.V = '-70*mvolt+20*mvolt*rand()'
RE_B.J = '600 * nA * cmeter ** -2'
RE_B.Ca_i = '1e-7 * mole * metre**-3'
##Synapses
eq_syn='''_post=s_i*g_i*(V_post-V_i) : amp * meter ** -2 (summed)
ds_i/dt=-s_i/taud_i+(1-s_i)/taur_i*0.5*(1+tanh(V_pre/10/mV)) : 1
g_i : siemens * meter**-2
V_i : volt
taud_i : second
taur_i : second
'''
#0.006, 0.03
g_HTCRE=0.4 * msiemens * cm **-2 #0.4
g_REHTC=0.4 * msiemens * cm **-2
g_AB=1
S_HTC_RE_A=Synapses(HTC_A,RE_A,model='IsynHTC'+eq_syn)
S_HTC_RE_A.connect()
S_HTC_RE_A.g_i=g_HTCRE
S_HTC_RE_A.taur_i=0.25*ms
S_HTC_RE_A.taud_i=5*ms
S_HTC_RE_A.V_i=0*mV
S_RE_A_HTC_B=Synapses(RE_A,HTC_B,model='IsynREA'+eq_syn)
S_RE_A_HTC_B.connect()
S_RE_A_HTC_B.g_i=g_REHTC*g_AB
S_RE_A_HTC_B.taur_i=0.25*ms
S_RE_A_HTC_B.taud_i=20*ms
S_RE_A_HTC_B.V_i=-80*mV
S_HTC_RE_B=Synapses(HTC_B,RE_B,model='IsynHTC'+eq_syn)
S_HTC_RE_B.connect()
S_HTC_RE_B.g_i=g_HTCRE
S_HTC_RE_B.taur_i=0.25*ms
S_HTC_RE_B.taud_i=5*ms
S_HTC_RE_B.V_i=0*mV
S_RE_B_HTC_A=Synapses(RE_B,HTC_A,model='IsynREB'+eq_syn)
S_RE_B_HTC_A.connect()
S_RE_B_HTC_A.g_i=g_REHTC*g_AB
S_RE_B_HTC_A.taur_i=0.25*ms
S_RE_B_HTC_A.taud_i=20*ms
S_RE_B_HTC_A.V_i=-80*mV
##Define monitors
RA=SpikeMonitor(RE_A,record=True)
RB=SpikeMonitor(RE_B,record=True)
all_neurons=all_neuronsA+all_neuronsB+(RE_A,RE_B)
all_synapses=all_synapsesA,all_synapsesB+(S_HTC_RE_A,S_RE_A_HTC_B,S_HTC_RE_B,S_RE_B_HTC_A)
all_monitors=all_monitorsA+all_monitorsB+(RA,RB)
all_gap_junctions=all_gap_junctionsA,all_gap_junctionsB
return all_neurons,all_synapses,all_gap_junctions,all_monitors
if __name__=='__main__':
close('all')
prefs.codegen.target = 'numpy'
runtime=1*second
f=13*Hz #rythmic input frequency
input_on=False
N_HTC, N_TC,N_RE= 20,80,100 #Number of neurons of RE, TC, and HTC type
Vrev_inp=0*mV
taurinp=0.1*ms
taudinp=0.5*ms
tauinp=taudinp
Vhigh=0*mV
Vlow=-80*mV
ginp_IB=0* msiemens * cm **-2
ginp=0* msiemens * cm **-2
Vrev_inp2=0*mV
taurinp2=0.1*ms
taudinp2=0.5*ms
tauinp2=taudinp2
Vhigh2=0*mV
Vlow2=-80*mV
#condition='mGluR1'
condition='mAChR'
in_mode='single_spike'
# in_mode='burst'
theta_phase='good'
if condition=='mGluR1':
gKL_TC=0.0028e-3 * siemens * cm **-2
gKL_HTC=0.0069e-3 * siemens * cm **-2
gKL_RE=0.05e-3 * siemens * cm **-2
elif condition=='mAChR':
gKL_TC=0.0028e-3 * siemens * cm **-2
gKL_HTC=0.0069e-3 * siemens * cm **-2
gKL_RE=0.08e-3 * siemens * cm **-2
# gapp=0.1*mamp * cmeter ** -2 # in HTC cells
gKL_HTC=0.001e-3 * siemens * cm **-2
gapp=0.1*mamp * cmeter ** -2 # in HTC cells
net=Network()
all_neurons,all_synapses,all_gap_junctions,all_monitors=create_mdPul(N_HTC,N_TC,N_RE,condition,in_mode,theta_phase)
R1A,R2A,R3A,V1A,V2A,V3A,I1A,I2A,R1B,R2B,R3B,V1B,V2B,V3B,I1B,I2B,RA,RB=all_monitors
# def generate_spike_timing(N,f,start_time,end_time=runtime):
# list_time_and_i=[]
# for i in range(N):
# list_time=[(start_time,i)]
# next_spike=list_time[-1][0]+(1+0.1*rand())/f
# while next_spike<end_time:
# list_time.append((next_spike,i))
# next_spike=list_time[-1][0]+(1+0.1*rand())/f
# list_time_and_i+=list_time
# return array(list_time_and_i)
#
# if input_on:
# RSA.ginp_RS=1* msiemens * cm **-2 #1
# inputs_topdown=generate_spike_timing(N_RS,f,0*ms,end_time=3000*ms)
## print(inputs_topdown)
# G_topdown = SpikeGeneratorGroup(N_RS, inputs_topdown[:,1], inputs_topdown[:,0]*second)
# topdown_in=Synapses(G_topdown,RSA,on_pre='Vinp=Vhigh')
# topdown_in.connect(j='i')
#
# RSB.ginp_RS=2* msiemens * cm **-2
# inputs_topdown2=generate_spike_timing(N_RS,f,0*ms,end_time=3000*ms)
## print(inputs_topdown2)
# G_topdown2 = SpikeGeneratorGroup(N_RS, inputs_topdown2[:,1], inputs_topdown2[:,0]*second)
# topdown_in2=Synapses(G_topdown2,RSB,on_pre='Vinp=Vhigh')
# topdown_in2.connect(j='i')
#
# RSC.ginp_RS=1* msiemens * cm **-2
# inputs_topdown3=generate_spike_timing(N_RS,f,0*ms,end_time=3000*ms)
## print(inputs_topdown)
# G_topdown3 = SpikeGeneratorGroup(N_RS, inputs_topdown3[:,1], inputs_topdown3[:,0]*second)
# topdown_in3=Synapses(G_topdown3,RSC,on_pre='Vinp=Vhigh')
# topdown_in3.connect(j='i')
#
# for elem in [G_topdown,topdown_in,G_topdown2,topdown_in2,G_topdown3,topdown_in3]:
# net.add(elem)
HTC_A,HTC_B=all_neurons[0],all_neurons[6]
if in_mode=='single_spike':
HTC_A.delay_steps = [1] # delay in time steps per neuron
HTC_B.delay_steps = [1] # delay in time steps per neuron
buffer_size = 2 # 1+Maximum delay (in time steps)
else :
HTC_A.delay_steps = [3999] # delay in time steps per neuron
HTC_B.delay_steps = [3999] # delay in time steps per neuron
buffer_size = 4000 # 1+Maximum delay (in time steps)
HTC_A.variables.add_array('voltage_buffer', dimensions=volt.dim, size=(buffer_size, len(HTC_A)))
HTC_B.variables.add_array('voltage_buffer', dimensions=volt.dim, size=(buffer_size, len(HTC_B)))
update_code = '''buffer_pointer = (buffer_pointer + 1) % buffer_size
voltage_delayed = update_voltage_buffer(V, voltage_buffer, buffer_pointer, delay_steps, buffer_size)'''
buffer_updater_A = HTC_A.run_regularly(update_code, codeobj_class=NumpyCodeObject)
buffer_updater_B = HTC_B.run_regularly(update_code, codeobj_class=NumpyCodeObject)
@check_units(V=volt, voltage_buffer=volt, buffer_pointer=1, delay_steps=1, buffer_size=1, result=volt)
def update_voltage_buffer(V, voltage_buffer, buffer_pointer, delay_steps, buffer_size):
# Write current rate into the buffer
voltage_buffer[buffer_pointer, :] = V
# Get delayed rates
rows = (buffer_pointer - delay_steps) % buffer_size
return voltage_buffer[rows, arange(len(rows))]
net.add(all_neurons)
net.add(all_synapses)
net.add(all_gap_junctions)
net.add(all_monitors)
prefs.codegen.target = 'cython'
net.run(runtime,report='text',report_period=300*second)
figure()
plot(R1A.t,R1A.i+0,'r.',label='HTC')
plot(R2A.t,R2A.i+20,'y.',label='TC')
plot(R3A.t,R3A.i+100,'g.',label='RE int')
plot(RA.t,RA.i+200,'b.',label='RE lat')
plot([0,1],[325,325],'k')
plot(R1B.t,R1B.i+350,'r.')
plot(R2B.t,R2B.i+370,'y.')
plot(R3B.t,R3B.i+450,'g.')
plot(RB.t,RB.i+550,'b.')
xlim(0,runtime/second)
yticks([150,500],['Object I','Object II'])
legend()
# figure()
# plot(V1A.t,V1A.V[0],label='TC V')
# plot(V2A.t,V2A.V[0],label='RE V')
# legend()
# f,Spectrum_LFP_V1=signal.periodogram(V1A.V[0], 100000,'flattop', scaling='spectrum')
# figure()
# plot(f,Spectrum_LFP_V1)
# xlim(0,100)
clear_cache('cython')