source_type = SpikeSourcePoisson(rate=p_rate)
expoisson = Population(NE, source_type, label="expoisson")
print "%d Creating inhibitory Poisson generator with the same rate." % rank
inpoisson = Population(NI, source_type, label="inpoisson")
# Record spikes
print "%d Setting up recording in excitatory population." % rank
E_net.sample(Nrec).record('spikes')
E_net[0:2].record('v')
print "%d Setting up recording in inhibitory population." % rank
I_net.sample(Nrec).record('spikes')
I_net[0:2].record('v')
progress_bar = ProgressBar(width=20)
connector = FixedProbabilityConnector(epsilon, rng=rng, callback=progress_bar)
E_syn = StaticSynapse(weight=JE, delay=delay)
I_syn = StaticSynapse(weight=JI, delay=delay)
ext_Connector = OneToOneConnector(callback=progress_bar)
ext_syn = StaticSynapse(weight=JE, delay=dt)
print "%d Connecting excitatory population with connection probability %g, weight %g nA and delay %g ms." % (
rank, epsilon, JE, delay)
E_to_E = Projection(E_net, E_net, connector, E_syn, receptor_type="excitatory")
print "E --> E\t\t", len(E_to_E), "connections"
I_to_E = Projection(I_net, E_net, connector, I_syn, receptor_type="inhibitory")
print "I --> E\t\t", len(I_to_E), "connections"
input_to_E = Projection(expoisson,
E_net,
ext_Connector,
def __init__(self, interval, t_stop):
self.interval = interval
self.t_stop = t_stop
from pyNN.utility import ProgressBar
self.pb = ProgressBar(width=int(t_stop / interval), char=".")
def __init__(self, interval, t_stop):
self.interval = interval
self.t_stop = t_stop
self.pb = ProgressBar(width=int(t_stop / interval), char=".")
def build_connections(connector_type, connector_parameters):
# === Setup ==============================================================
node_id = sim.setup(**extra)
np = sim.num_processes()
host_name = socket.gethostname()
print("Host #%d is on %s" % (node_id + 1, host_name))
print("%s Initialising the simulator with %d thread(s)..." % (node_id, extra['threads']))
# === Type references ====================================================
celltype = sim.IF_cond_exp
synapsetype = sim.StaticSynapse
# === Definition of the types of neurons, synapses and connections =======
progress_bar = ProgressBar(width=20)
cell_stim = sim.SpikeSourceArray(spike_times=[1.0])
cell_exc = celltype()
cell_inh = celltype()
syn_stim = synapsetype()
syn_exc = synapsetype()
syn_inh = synapsetype()
conn_stim = connector_type(**connector_parameters)
conn_exc = connector_type(**connector_parameters)
conn_inh = connector_type(**connector_parameters)
# === Populations ========================================================
print("%s Creating cell populations..." % node_id)
pop_stim = sim.Population(n_ext, cell_stim, label="spikes")
pop_exc = sim.Population(n_exc, cell_exc, label="Excitatory_Cells")
pop_inh = sim.Population(n_inh, cell_inh, label="Inhibitory_Cells")
print("%s Connecting populations..." % node_id)
connections = {}
connections['stim2e'] = sim.Projection(
pop_stim, pop_exc, connector=conn_stim, synapse_type=syn_stim, receptor_type='excitatory')
connections['stim2i'] = sim.Projection(
pop_stim, pop_inh, connector=conn_stim, synapse_type=syn_stim, receptor_type='excitatory')
connections['e2e'] = sim.Projection(
pop_exc, pop_exc, conn_exc, syn_exc, receptor_type='excitatory')
connections['e2i'] = sim.Projection(
pop_exc, pop_inh, conn_exc, syn_exc, receptor_type='excitatory')
connections['i2e'] = sim.Projection(
pop_inh, pop_exc, conn_inh, syn_inh, receptor_type='inhibitory')
connections['i2i'] = sim.Projection(
pop_inh, pop_inh, conn_inh, syn_inh, receptor_type='inhibitory')
# === Output connection results ===========================================
# for prj in connections.keys():
# connections[prj].saveConnections('Results/VAconnections_%s_%s_np%d.conn'
# % (prj, options.simulator, np))
str_connections = "%d e->e %d e->i %d i->e %d i->i" % (connections['e2e'].size(),
connections[
'e2i'].size(),
connections[
'i2e'].size(),
connections['i2i'].size())
str_stim_connections = "%d stim->e %d stim->i" % (
connections['stim2e'].size(), connections['stim2i'].size())
if node_id == 0:
print("\n\n--- Connector : %s ---" % connector_type.__name__)
print("Nodes : %d" % np)
print("Number of Stims : %d" % n_ext)
print("Number of Exc Neurons : %d" % n_exc)
print("Number of Inh Neurons : %d" % n_inh)
print("Number of Synapses : %s" % str_connections)
print("Number of inputs : %s" % str_stim_connections)
print("\n")
def normalize_array(arr):
res = nan_to_num(arr)
res = (res != 0)
return res.astype(int)
if options.plot_figure:
filename = options.plot_figure + '_' + connector_type.__name__
from pyNN.utility.plotting import Figure, Panel
array_stim_exc = normalize_array(
connections['stim2e'].get('delay', format="array")[0:20, :])
array_stim_inh = normalize_array(
connections['stim2i'].get('delay', format="array")[0:20, :])
array_exc_exc = normalize_array(
connections['e2e'].get('delay', format="array")[0:20, :])
array_exc_inh = normalize_array(
connections['e2i'].get('delay', format="array")[0:20, :])
array_inh_exc = normalize_array(
connections['i2e'].get('delay', format="array")[0:20, :])
array_inh_inh = normalize_array(
connections['i2i'].get('delay', format="array")[0:20, :])
Figure(
Panel(array_stim_exc, data_labels=["stim->exc"], line_properties=[
{'xticks': True, 'yticks': True, 'cmap': 'Greys', 'vmin': 0.}]),
Panel(array_stim_inh, data_labels=["stim->inh"], line_properties=[
{'xticks': True, 'yticks': True, 'cmap': 'Greys', 'vmin': 0.}]),
Panel(array_exc_exc, data_labels=["exc->exc"], line_properties=[
{'xticks': True, 'yticks': True, 'cmap': 'Greys', 'vmin': 0.}]),
Panel(array_exc_inh, data_labels=["exc->inh"], line_properties=[
{'xticks': True, 'yticks': True, 'cmap': 'Greys', 'vmin': 0.}]),
Panel(array_inh_exc, data_labels=["inh->exc"], line_properties=[
{'xticks': True, 'yticks': True, 'cmap': 'Greys', 'vmin': 0.}]),
Panel(array_inh_inh, data_labels=["inh->inh"], line_properties=[
{'xticks': True, 'yticks': True, 'cmap': 'Greys', 'vmin': 0.}]),
).save(filename)
# === Finished with simulator ============================================
sim.end()
inh_cells.label = "Inhibitory cells"
if benchmark == "COBA":
ext_stim = Population(20,
SpikeSourcePoisson(rate=rate, duration=stim_dur),
label="expoisson")
rconn = 0.01
ext_conn = FixedProbabilityConnector(rconn)
ext_syn = StaticSynapse(weight=0.1)
print "%s Initialising membrane potential to random values..." % node_id
rng = NumpyRNG(seed=rngseed, parallel_safe=parallel_safe)
uniformDistr = RandomDistribution('uniform', [v_reset, v_thresh], rng=rng)
all_cells.initialize(v=uniformDistr)
print "%s Connecting populations..." % node_id
progress_bar = ProgressBar(60, '=')
connector = FixedProbabilityConnector(pconn, rng=rng, callback=progress_bar)
exc_syn = StaticSynapse(weight=w_exc, delay=delay)
inh_syn = StaticSynapse(weight=w_inh, delay=delay)
connections = {}
connections['exc'] = Projection(exc_cells,
all_cells,
connector,
exc_syn,
receptor_type='excitatory')
connections['inh'] = Projection(inh_cells,
all_cells,
connector,
inh_syn,
receptor_type='inhibitory')
from pylab import *
from pyNN.utility import Timer, init_logging, ProgressBar
import os
simulator_name = sys.argv[1]
exec("from pyNN.%s import *" % simulator_name)
test_cases = [int(x) for x in sys.argv[2:]]
from pyNN.recording import files
from pyNN.space import *
timer = Timer()
progress_bar = ProgressBar(mode='fixed', width=20)
init_logging("connectors_benchmark_%s.log" % simulator_name, debug=True)
def draw_rf(cell, positions, connections, color='k'):
idx = numpy.where(connections[:, 1] == cell)[0]
sources = connections[idx, 0]
for src in sources:
plot([positions[cell, 1], positions[src, 1]],
[positions[cell, 2], positions[src, 2]],
c=color)
def distances(pos_1, pos_2, N):
dx = abs(pos_1[:, 0] - pos_2[:, 0])
dy = abs(pos_1[:, 1] - pos_2[:, 1])
dx = numpy.minimum(dx, N - dx)
dy = numpy.minimum(dy, N - dy)
return sqrt(dx * dx + dy * dy)
rate=rate,
duration=stim_dur),
label="expoisson")
if options.protocol == "1":
t_stim2 = 1000.
ext_stim2 = sim.Population(stim_population,
sim.SpikeSourcePoisson(start=t_stim2,
rate=rate,
duration=stim_dur),
label="expoisson")
rconn = 0.01 / stim_population #so that stim_population*rconn = 1 #used to be 0.01
ext_conn = sim.FixedProbabilityConnector(rconn)
ext_syn = sim.StaticSynapse(weight=0.1)
print("%s Connecting populations..." % node_id)
progress_bar = ProgressBar(width=20)
if options.use_csa:
connector = sim.CSAConnector(csa.cset(csa.random(pconn)))
else:
connector = sim.FixedProbabilityConnector(pconn,
rng=rng,
callback=progress_bar)
exc_syn = sim.StaticSynapse(weight=w_exc, delay=delay)
inh_syn = sim.StaticSynapse(weight=w_inh, delay=delay)
connections = {}
if options.use_views or options.use_assembly:
connections['exc'] = sim.Projection(exc_cells,
all_cells,
connector,
exc_syn,
def runNetwork(Be,
Bi,
nn_stim,
show_gui=True,
dt = defaultParams.dt,
N_rec_v = 5,
save=False,
simtime = defaultParams.Tpost+defaultParams.Tstim+defaultParams.Tblank+defaultParams.Ttrans,
extra = {},
kernelseed = 123):
exec("from pyNN.%s import *" % simulator_name) in globals()
timer = Timer()
rec_conn={'EtoE':1, 'EtoI':1, 'ItoE':1, 'ItoI':1}
print('####################')
print('### (Be, Bi, nn_stim): ', Be, Bi, nn_stim)
print('####################')
Bee, Bei = Be, Be
Bie, Bii = Bi, Bi
N = defaultParams.N
NE = defaultParams.NE
NI = defaultParams.NI
print('\n # -----> Num cells: %s, size of pert. inh: %s; base rate %s; pert rate %s'% (N, nn_stim, defaultParams.r_bkg, defaultParams.r_stim))
r_extra = np.zeros(N)
r_extra[NE:NE+nn_stim] = defaultParams.r_stim
rr1 = defaultParams.r_bkg*np.random.uniform(.75,1.25, N)
rr2 = rr1 + r_extra
rank = setup(timestep=dt, max_delay=defaultParams.delay_default, reference='ISN', save_format='hdf5', **extra)
print("rank =", rank)
nump = num_processes()
print("num_processes =", nump)
import socket
host_name = socket.gethostname()
print("Host #%d is on %s" % (rank+1, host_name))
if 'threads' in extra:
print("%d Initialising the simulator with %d threads..." % (rank, extra['threads']))
else:
print("%d Initialising the simulator with single thread..." % rank)
timer.start() # start timer on construction
print("%d Setting up random number generator using seed %s" % (rank, kernelseed))
ks = open('kernelseed','w')
ks.write('%i'%kernelseed)
ks.close()
rng = NumpyRNG(kernelseed, parallel_safe=True)
nesp = defaultParams.neuron_params_default
cell_parameters = {
'cm': nesp['C_m']/1000, # Capacitance of the membrane in nF
'tau_refrac': nesp['t_ref'], # Duration of refractory period in ms.
'v_spike': 0.0 , # Spike detection threshold in mV. https://github.com/nest/nest-simulator/blob/master/models/aeif_cond_alpha.cpp
'v_reset': nesp['V_reset'], # Reset value for V_m after a spike. In mV.
'v_rest': nesp['E_L'], # Resting membrane potential (Leak reversal potential) in mV.
'tau_m': nesp['C_m']/nesp['g_L'], # Membrane time constant in ms = cm/tau_m*1000.0, C_m/g_L
'i_offset': nesp['I_e']/1000, # Offset current in nA
'a': 0, # Subthreshold adaptation conductance in nS.
'b': 0, # Spike-triggered adaptation in nA
'delta_T': 2 , # Slope factor in mV. See https://github.com/nest/nest-simulator/blob/master/models/aeif_cond_alpha.cpp
'tau_w': 144.0, # Adaptation time constant in ms. See https://github.com/nest/nest-simulator/blob/master/models/aeif_cond_alpha.cpp
'v_thresh': nesp['V_th'], # Spike initiation threshold in mV
'e_rev_E': nesp['E_ex'], # Excitatory reversal potential in mV.
'tau_syn_E': nesp['tau_syn_ex'], # Rise time of excitatory synaptic conductance in ms (alpha function).
'e_rev_I': nesp['E_in'], # Inhibitory reversal potential in mV.
'tau_syn_I': nesp['tau_syn_in'], # Rise time of the inhibitory synaptic conductance in ms (alpha function).
}
print("%d Creating population with %d neurons." % (rank, N))
celltype = EIF_cond_alpha_isfa_ista(**cell_parameters)
celltype.default_initial_values['v'] = cell_parameters['v_rest'] # Setting default init v, useful for NML2 export
layer_volume = Cuboid(1000,100,1000)
layer_structure = RandomStructure(layer_volume, origin=(0,0,0))
layer_structure_input = RandomStructure(layer_volume, origin=(0,-150,0))
default_cell_radius = 15
stim_cell_radius = 10
#EI_pop = Population(N, celltype, structure=layer_structure, label="EI")
E_pop = Population(NE, celltype, structure=layer_structure, label='E_pop')
E_pop.annotate(color='1 0 0')
E_pop.annotate(radius=default_cell_radius)
E_pop.annotate(type='E') # temp indicator to use for connection arrowhead
#print("%d Creating pop %s." % (rank, E_pop))
I_pop = Population(NI, celltype, structure=layer_structure, label='I_pop')
I_pop.annotate(color='0 0 .9')
I_pop.annotate(radius=default_cell_radius)
I_pop.annotate(type='I') # temp indicator to use for connection arrowhead
#print("%d Creating pop %s." % (rank, I_pop))
I_pert_pop = PopulationView(I_pop, np.array(range(0,nn_stim)),label='I_pert_pop')
I_nonpert_pop = PopulationView(I_pop, np.array(range(nn_stim,NI)),label='I_nonpert_pop')
p_rate = defaultParams.r_bkg
print("%d Creating excitatory Poisson generator with rate %g spikes/s." % (rank, p_rate))
source_typeA_E = SpikeSourcePoisson(rate=p_rate, start=0,duration=defaultParams.Ttrans+defaultParams.Tblank+defaultParams.Tstim+defaultParams.Tpost)
expoissonA_E = Population(NE, source_typeA_E, structure=layer_structure_input, label="stim_E")
print("%d Creating excitatory Poisson generator with rate %g spikes/s." % (rank, p_rate))
source_typeA_I = SpikeSourcePoisson(rate=p_rate, start=0,duration=defaultParams.Ttrans+defaultParams.Tblank)
expoissonA_I = Population(NI, source_typeA_I, structure=layer_structure_input, label="pre_pert_stim_I")
print("%d Creating excitatory Poisson generator with rate %g spikes/s." % (rank, p_rate))
source_typeB = SpikeSourcePoisson(rate=p_rate, start=defaultParams.Ttrans+defaultParams.Tblank,duration=defaultParams.Tstim+defaultParams.Tpost)
#expoissonB_E = Population(NE, source_typeB, label="non_pert_stim_E")
expoissonB_I = Population(len(I_nonpert_pop), source_typeB, structure=layer_structure_input, label="non_pert_stim_I")
p_rate = defaultParams.r_bkg+defaultParams.r_stim
print("%d Creating excitatory Poisson generator with rate %g spikes/s." % (rank, p_rate))
source_typeC = SpikeSourcePoisson(rate=p_rate, start=defaultParams.Ttrans+defaultParams.Tblank, duration=defaultParams.Tstim)
expoissonC = Population(nn_stim, source_typeC, structure=layer_structure_input, label="pert_stim")
p_rate = defaultParams.r_bkg
print("%d Creating excitatory Poisson generator with rate %g spikes/s." % (rank, p_rate))
source_typeD = SpikeSourcePoisson(rate=p_rate, start=defaultParams.Ttrans+defaultParams.Tblank+defaultParams.Tstim, duration=defaultParams.Tpost)
expoissonD = Population(nn_stim, source_typeD, structure=layer_structure_input, label="pert_poststim")
for p in [expoissonA_E,expoissonA_I,expoissonB_I,expoissonC,expoissonD]:
p.annotate(color='0.8 0.8 0.8')
p.annotate(radius=stim_cell_radius)
progress_bar = ProgressBar(width=20)
connector_E = FixedProbabilityConnector(0.15, rng=rng, callback=progress_bar)
connector_I = FixedProbabilityConnector(1, rng=rng, callback=progress_bar)
EE_syn = StaticSynapse(weight=0.001*Bee, delay=defaultParams.delay_default)
EI_syn = StaticSynapse(weight=0.001*Bei, delay=defaultParams.delay_default)
II_syn = StaticSynapse(weight=0.001*Bii, delay=defaultParams.delay_default)
IE_syn = StaticSynapse(weight=0.001*Bie, delay=defaultParams.delay_default)
#I_syn = StaticSynapse(weight=JI, delay=delay)
ext_Connector = OneToOneConnector(callback=progress_bar)
ext_syn_bkg = StaticSynapse(weight=0.001*defaultParams.Be_bkg, delay=defaultParams.delay_default)
ext_syn_stim = StaticSynapse(weight=0.001*defaultParams.Be_stim, delay=defaultParams.delay_default)
E_to_E = Projection(E_pop, E_pop, connector_E, EE_syn, receptor_type="excitatory")
print("E --> E\t\t", len(E_to_E), "connections")
E_to_I = Projection(E_pop, I_pop, connector_E, EI_syn, receptor_type="excitatory")
print("E --> I\t\t", len(E_to_I), "connections")
I_to_I = Projection(I_pop, I_pop, connector_I, II_syn, receptor_type="inhibitory")
print("I --> I\t\t", len(I_to_I), "connections")
I_to_E = Projection(I_pop, E_pop, connector_I, IE_syn, receptor_type="inhibitory")
print("I --> E\t\t", len(I_to_E), "connections")
input_A_E = Projection(expoissonA_E, E_pop, ext_Connector, ext_syn_bkg, receptor_type="excitatory")
print("input --> %s cells pre pert\t"%len(E_pop), len(input_A_E), "connections")
input_A_I = Projection(expoissonA_I, I_pop, ext_Connector, ext_syn_bkg, receptor_type="excitatory")
print("input --> %s cells pre pert\t"%len(I_pop), len(input_A_I), "connections")
##input_B_E = Projection(expoissonB_E, E_pop, ext_Connector, ext_syn_bkg, receptor_type="excitatory")
##print("input --> %s cells post pert\t"%len(E_pop), len(input_B_E), "connections")
input_B_I = Projection(expoissonB_I, I_nonpert_pop, ext_Connector, ext_syn_bkg, receptor_type="excitatory")
print("input --> %s cells post pert\t"%len(I_nonpert_pop), len(input_B_I), "connections")
input_C = Projection(expoissonC, I_pert_pop, ext_Connector, ext_syn_stim, receptor_type="excitatory")
print("input --> %s cells pre pert\t"%len(I_pert_pop), len(input_C), "connections")
input_D = Projection(expoissonD, I_pert_pop, ext_Connector, ext_syn_stim, receptor_type="excitatory")
print("input --> %s cells pre pert\t"%len(I_pert_pop), len(input_D), "connections")
# Can't be used for connections etc. as NeuroML export not (yet) supported
EI_pop = Assembly(E_pop, I_pop, label='EI')
# Record spikes
print("%d Setting up recording in excitatory population." % rank)
EI_pop.record('spikes')
if N_rec_v>0:
EI_pop[0:min(N,N_rec_v)].record('v')
# read out time used for building
buildCPUTime = timer.elapsedTime()
# === Run simulation ===========================================================
# run, measure computer time
timer.start() # start timer on construction
print("%d Running simulation in %s for %g ms (dt=%sms)." % (rank, simulator_name, simtime, dt))
run(simtime)
print("Done")
simCPUTime = timer.elapsedTime()
# write data to file
if save and not simulator_name=='neuroml':
for pop in [EI_pop]:
filename="ISN-%s-%s-%i.gdf"%(simulator_name, pop.label, rank)
ff = open(filename, 'w')
spikes = pop.get_data('spikes', gather=False)
spiketrains = spikes.segments[0].spiketrains
print('Saving data recorded for %i spiketrains in pop %s, indices: %s, ids: %s to %s'% \
(len(spiketrains),
pop.label,
[s.annotations['source_index'] for s in spiketrains],
[s.annotations['source_id'] for s in spiketrains],
filename))
for spiketrain_i in range(len(spiketrains)):
spiketrain = spiketrains[spiketrain_i]
source_id = spiketrain.annotations['source_id']
source_index = spiketrain.annotations['source_index']
#print("Writing spike data for cell %s[%s] (gid: %i): %i spikes: [%s,...,%s] "%(pop.label,source_index, source_id, len(spiketrain),spiketrain[0],spiketrain[-1]))
for t in spiketrain:
ff.write('%s\t%i\n'%(t.magnitude,spiketrain_i))
ff.close()
vs = pop.get_data('v', gather=False)
for segment in vs.segments:
for i in range(len(segment.analogsignals[0].transpose())):
filename="ISN-%s-%s-cell%i.dat"%(simulator_name, pop.label, i)
print('Saving cell %i in %s to %s'%(i,pop.label,filename))
vm = segment.analogsignals[0].transpose()[i]
tt = np.array([t*dt/1000. for t in range(len(vm))])
times_vm = np.array([tt, vm/1000.]).transpose()
np.savetxt(filename, times_vm , delimiter = '\t', fmt='%s')
spike_data = {}
spike_data['senders'] = []
spike_data['times'] = []
index_offset = 1
for pop in [EI_pop]:
if rank == 0:
spikes = pop.get_data('spikes', gather=False)
#print(spikes.segments[0].all_data)
num_rec = len(spikes.segments[0].spiketrains)
print("Extracting spike info (%i) for %i cells in %s"%(num_rec,pop.size,pop.label))
#assert(num_rec==len(spikes.segments[0].spiketrains))
for i in range(num_rec):
ss = spikes.segments[0].spiketrains[i]
for s in ss:
index = i+index_offset
#print("Adding spike at %s in %s[%i] (cell %i)"%(s,pop.label,i,index))
spike_data['senders'].append(index)
spike_data['times'].append(s)
index_offset+=pop.size
print("Build time : %g s" % buildCPUTime)
print("Simulation time : %g s" % simCPUTime)
# === Clean up and quit ========================================================
end()
def runBrunelNetwork(g=5.,
eta=2.,
dt=0.1,
simtime=1000.0,
delay=1.5,
epsilon=0.1,
order=2500,
N_rec=50,
N_rec_v=2,
save=False,
simulator_name='nest',
jnml_simulator=None,
extra={}):
exec("from pyNN.%s import *" % simulator_name) in globals()
timer = Timer()
# === Define parameters ========================================================
downscale = 1 # scale number of neurons down by this factor
# scale synaptic weights up by this factor to
# obtain similar dynamics independent of size
order = order # determines size of network:
# 4*order excitatory neurons
# 1*order inhibitory neurons
Nrec = N_rec # number of neurons to record from, per population
epsilon = epsilon # connectivity: proportion of neurons each neuron projects to
# Parameters determining model dynamics, cf Brunel (2000), Figs 7, 8 and Table 1
# here: Case C, asynchronous irregular firing, ~35 Hz
eta = eta # rel rate of external input
g = g # rel strength of inhibitory synapses
J = 0.1 # synaptic weight [mV]
delay = delay # synaptic delay, all connections [ms]
# single neuron parameters
tauMem = 20.0 # neuron membrane time constant [ms]
tauSyn = 0.1 # synaptic time constant [ms]
tauRef = 2.0 # refractory time [ms]
U0 = 0.0 # resting potential [mV]
theta = 20.0 # threshold
# simulation-related parameters
simtime = simtime # simulation time [ms]
dt = dt # simulation step length [ms]
# seed for random generator used when building connections
connectseed = 12345789
use_RandomArray = True # use Python rng rather than NEST rng
# seed for random generator(s) used during simulation
kernelseed = 43210987
# === Calculate derived parameters =============================================
# scaling: compute effective order and synaptic strength
order_eff = int(float(order) / downscale)
J_eff = J * downscale
# compute neuron numbers
NE = int(4 * order_eff) # number of excitatory neurons
NI = int(1 * order_eff) # number of inhibitory neurons
N = NI + NE # total number of neurons
# compute synapse numbers
CE = int(epsilon * NE) # number of excitatory synapses on neuron
CI = int(epsilon * NI) # number of inhibitory synapses on neuron
C = CE + CI # total number of internal synapses per n.
Cext = CE # number of external synapses on neuron
# synaptic weights, scaled for alpha functions, such that
# for constant membrane potential, charge J would be deposited
fudge = 0.00041363506632638 # ensures dV = J at V=0
# excitatory weight: JE = J_eff / tauSyn * fudge
JE = (J_eff / tauSyn) * fudge
# inhibitory weight: JI = - g * JE
JI = -g * JE
# threshold, external, and Poisson generator rates:
nu_thresh = theta / (J_eff * CE * tauMem)
nu_ext = eta * nu_thresh # external rate per synapse
p_rate = 1000 * nu_ext * Cext # external input rate per neuron (Hz)
# number of synapses---just so we know
Nsyn = (
C + 1
) * N + 2 * Nrec # number of neurons * (internal synapses + 1 synapse from PoissonGenerator) + 2synapses" to spike detectors
# put cell parameters into a dict
cell_params = {
'tau_m': tauMem,
'tau_syn_E': tauSyn,
'tau_syn_I': tauSyn,
'tau_refrac': tauRef,
'v_rest': U0,
'v_reset': U0,
'v_thresh': theta,
'cm': 0.001
} # (nF)
# === Build the network ========================================================
# clear all existing network elements and set resolution and limits on delays.
# For NEST, limits must be set BEFORE connecting any elements
#extra = {'threads' : 2}
rank = setup(timestep=dt, max_delay=delay, **extra)
print("rank =", rank)
np = num_processes()
print("np =", np)
import socket
host_name = socket.gethostname()
print("Host #%d is on %s" % (rank + 1, host_name))
if 'threads' in extra:
print("%d Initialising the simulator with %d threads..." %
(rank, extra['threads']))
else:
print("%d Initialising the simulator with single thread..." % rank)
# Small function to display information only on node 1
def nprint(s):
if rank == 0:
print(s)
timer.start() # start timer on construction
print("%d Setting up random number generator" % rank)
rng = NumpyRNG(kernelseed, parallel_safe=True)
print("%d Creating excitatory population with %d neurons." % (rank, NE))
celltype = IF_curr_alpha(**cell_params)
celltype.default_initial_values[
'v'] = U0 # Setting default init v, useful for NML2 export
E_net = Population(NE, celltype, label="E_net")
print("%d Creating inhibitory population with %d neurons." % (rank, NI))
I_net = Population(NI, celltype, label="I_net")
print(
"%d Initialising membrane potential to random values between %g mV and %g mV."
% (rank, U0, theta))
uniformDistr = RandomDistribution('uniform', low=U0, high=theta, rng=rng)
E_net.initialize(v=uniformDistr)
I_net.initialize(v=uniformDistr)
print("%d Creating excitatory Poisson generator with rate %g spikes/s." %
(rank, p_rate))
source_type = SpikeSourcePoisson(rate=p_rate)
expoisson = Population(NE, source_type, label="expoisson")
print("%d Creating inhibitory Poisson generator with the same rate." %
rank)
inpoisson = Population(NI, source_type, label="inpoisson")
# Record spikes
print("%d Setting up recording in excitatory population." % rank)
E_net.record('spikes')
if N_rec_v > 0:
E_net[0:min(NE, N_rec_v)].record('v')
print("%d Setting up recording in inhibitory population." % rank)
I_net.record('spikes')
if N_rec_v > 0:
I_net[0:min(NI, N_rec_v)].record('v')
progress_bar = ProgressBar(width=20)
connector = FixedProbabilityConnector(epsilon,
rng=rng,
callback=progress_bar)
E_syn = StaticSynapse(weight=JE, delay=delay)
I_syn = StaticSynapse(weight=JI, delay=delay)
ext_Connector = OneToOneConnector(callback=progress_bar)
ext_syn = StaticSynapse(weight=JE, delay=dt)
print(
"%d Connecting excitatory population with connection probability %g, weight %g nA and delay %g ms."
% (rank, epsilon, JE, delay))
E_to_E = Projection(E_net,
E_net,
connector,
E_syn,
receptor_type="excitatory")
print("E --> E\t\t", len(E_to_E), "connections")
I_to_E = Projection(I_net,
E_net,
connector,
I_syn,
receptor_type="inhibitory")
print("I --> E\t\t", len(I_to_E), "connections")
input_to_E = Projection(expoisson,
E_net,
ext_Connector,
ext_syn,
receptor_type="excitatory")
print("input --> E\t", len(input_to_E), "connections")
print(
"%d Connecting inhibitory population with connection probability %g, weight %g nA and delay %g ms."
% (rank, epsilon, JI, delay))
E_to_I = Projection(E_net,
I_net,
connector,
E_syn,
receptor_type="excitatory")
print("E --> I\t\t", len(E_to_I), "connections")
I_to_I = Projection(I_net,
I_net,
connector,
I_syn,
receptor_type="inhibitory")
print("I --> I\t\t", len(I_to_I), "connections")
input_to_I = Projection(inpoisson,
I_net,
ext_Connector,
ext_syn,
receptor_type="excitatory")
print("input --> I\t", len(input_to_I), "connections")
# read out time used for building
buildCPUTime = timer.elapsedTime()
# === Run simulation ===========================================================
# run, measure computer time
timer.start() # start timer on construction
print("%d Running simulation for %g ms (dt=%sms)." % (rank, simtime, dt))
run(simtime)
print("Done")
simCPUTime = timer.elapsedTime()
# write data to file
#print("%d Writing data to file." % rank)
#(E_net + I_net).write_data("Results/brunel_np%d_%s.pkl" % (np, simulator_name))
if save and not simulator_name == 'neuroml':
for pop in [E_net, I_net]:
io = PyNNTextIO(filename="brunel-PyNN-%s-%s-%i.gdf" %
(simulator_name, pop.label, rank))
spikes = pop.get_data('spikes', gather=False)
for segment in spikes.segments:
io.write_segment(segment)
io = PyNNTextIO(filename="brunel-PyNN-%s-%s-%i.dat" %
(simulator_name, pop.label, rank))
vs = pop.get_data('v', gather=False)
for segment in vs.segments:
io.write_segment(segment)
spike_data = {}
spike_data['senders'] = []
spike_data['times'] = []
index_offset = 1
for pop in [E_net, I_net]:
if rank == 0:
spikes = pop.get_data('spikes', gather=False)
#print(spikes.segments[0].all_data)
num_rec = len(spikes.segments[0].spiketrains)
print("Extracting spike info (%i) for %i cells in %s" %
(num_rec, pop.size, pop.label))
#assert(num_rec==len(spikes.segments[0].spiketrains))
for i in range(num_rec):
ss = spikes.segments[0].spiketrains[i]
for s in ss:
index = i + index_offset
#print("Adding spike at %s in %s[%i] (cell %i)"%(s,pop.label,i,index))
spike_data['senders'].append(index)
spike_data['times'].append(s)
index_offset += pop.size
#from IPython.core.debugger import Tracer
#Tracer()()
E_rate = E_net.mean_spike_count() * 1000.0 / simtime
I_rate = I_net.mean_spike_count() * 1000.0 / simtime
# write a short report
nprint("\n--- Brunel Network Simulation ---")
nprint("Nodes : %d" % np)
nprint("Number of Neurons : %d" % N)
nprint("Number of Synapses : %d" % Nsyn)
nprint("Input firing rate : %g" % p_rate)
nprint("Excitatory weight : %g" % JE)
nprint("Inhibitory weight : %g" % JI)
nprint("Excitatory rate : %g Hz" % E_rate)
nprint("Inhibitory rate : %g Hz" % I_rate)
nprint("Build time : %g s" % buildCPUTime)
nprint("Simulation time : %g s" % simCPUTime)
# === Clean up and quit ========================================================
end()
if simulator_name == 'neuroml' and jnml_simulator:
from pyneuroml import pynml
lems_file = 'LEMS_Sim_PyNN_NeuroML2_Export.xml'
print('Going to run generated LEMS file: %s on simulator: %s' %
(lems_file, jnml_simulator))
if jnml_simulator == 'jNeuroML':
results, events = pynml.run_lems_with_jneuroml(
lems_file,
nogui=True,
load_saved_data=True,
reload_events=True)
elif jnml_simulator == 'jNeuroML_NEURON':
results, events = pynml.run_lems_with_jneuroml_neuron(
lems_file,
nogui=True,
load_saved_data=True,
reload_events=True)
spike_data['senders'] = []
spike_data['times'] = []
for k in events.keys():
values = k.split('/')
index = int(
values[1]) if values[0] == 'E_net' else NE + int(values[1])
n = len(events[k])
print(
"Loading spikes for %s (index %i): [%s, ..., %s (n=%s)] sec" %
(k, index, events[k][0] if n > 0 else '-',
events[k][-1] if n > 0 else '-', n))
for t in events[k]:
spike_data['senders'].append(index)
spike_data['times'].append(t * 1000)
#print spike_data
return spike_data
sim.setup()
celltype = sim.IF_curr_alpha(tau_m=tau, v_thresh=theta,
tau_refrac=2.0, v_reset=10.0,
v_rest=0.0, cm=tau/1.5,
tau_syn_E=tau_syn, tau_syn_I=tau_syn)
ext_stim = sim.SpikeSourcePoisson(rate=input_rate)
exc_cells = sim.Population(Ne, celltype, initial_values={'v': v_init}, label="Exc")
inh_cells = sim.Population(Ni, celltype, initial_values={'v': v_init}, label="Inh")
external = sim.Population(int(Cext), ext_stim, label="Ext")
all_cells = exc_cells + inh_cells
all_to_all = sim.AllToAllConnector(callback=ProgressBar())
random_uniform = sim.FixedProbabilityConnector(p_connect=epsilon, callback=ProgressBar())
static_ext = sim.StaticSynapse(delay=delay, weight=Jext)
static_exc = sim.StaticSynapse(delay=delay, weight=Je)
static_inh = sim.StaticSynapse(delay=delay, weight=Ji)
input_prj = sim.Projection(external, all_cells, all_to_all,
synapse_type=static_ext, receptor_type="excitatory",
label="External")
exc_prj = sim.Projection(exc_cells, all_cells, random_uniform,
synapse_type=static_exc, receptor_type="excitatory",
label="Excitation")
inh_prj = sim.Projection(inh_cells, all_cells, random_uniform,
synapse_type=static_inh, receptor_type="inhibitory",
label="Inhibition")
