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demo_cx_Up-Down.py
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demo_cx_Up-Down.py
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"""
2Layer Cortical Network Simulation
Destexhe Lab, Lyle Muller
LTS 2-Layer Demo
18 Mar 2010
This file implements a PyNN version of the model detailed in
Destexhe, A. Self-sustained asynchronous irregular states and
Up/Down states in thalamic, cortical and thalamocortical
networks of nonlinear integrate-and-fire neurons.
Journal of Computational Neuroscience 27: 493-506, 2009.
arXiv preprint: http://arxiv.org/abs/0809.0654
This file reproduces Fig 13 of that paper
Updated to work with PyNN 0.8, August 2016 by Andrew Davison
"""
import numpy.random, os
from pyNN.nest import *
from numpy import *
import matplotlib.pyplot as plot
from pyNN.utility import Timer, plotting
setup()
### Switch Statements
DistanceDep = True
run_time = 5000.0 # ms
b = .005 # b = .05 SA, .005 WA
#
# Population Numbers
py_n = 1600
inh_n = 400
pyB_n = 400
inhB_n = 100
#
# Synaptic Conductances / External Stimulation
g_e = 6e-3 # nS
g_i = 67e-3 # nS
g_ext = 6e-3
stim_dur = 50.0
p_c = .02
scale_factor = round((py_n+inh_n) / (pyB_n+inhB_n))
inter_p_c = .01
#
# Parameters
py_params = {'tau_m' : 20.0, # ms
'tau_syn_E' : 5.0,
'tau_syn_I' : 10.0,
'tau_refrac' : 2.5,
'v_rest' : -60.0,
'v_reset' : -60.0,
'v_thresh' : -50.0,
'delta_T' : 2.5,
'tau_w' : 600.0,
'cm' : 0.200,
'a' : 0.001e3,
'b' : b }
pyB_params = {'tau_m' : 20.0, # ms
'tau_syn_E' : 5.0,
'tau_syn_I' : 10.0,
'tau_refrac' : 2.5,
'v_rest' : -60.0,
'v_reset' : -60.0,
'v_thresh' : -50.0,
'delta_T' : 2.5,
'tau_w' : 600.0,
'cm' : 0.200,
'a' : 0.001e3,
'b' : b }
inh_params = {'tau_m' : 20.0, # ms
'tau_syn_E' : 5.0,
'tau_syn_I' : 10.0,
'tau_refrac' : 2.5,
'v_rest' : -60.0,
'v_reset' : -60.0,
'v_thresh' : -50.0,
'delta_T' : 2.5,
'tau_w' : 600.0,
'cm' : 0.200,
'a' : 0.001e3,
'b' : 0.0 }
V_INIT = -60
print("Building Network")
# Create Populations
py = Population(py_n, EIF_cond_alpha_isfa_ista, cellparams=py_params, initial_values={'v': V_INIT})
inh = Population(inh_n, EIF_cond_alpha_isfa_ista, cellparams=inh_params, initial_values={'v': V_INIT})
pyB = Population(pyB_n, EIF_cond_alpha_isfa_ista, cellparams=pyB_params, initial_values={'v': V_INIT})
inhB = Population(inhB_n, EIF_cond_alpha_isfa_ista, cellparams=inh_params, initial_values={'v': V_INIT})
#
# External Stimulation - Start of Simulation
ext_py = Population(1, SpikeSourcePoisson,
cellparams={'start':0.0, 'rate':50., 'duration':stim_dur})
ext_prj_py = Projection(ext_py, py, FixedProbabilityConnector(.02),
synapse_type=StaticSynapse(weight=g_ext, delay=None),
receptor_type='excitatory')
ext_pyB = Population(1, SpikeSourcePoisson,
cellparams={'start':0.0, 'rate':50., 'duration':stim_dur})
ext_prj_pyB = Projection(ext_pyB, pyB, FixedProbabilityConnector(.02),
synapse_type=StaticSynapse(weight=g_ext, delay=None),
receptor_type='excitatory')
#
# LTS Subgroup - 10% of Layer B
cells = pyB.local_cells
#cells = numpy.random.permutation(cells)[0:int(0.1*len(cells))]
cells = cells[0:int(0.05*len(cells))]
for cell in cells:
cell.a = 0.02e3
cell.b = 0.0
# Connect Groups - Random Connect
print("Random Connect")
rng = NumpyRNG(1235342134, parallel_safe=False)
py_py = Projection(py, py,
FixedProbabilityConnector(p_c, allow_self_connections=False, rng=rng),
synapse_type=StaticSynapse(weight=g_e, delay=None),
receptor_type='excitatory')
print("Number of Synapses (py_py):", len(py_py))
py_inh = Projection(py, inh,
FixedProbabilityConnector(p_c, allow_self_connections=False, rng=rng),
synapse_type=StaticSynapse(weight=g_e, delay=None),
receptor_type='excitatory')
print("Number of Synapses (py_inh):", len(py_inh))
inh_py = Projection(inh, py,
FixedProbabilityConnector(p_c, allow_self_connections=False, rng=rng),
synapse_type=StaticSynapse(weight=g_i, delay=None),
receptor_type='inhibitory')
print("Number of Synapses (inh_py):", len(inh_py))
inh_inh = Projection(inh, inh,
FixedProbabilityConnector(p_c, allow_self_connections=False, rng=rng),
synapse_type=StaticSynapse(weight=g_i, delay=None),
receptor_type='inhibitory')
print("Number of Synapses (inh_inh):", len(inh_inh))
pyB_pyB = Projection(pyB, pyB,
FixedProbabilityConnector(p_c*scale_factor, allow_self_connections=False, rng=rng),
synapse_type=StaticSynapse(weight=g_e, delay=None),
receptor_type='excitatory')
print("Number of Synapses (pyB_pyB):", len(pyB_pyB))
pyB_inhB = Projection(pyB, inhB,
FixedProbabilityConnector(p_c*scale_factor, allow_self_connections=False, rng=rng),
synapse_type=StaticSynapse(weight=g_e, delay=None),
receptor_type='excitatory')
print("Number of Synapses (pyB_inhB):", len(pyB_inhB))
inhB_pyB = Projection(inhB, pyB,
FixedProbabilityConnector(p_c*scale_factor, allow_self_connections=False, rng=rng),
synapse_type=StaticSynapse(weight=g_i, delay=None),
receptor_type='inhibitory')
print("Number of Synapses (inhB_pyB):", len(inhB_pyB))
inhB_inhB = Projection(inhB, inhB,
FixedProbabilityConnector(p_c*scale_factor, allow_self_connections=False, rng=rng),
synapse_type=StaticSynapse(weight=g_i, delay=None),
receptor_type='inhibitory')
print("Number of Synapses (inhB_inhB):", len(inhB_inhB))
### Excitatory Connection Between the Layers
py_pyB = Projection(py, pyB,
FixedProbabilityConnector(inter_p_c, allow_self_connections=False, rng=rng),
synapse_type=StaticSynapse(weight=g_e, delay=None),
receptor_type='excitatory')
pyB_py = Projection(pyB, py,
FixedProbabilityConnector(inter_p_c, allow_self_connections=False, rng=rng),
synapse_type=StaticSynapse(weight=g_e, delay=None),
receptor_type='excitatory')
py_inhB = Projection(py, inhB,
FixedProbabilityConnector(inter_p_c, allow_self_connections=False, rng=rng),
synapse_type=StaticSynapse(weight=g_e, delay=None),
receptor_type='excitatory')
pyB_inh = Projection(pyB, inh,
FixedProbabilityConnector(inter_p_c, allow_self_connections=False, rng=rng),
synapse_type=StaticSynapse(weight=g_e, delay=None),
receptor_type='excitatory')
# Recording
#pyB.record_v(10)
#inhB.record_v(10)
py.record('spikes')
inh.record('spikes')
pyB.record('spikes')
inhB.record('spikes')
#
print("Running Network")
timer = Timer()
timer.reset()
run(run_time)
simCPUtime = timer.elapsedTime()
print("Simulation Time: %s" % str(simCPUtime))
py_sp = py.get_data().segments[0].spiketrains
# print("Layer A Pyramidal Mean Rate (initial stimulation): %s" % str(py_sp.mean_rate(t_start=0,
# t_stop=stim_dur)))
# print("Layer A Pyramidal Mean Rate: %s" % str(py_sp.mean_rate(t_start=stim_dur,
# t_stop=run_time)))
# print("Layer A Pyramidal Mean CV: %s" % str(mean(py_sp.cv_isi(float_only=True))))
plotting.plot_spiketrains(plot.subplot(221), py_sp, label='PY Layer A', ms=0.1)
inh_sp = inh.get_data().segments[0].spiketrains
# print("Layer A Interneuron Mean Rate (initial stimulation): %s" % str(inh_sp.mean_rate(t_start=0,
# t_stop=stim_dur)))
# print("Layer A Interneuron Mean Rate: %s" % str(inh_sp.mean_rate(t_start=stim_dur,
# t_stop=run_time)))
# print("Layer A Interneuron Mean CV: %s" % str(mean(inh_sp.cv_isi(float_only=True))))
plotting.plot_spiketrains(plot.subplot(222), inh_sp, label='INH Layer A', ms=0.1)
pyB_sp = pyB.get_data().segments[0].spiketrains
# print("Layer B Pyramidal Mean Rate (initial stimulation): %s" % str(pyB_sp.mean_rate(t_start=0,
# t_stop=stim_dur)))
# print("Layer B Pyramidal Mean Rate: %s" % str(pyB_sp.mean_rate(t_start=stim_dur,
# t_stop=run_time)))
# print("Layer B Pyramidal Mean CV: %s" % str(mean(pyB_sp.cv_isi(float_only=True))))
plotting.plot_spiketrains(plot.subplot(223), pyB_sp, label='PY Layer B', ms=0.1)
plot.axhline(y=.05*pyB_n, linewidth=2, color='r')
inhB_sp = inhB.get_data().segments[0].spiketrains
# print("Layer B Interneuron Mean Rate (initial stimulation): %s" % str(inhB_sp.mean_rate(t_start=0,
# t_stop=stim_dur)))
# print("Layer B Interneuron Mean Rate: %s" % str(inhB_sp.mean_rate(t_start=stim_dur,
# t_stop=run_time)))
# print("Layer B Interneuron Mean CV: %s" % str(mean(inhB_sp.cv_isi(float_only=True))))
plotting.plot_spiketrains(plot.subplot(224), inhB_sp, label='INH Layer B', ms=0.1, xlabel="Time(ms)", xticks=True)
plot.savefig("demo_cx_Up-Down.png")
# Cleanup
end()