def csa_example():
cs = csa.cset(csa.random(0.1), 10000.0, 1.0)
pop1 = nest.LayoutNetwork("iaf_neuron", [16])
pop2 = nest.LayoutNetwork("iaf_neuron", [16])
nest.PrintNetwork(10)
nest.CGConnect(pop1, pop2, cs, {"weight": 0, "delay": 1})
pg = nest.Create("poisson_generator", params={"rate": 80000.0})
nest.DivergentConnect(pg, nest.GetLeaves(pop1)[0], 1.2, 1.0)
vm_params = {
"record_to": ["memory"],
"withgid": True,
"withtime": True,
"interval": 0.1
}
vm = nest.Create("voltmeter", params=vm_params)
nest.DivergentConnect(vm, nest.GetLeaves(pop2)[0])
nest.Simulate(50.0)
from nest import visualization
allnodes = pg + nest.GetLeaves(pop1)[0] + nest.GetLeaves(pop2)[0] + vm
visualization.plot_network(allnodes, "test_csa.png")
if havePIL:
im = Image.open("test_csa.png")
im.show()
from nest import voltage_trace
voltage_trace.from_device(vm)
def csa_example():
cs = csa.cset(csa.random(0.1), 10000.0, 1.0)
pop1 = nest.LayoutNetwork("iaf_neuron", [16])
pop2 = nest.LayoutNetwork("iaf_neuron", [16])
nest.PrintNetwork(10)
nest.CGConnect(pop1, pop2, cs, {"weight": 0, "delay": 1})
pg = nest.Create("poisson_generator", params={"rate": 80000.0})
nest.DivergentConnect(pg, nest.GetLeaves(pop1)[0], 1.2, 1.0)
vm_params = {"record_to": ["memory"], "withgid": True,
"withtime": True, "interval": 0.1}
vm = nest.Create("voltmeter", params=vm_params)
nest.DivergentConnect(vm, nest.GetLeaves(pop2)[0])
nest.Simulate(50.0)
from nest import visualization
allnodes = pg + nest.GetLeaves(pop1)[0] + nest.GetLeaves(pop2)[0] + vm
visualization.plot_network(allnodes, "test_csa.png")
if havePIL:
im = Image.open("test_csa.png")
im.show()
from nest import voltage_trace
voltage_trace.from_device(vm)
def checkConninMV():
nest.ResetKernel()
nest.SetKernelStatus({
"resolution": timestep,
"overwrite_files": True,
"rng_seeds": seed,
"print_time": True,
"local_num_threads": num_threads
})
nest.CopyModel("iaf_cond_alpha", "d21", d2_params)
#nest.CopyModel("izhikevich", "d1", d1_params_iz)
nest.CopyModel("iaf_cond_alpha", "d22", d2_params)
#nest.CopyModel("izhikevich", "d2", d2_params_iz)
d21 = nest.Create("d21", 1)
d22 = nest.Create("d22", 1)
nest.SetStatus(d22, {'I_e': 27.}) # Has to be tuned so that d2 is at -80
# nest.SetStatus(d1,{'I_e':69.}) # Has to be tuned so that d1 is at -80
dc = nest.Create("dc_generator", 1)
sd = nest.Create("spike_detector", 2)
mult = nest.Create("multimeter",
1,
params={
"withgid": True,
"withtime": True,
"record_from": ["V_m"]
})
nest.Connect(d21, [sd[0]])
nest.Connect(d22, [sd[1]])
nest.Connect(dc, d21)
# nest.Connect(dc,d2)
# nest.Connect(mult,d1)
nest.Connect(mult, d22)
nest.Connect(d21, d22, syn_spec={'weight': jd2d2})
nest.SetStatus(dc, params={"amplitude": 250.})
nest.Simulate(1000.)
evs_d1 = nest.GetStatus([sd[0]])[0]["events"]["senders"]
ts_d1 = nest.GetStatus([sd[0]])[0]["events"]["times"]
V_m = nest.GetStatus(mult)[0]["events"]["V_m"]
ts = nest.GetStatus(mult)[0]["events"]["times"]
inds = np.where(ts > 400.)
Vmmin = np.min(V_m[inds])
print "conn_strength", Vmmin + 80.
# pl.figure(1)
# rplt.from_device(sd)
pl.figure(2)
voltage_trace.from_device(mult)
pl.plot(ts_d1, np.ones(len(ts_d1)) * -80., 'r|', markersize=10)
pl.show()
def csa_example():
cs = csa.cset(csa.random(0.1), 10000.0, 1.0)
# This does not work yet, as the implementation does not yet
# support nested subnets.
#
#pop1 = nest.LayoutNetwork("iaf_neuron", [4,4])
#pop2 = nest.LayoutNetwork("iaf_neuron", [4,4])
pop1 = nest.LayoutNetwork("iaf_neuron", [16])
pop2 = nest.LayoutNetwork("iaf_neuron", [16])
nest.PrintNetwork(10)
nest.CGConnect(pop1, pop2, cs, {"weight": 0, "delay": 1})
pg = nest.Create("poisson_generator", params={"rate": 80000.0})
nest.DivergentConnect(pg, nest.GetLeaves(pop1)[0], 1.2, 1.0)
vm = nest.Create("voltmeter",
params={
"record_to": ["memory"],
"withgid": True,
"withtime": True,
"interval": 0.1
})
nest.DivergentConnect(vm, nest.GetLeaves(pop2)[0])
nest.Simulate(50.0)
# Not yet supported in NEST
#from nest import visualization
#allnodes = [pg, nest.GetLeaves(pop1)[0], nest.GetLeaves(pop2)[0], vm]
#visualization.plot_network(allnodes, "test_csa.png", "png", plot_modelnames=True
#if havePIL:
# im = Image.open("test_csa.png")
# im.show()
from nest import voltage_trace
voltage_trace.from_device(vm)
def csa_example():
cs = csa.cset(csa.random(0.1), 10000.0, 1.0)
# This does not work yet, as the implementation does not yet
# support nested subnets.
#
# pop1 = nest.LayoutNetwork("iaf_neuron", [4,4])
# pop2 = nest.LayoutNetwork("iaf_neuron", [4,4])
pop1 = nest.LayoutNetwork("iaf_neuron", [16])
pop2 = nest.LayoutNetwork("iaf_neuron", [16])
nest.PrintNetwork(10)
nest.CGConnect(pop1, pop2, cs, {"weight": 0, "delay": 1})
pg = nest.Create("poisson_generator", params={"rate": 80000.0})
nest.DivergentConnect(pg, nest.GetLeaves(pop1)[0], 1.2, 1.0)
vm = nest.Create("voltmeter", params={"record_to": ["memory"], "withgid": True, "withtime": True, "interval": 0.1})
nest.DivergentConnect(vm, nest.GetLeaves(pop2)[0])
nest.Simulate(50.0)
# Not yet supported in NEST
# from nest import visualization
# allnodes = [pg, nest.GetLeaves(pop1)[0], nest.GetLeaves(pop2)[0], vm]
# visualization.plot_network(allnodes, "test_csa.png", "png", plot_modelnames=True
# if havePIL:
# im = Image.open("test_csa.png")
# im.show()
from nest import voltage_trace
voltage_trace.from_device(vm)
the neurons of the pre-synaptic population.
"""
pg = nest.Create("poisson_generator", params={"rate": 100000.0})
nest.Connect(pg, pre, "all_to_all")
"""
To measure and record the membrane potentials of the neurons, we
create a `voltmeter` and connect it to all post-synaptic nodes.
"""
vm = nest.Create("voltmeter")
nest.Connect(vm, post, "all_to_all")
"""
We save the whole connection graph of the network as a PNG image
using the `plot_network` function of the `visualization` submodule of
PyNEST.
"""
allnodes = pg + pre + post + vm
visualization.plot_network(allnodes, "csa_example_graph.png")
"""
Finally, we simulate the network for 50 ms. The voltage traces of
the post-synaptic nodes are plotted.
"""
nest.Simulate(50.0)
voltage_trace.from_device(vm)
"""
To stimulate the network, we create a `poisson_generator` and set it
up to fire with a rate of 100000 spikes per second. It is connected to
the neurons of the pre-synaptic population.
"""
pg = nest.Create("poisson_generator", params={"rate": 100000.0})
nest.Connect(pg, pre, "all_to_all")
"""
To measure and record the membrane potentials of the neurons, we
create a `voltmeter` and connect it to all post-synaptic nodes.
"""
vm = nest.Create("voltmeter")
nest.Connect(vm, post, "all_to_all")
"""
We save the whole connection graph of the network as a PNG image
using the `plot_network` function of the `visualization` submodule of
PyNEST.
"""
allnodes = pg + pre + post + vm
visualization.plot_network(allnodes, "csa_example_graph.png")
"""
Finally, we simulate the network for 50 ms. The voltage traces of
the post-synaptic nodes are plotted.
"""
nest.Simulate(50.0)
voltage_trace.from_device(vm)
nest.Connect(voltmeter, sero_neuron)
dt = 10
T = 150
weight = None
time = []
sero_dyn = []
results_folder = ""
if nest.GetStatus(neuron2)[0]['local']:
sum_time = 0
for t in range(0, T + dt, dt):
nest.Simulate(t)
sum_time += t
conns = nest.GetConnections(neuron1, synapse_model="sero")
n = nest.GetStatus(conns)[0]['n']
time.append(sum_time)
sero_dyn.append(n)
nest.raster_plot.from_device(sd, hist=True, hist_binwidth=100.)
pl.savefig(results_folder + "spikes_" + str(t) + ".png")
pl.close()
plot.from_device(voltmeter, timeunit="s")
pl.savefig(results_folder + "voltage_" + str(t) + ".png")
pl.close()
plt.plot(time, sero_dyn)
plt.xlabel('Time (ms)')
plt.title('Serotonin concentration dynamics')
plt.savefig(results_folder + "sero_dynamic_" + str(t) + ".png")
raise
# Tab delimiter in a string
dlm = '\t'
# Next line symbol
nl = '\n'
# Set-up file header: time[ms], connection weight, serotonin concentration, eligibility trace
fname.write('time'+ dlm + 'weight' + dlm + 'n' + dlm + 'c' + nl)
def get_neuron1_prop(property_name):
return str(nest.GetStatus(nest.FindConnections(neuron1, synapse_model="sero"))[0][property_name])
# Run simualtion
weight = None
for t in np.arange(0, T + dt, dt):
if nest.GetStatus(neuron2)[0]['local']:
data = str(t) + dlm + get_neuron1_prop('weight') + dlm + get_neuron1_prop('n') + dlm + get_neuron1_prop('c') + nl
fname.write(data)
nest.Simulate(dt)
print '///////////////////////////////// ' + str(t/T * 100) + ' % completed'
if nest.GetStatus(neuron2)[0]['local']:
print("weight = " + str(weight) + " pA")
fname.close()
print("Eligibility trace = " + get_neuron1_prop('c'))
print("Serotonin conc-on = " + get_neuron1_prop('n'))
plot.from_device(voltmeter, timeunit="s")
plot.show()
times = events["times"] Vm = events["V_m"] Vm_exp = Vm[np.where(senders == n_exp)] Vm_cond = Vm[np.where(senders == n_cond)] Vm_exp_b4 = Vm[np.logical_and(times < 199, senders == n_exp)] Vm_cond_b4 = Vm[np.logical_and(times < 199, senders == n_cond)] print "Amplitudes of psc_exp and cond_exp before spike" print np.mean(Vm_exp_b4), np.mean(Vm_cond_b4) print "Amplitudes of psc_exp and cond_exp after spike" print max(Vm_exp), max(Vm_cond) print "PSP sizes for psc_exp and cond_exp after spike" print np.mean(Vm_exp_b4) - max(Vm_exp), np.mean(Vm_cond_b4) - max(Vm_cond) print np.argmax(Vm_exp), np.argmax(Vm_cond) vt.from_device(mm) vt.show() # V_m baseline for I_e = 300 / 200: -58. # V_m baseline for I_e = -300 / -200: -82. # SOLUTION # With +ve background #Amplitudes of psc_exp and cond_exp before spike #-62.4800000189 -62.5000288791 #Amplitudes of psc_exp and cond_exp after spike #-62.4746501618 -62.4529781256 #PSP sizes for psc_exp and cond_exp after spike #-0.00534985715843 -0.0470507534623 # Without +ve background
def calcFI():
#amplitudesList = np.arange(3.5,4.5,0.1)
amplitudesList = np.arange(100, 500, 50.)
listD1 = []
listD2 = []
for amp in amplitudesList:
nest.ResetKernel()
nest.SetKernelStatus({
"resolution": timestep,
"overwrite_files": True,
"rng_seeds": seed,
"print_time": True,
"local_num_threads": num_threads
})
nest.CopyModel("iaf_cond_alpha", "d1", d1_params)
#nest.CopyModel("izhikevich", "d1", d1_params_iz)
nest.CopyModel("iaf_cond_alpha", "d2", d2_params)
#nest.CopyModel("izhikevich", "d2", d2_params_iz)
d1 = nest.Create("d1", 1)
d2 = nest.Create("d2", 1)
dc = nest.Create("dc_generator", 1)
sd = nest.Create("spike_detector", 2)
mult = nest.Create("multimeter",
1,
params={
"withgid": True,
"withtime": True,
"record_from": ["V_m"]
})
nest.Connect(d1, [sd[0]])
nest.Connect(d2, [sd[1]])
nest.Connect(dc, d1)
nest.Connect(dc, d2)
nest.Connect(mult, d1)
nest.Connect(mult, d2)
nest.SetStatus(dc, params={"amplitude": amp})
nest.Simulate(10000.)
evs_d1 = nest.GetStatus([sd[0]])[0]["events"]["senders"]
ts_d1 = nest.GetStatus([sd[0]])[0]["events"]["times"]
evs_d2 = nest.GetStatus([sd[1]])[0]["events"]["senders"]
ts_d2 = nest.GetStatus([sd[1]])[0]["events"]["times"]
listD1.append(len(ts_d1) / 10.0)
listD2.append(len(ts_d2) / 10.0)
# voltage_trace.from_device(mult)
# pl.show()
FI = dict()
FI["d1"] = listD1
FI["d2"] = listD2
pickle.dump(FI, open("../../data/FI.pickle", "w"))
pl.figure()
pl.text(70, 62, "A", fontweight='bold', fontsize=15)
pl.plot(amplitudesList, listD1, 'bo-', label='D1', linewidth=1.5)
pl.plot(amplitudesList, listD2, 'go-', label='D2', linewidth=1.5)
pl.legend(loc='best')
pl.xlabel("Amplitude(pA)", fontweight='bold', fontsize=14)
pl.ylabel("Firing rate (sps)", fontweight='bold', fontsize=14)
for x in pl.gca().get_xticklabels():
x.set_fontweight('bold')
x.set_fontsize(10)
for x in pl.gca().get_yticklabels():
x.set_fontweight('bold')
x.set_fontsize(10)
pl.savefig("../../data/FI.pdf")
print "d1", FI["d1"], "d2", FI["d2"], amplitudesList
pl.figure()
voltage_trace.from_device(mult)
pl.show()
