def make_synth(N, pconnect, pfire, delay):
pg = ngpu.Create("poisson_generator")
ngpu.SetStatus(pg, "rate", 10000 * pfire)
neuron = ngpu.Create("iaf_psc_exp", N, 2)
ngpu.SetStatus(neuron, {"V_m_rel": 0, "V_reset_rel": 0, "Theta_rel": 20, "t_ref": 0})
ngpu.Connect(pg, neuron, {"rule": "all_to_all"}, {"weight": 20, "delay": 0.1, "receptor":0})
ngpu.Connect(neuron, neuron,
{"rule": "fixed_total_number", "total_num": int(N * N * pconnect)},
{"weight": 0, "delay": 0.1 * delay, "receptor":0})
def make_vogels(N):
NE = N // 5 * 4
NI = N // 5 * 1
N = NE + NI
Wex = 0.4 * 16000000 / N / N
Win = 5.1 * 16000000 / N / N
neuron = ngpu.Create("iaf_psc_exp_g", N, 2)
E = neuron[0:NE] # inhibitory neurons
I = neuron[NE:N]
ngpu.SetStatus(neuron, {"V_m_rel": -60, "V_reset_rel": -60, "Theta_rel": -50, "t_ref": 5, "G_ex": 0, "G_in": 0})
ngpu.Connect(E, E,
{"rule": "fixed_total_number", "total_num": NE * NE // 50},
{"weight": Wex, "delay": 0.8, "receptor":0})
ngpu.Connect(E, I,
{"rule": "fixed_total_number", "total_num": NE * NI // 50},
{"weight": Wex, "delay": 0.8, "receptor":0})
ngpu.Connect(I, E,
{"rule": "fixed_total_number", "total_num": NI * NE // 50},
{"weight": Win, "delay": 0.8, "receptor":1})
ngpu.Connect(I, I,
{"rule": "fixed_total_number", "total_num": NI * NI // 50},
{"weight": Win, "delay": 0.8, "receptor":1})
def make_brunel(N):
NP = N // 10 * 5
NE = N // 10 * 4
NI = N // 10 * 1
N = NP + NE + NI
Wex = 0.1 * 20000 / N
Win = -0.5 * 20000 / N
pg = ngpu.Create("poisson_generator")
ngpu.SetStatus(pg, "rate", 20)
neuron = ngpu.Create("iaf_psc_exp", N, 2)
P = neuron[0:NP] # excitatory neurons
E = neuron[NP:NP+NE] # inhibitory neurons
I = neuron[NP+NE:N]
ngpu.SetStatus(P, {"V_m_rel": 0, "V_reset_rel": 0, "Theta_rel": 20, "t_ref": 0})
ngpu.SetStatus(E, {"V_m_rel": 0, "V_reset_rel": 0, "Theta_rel": 20, "t_ref": 2})
ngpu.SetStatus(I, {"V_m_rel": 0, "V_reset_rel": 0, "Theta_rel": 20, "t_ref": 2})
ngpu.Connect(pg, P, {"rule": "all_to_all"}, {"weight": 20, "delay": 0.1, "receptor":0})
ngpu.Connect(P, E,
{"rule": "fixed_total_number", "total_num": NP * NE // 10},
{"weight": Wex, "delay": 1.5, "receptor":0})
ngpu.Connect(P, I,
{"rule": "fixed_total_number", "total_num": NP * NI // 10},
{"weight": Wex, "delay": 1.5, "receptor":0})
ngpu.Connect(E, E,
{"rule": "fixed_total_number", "total_num": NE * NE // 10},
{"weight": Wex, "delay": 1.5, "receptor":0})
ngpu.Connect(E, I,
{"rule": "fixed_total_number", "total_num": NE * NI // 10},
{"weight": Wex, "delay": 1.5, "receptor":0})
ngpu.Connect(I, E,
{"rule": "fixed_total_number", "total_num": NI * NE // 10},
{"weight": Win, "delay": 1.5, "receptor":1})
ngpu.Connect(I, I,
{"rule": "fixed_total_number", "total_num": NI * NI // 10},
{"weight": Win, "delay": 1.5, "receptor":1})
def CreateNeuronImage(nodes, n_port):
dt = nest.GetKernelStatus("resolution")
n_nodes = len(nodes)
node_image = neurongpu.Create("ext_neuron", n_nodes, n_port)
for i in range(n_nodes):
nest_node_id = nodes[i].global_id
i_port = AddNode(nest_node_id, node_image[i])
syn_dict = {"weight": 1.0, "delay": dt, "receptor_type": i_port}
nest.Connect(nodes[i], ngpu_nest_interface, "one_to_one", syn_dict)
return node_image
def __create_thalamic_stim_input(self):
""" Creates the thalamic neuronal population if specified in
``stim_dict``.
Thalamic neurons are of type ``parrot_neuron`` and receive input from a
Poisson generator.
Note that the number of thalamic neurons is not scaled with
``N_scaling``.
"""
if self.Rank == 0:
print('Creating thalamic input for external stimulation.')
self.thalamic_population = ngpu.Create(
'parrot_neuron', n=self.stim_dict['num_th_neurons'])
self.poisson_th = ngpu.Create('poisson_generator')
self.poisson_th.set(rate=self.stim_dict['th_rate'],
start=self.stim_dict['th_start'],
stop=(self.stim_dict['th_start'] +
self.stim_dict['th_duration']))
def __create_poisson_bg_input(self):
""" Creates the Poisson generators for ongoing background input if
specified in ``network_params.py``.
If ``poisson_input`` is ``False``, DC input is applied for compensation
in ``create_neuronal_populations()``.
"""
if self.Rank == 0:
print('Creating Poisson generators for background input.')
self.poisson_bg_input = ngpu.Create('poisson_generator', self.num_pops)
rate_list = self.net_dict['bg_rate'] * self.ext_indegrees
for i_pop in range(self.num_pops):
ngpu.SetStatus([self.poisson_bg_input[i_pop]], "rate",
rate_list[i_pop])
syn_group_ex = ngpu.CreateSynGroup \
("stdp", {"tau_plus":tau_plus, "tau_minus":tau_minus, \
"lambda":lambd_ex, "alpha":alpha, "mu_plus":mu_plus, \
"mu_minus":mu_minus, "Wmax":Wmax})
syn_group_in = ngpu.CreateSynGroup \
("stdp", {"tau_plus":tau_plus, "tau_minus":tau_minus, \
"lambda":lambd_in, "alpha":alpha, "mu_plus":mu_plus, \
"mu_minus":mu_minus, "Wmax":Wmax})
# poisson generator parameters
poiss_rate = 20000.0 # poisson signal rate in Hz
poiss_weight = 0.37 * fact
poiss_delay = 1.0 # poisson signal delay in ms
# create poisson generator
pg = ngpu.Create("poisson_generator")
ngpu.SetStatus(pg, "rate", poiss_rate)
# Create n_neurons neurons with n_receptor receptor ports
neuron = ngpu.Create("izhikevich_psc_exp_5s", n_neurons, n_receptors)
exc_neuron = neuron[0:NE] # excitatory neurons
inh_neuron = neuron[NE:n_neurons] # inhibitory neurons
# receptor parameters
delay = 2.0
# Excitatory connections
# connect excitatory neurons to port 0 of all neurons
# normally distributed delays, weight Wex and CE connections per neuron
exc_conn_dict = {"rule": "fixed_indegree", "indegree": CE}
import sys
import neurongpu as ngpu
import numpy as np
tolerance = 0.0005
neuron = ngpu.Create('aeif_psc_alpha', 1, 2)
ngpu.SetStatus(neuron, {"V_peak": 0.0, "a": 4.0, "b":80.5, "E_L":-70.6, \
"g_L":300.0, "tau_syn":[40.0, 20.0]})
spike = ngpu.Create("spike_generator")
spike_times = [10.0, 400.0]
n_spikes = 2
# set spike times and height
ngpu.SetStatus(spike, {"spike_times": spike_times})
delay = [1.0, 100.0]
weight = [1.0, -2.0]
conn_spec = {"rule": "all_to_all"}
syn_spec_ex = {'receptor': 0, 'weight': weight[0], 'delay': delay[0]}
syn_spec_in = {'receptor': 1, 'weight': weight[1], 'delay': delay[1]}
ngpu.Connect(spike, neuron, conn_spec, syn_spec_ex)
ngpu.Connect(spike, neuron, conn_spec, syn_spec_in)
record = ngpu.CreateRecord("", ["V_m"], [neuron[0]], [0])
#voltmeter = nest.Create('voltmeter')
#nest.Connect(voltmeter, neuron)
ngpu.Simulate(800.0)
data_list = ngpu.GetRecordData(record)
import neurongpu as ngpu
import numpy as np
neuron = ngpu.Create("aeif_cond_beta", 100000)
ngpu.SetStatus(
neuron, {
"V_m": {
"distribution": "normal_clipped",
"mu": -70.0,
"low": -90.0,
"high": -50.0,
"sigma": 5.0
}
})
l = ngpu.GetStatus(neuron, "V_m")
d = []
for elem in l:
d.append(elem[0])
print(len(d))
import matplotlib.pyplot as plt
# An "interface" to matplotlib.axes.Axes.hist() method
n, bins, patches = plt.hist(d,
bins='auto',
color='#0504aa',
alpha=0.7,
rwidth=0.85)
plt.grid(axis='y', alpha=0.75)
plt.xlabel('Value')
NI = 1 * order # number of inhibitory neurons
n_neurons = NE + NI # number of neurons in total
CE = 800 # number of excitatory synapses per neuron
CI = CE//4 # number of inhibitory synapses per neuron
Wex = 0.05
Win = 0.35
# poisson generator parameters
poiss_rate = 20000.0 # poisson signal rate in Hz
poiss_weight = 0.37
poiss_delay = 0.2 # poisson signal delay in ms
# create poisson generator
pg = ngpu.Create("poisson_generator")
ngpu.SetStatus(pg, "rate", poiss_rate)
pg_list = pg.ToList()
# Create n_neurons neurons with n_receptor receptor ports
neuron = ngpu.Create("user_m1", n_neurons, n_receptors)
exc_neuron = neuron[0:NE] # excitatory neurons
inh_neuron = neuron[NE:n_neurons] # inhibitory neurons
neuron_list = neuron.ToList()
exc_neuron_list = exc_neuron.ToList()
inh_neuron_list = inh_neuron.ToList()
# receptor parameters
E_rev = [0.0, -85.0]
tau_decay = [1.0, 1.0]
tau_rise = [1.0, 1.0]
import sys
import neurongpu as ngpu
import numpy as np
tolerance = 0.0005
neuron = ngpu.Create('izhikevich', 1)
#ngpu.SetStatus(neuron, {"tau_syn": 1.0e-6})
spike = ngpu.Create("spike_generator")
spike_times = [10.0, 40.0]
n_spikes = 2
# set spike times and height
ngpu.SetStatus(spike, {"spike_times": spike_times})
delay = [1.0, 10.0]
weight = [1.0, -2.0]
conn_spec = {"rule": "all_to_all"}
syn_spec_ex = {'weight': weight[0], 'delay': delay[0]}
syn_spec_in = {'weight': weight[1], 'delay': delay[1]}
ngpu.Connect(spike, neuron, conn_spec, syn_spec_ex)
ngpu.Connect(spike, neuron, conn_spec, syn_spec_in)
record = ngpu.CreateRecord("", ["V_m"], [neuron[0]], [0])
#voltmeter = nest.Create('voltmeter')
#nest.Connect(voltmeter, neuron)
ngpu.Simulate(80.0)
data_list = ngpu.GetRecordData(record)
t = [row[0] for row in data_list]
V_m = [row[1] for row in data_list]
import sys
import neurongpu as ngpu
import numpy as np
tolerance = 0.0005
neuron = ngpu.Create('aeif_cond_alpha', 1, 3)
ngpu.SetStatus(neuron, {"V_peak": 0.0, "a": 4.0, "b":80.5, "E_L":-70.6,
"g_L":300.0, 'E_rev':[20.0, 0.0, -85.0], \
'tau_syn':[40.0, 20.0, 30.0]})
spike = ngpu.Create("spike_generator")
spike_times = [10.0, 400.0]
n_spikes = 2
# set spike times and heights
ngpu.SetStatus(spike, {"spike_times": spike_times})
delay = [1.0, 100.0, 130.0]
weight = [0.1, 0.2, 0.5]
conn_spec={"rule": "all_to_all"}
for syn in range(3):
syn_spec={'receptor': syn, 'weight': weight[syn], 'delay': delay[syn]}
ngpu.Connect(spike, neuron, conn_spec, syn_spec)
record = ngpu.CreateRecord("", ["V_m"], [neuron[0]], [0])
#voltmeter = nest.Create('voltmeter')
#nest.Connect(voltmeter, neuron)
ngpu.Simulate(800.0)
data_list = ngpu.GetRecordData(record)
t=[row[0] for row in data_list]
V_m=[row[1] for row in data_list]
import sys
import neurongpu as ngpu
import numpy as np
tolerance = 0.0005
neuron = ngpu.Create('izhikevich_cond_beta', 1, 3)
#ngpu.SetStatus(neuron, {'u':-0.2*70.0, 'V_m':-70.0})
ngpu.SetStatus(neuron, {'E_rev':[20.0, 0.0, -85.0], \
'tau_decay':[40.0, 20.0, 30.0], \
'tau_rise':[20.0, 10.0, 5.0]})
spike = ngpu.Create("spike_generator")
spike_times = [10.0, 400.0]
n_spikes = 2
# set spike times and heights
ngpu.SetStatus(spike, {"spike_times": spike_times})
delay = [1.0, 100.0, 130.0]
fact = 0.00225
weight = [0.1 * fact, 0.2 * fact, 0.5 * fact]
conn_spec = {"rule": "all_to_all"}
for syn in range(3):
syn_spec = {'receptor': syn, 'weight': weight[syn], 'delay': delay[syn]}
ngpu.Connect(spike, neuron, conn_spec, syn_spec)
record = ngpu.CreateRecord("", ["V_m"], [neuron[0]], [0])
#voltmeter = nest.Create('voltmeter')
#nest.Connect(voltmeter, neuron)
ngpu.Simulate(800.0)
data_list = ngpu.GetRecordData(record)
if (Dt >= 0):
fact = Wplus * math.exp(-Dt / tau_plus)
w1 = w + fact * math.pow(1.0 - w / Wmax, mu_plus)
if w1 > Wmax:
w1 = Wmax
else:
fact = -alpha * Wplus * math.exp(Dt / tau_minus)
w1 = w + fact * math.pow(w / Wmax, mu_minus)
if w1 < 0.0:
w1 = 0.0
return w1
# postsynaptic and presynaptic neurons
neuron_post = ngpu.Create("parrot_neuron")
ngpu.ActivateRecSpikeTimes(neuron_post, 20)
neuron_pre = ngpu.Create("parrot_neuron")
ngpu.ActivateRecSpikeTimes(neuron_pre, 20)
#spike generators
sg_post = ngpu.Create("spike_generator")
sg_pre = ngpu.Create("spike_generator")
# spike times
spike_times_post = [1.0]
spike_times_pre = [1.0]
ngpu.SetStatus(sg_post, {"spike_times": spike_times_post})
ngpu.SetStatus(sg_pre, {"spike_times": spike_times_pre})
# connect spike generators to neurons
syn_group_ex = ngpu.CreateSynGroup \
("stdp", {"tau_plus":tau_plus, "tau_minus":tau_minus, \
"lambda":lambd_ex, "alpha":alpha, "mu_plus":mu_plus, \
"mu_minus":mu_minus, "Wmax":Wmax})
syn_group_in = ngpu.CreateSynGroup \
("stdp", {"tau_plus":tau_plus, "tau_minus":tau_minus, \
"lambda":lambd_in, "alpha":alpha, "mu_plus":mu_plus, \
"mu_minus":mu_minus, "Wmax":Wmax})
# poisson generator parameters
poiss_rate = 20000.0 # poisson signal rate in Hz
poiss_weight = 0.37 * fact
poiss_delay = 1.0 # poisson signal delay in ms
# create poisson generator
pg = ngpu.Create("poisson_generator")
ngpu.SetStatus(pg, "rate", poiss_rate)
# Create n_neurons neurons with n_receptor receptor ports
neuron = ngpu.Create("izhikevich_cond_beta", n_neurons, n_receptors)
exc_neuron = neuron[0:NE] # excitatory neurons
inh_neuron = neuron[NE:n_neurons] # inhibitory neurons
# receptor parameters
E_rev = [0.0, -85.0]
tau_decay = [1.0, 1.0]
tau_rise = [1.0, 1.0]
ngpu.SetStatus(neuron, {
"E_rev": E_rev,
"tau_decay": tau_decay,
import sys
import neurongpu as ngpu
tolerance = 1.0e-6
neuron = ngpu.Create("aeif_cond_beta", 3)
ngpu.SetStatus(neuron, {"I_e": 1000.0})
spike_det = ngpu.Create("spike_detector")
conn_dict = {"rule": "one_to_one"}
syn_dict1 = {"weight": 1.0, "delay": 10.0, "receptor": 0}
syn_dict2 = {"weight": 2.0, "delay": 20.0, "receptor": 0}
syn_dict3 = {"weight": 3.0, "delay": 30.0, "receptor": 0}
ngpu.Connect(neuron[0:0], spike_det, conn_dict, syn_dict1)
ngpu.Connect(neuron[1:1], spike_det, conn_dict, syn_dict2)
ngpu.Connect(neuron[2:2], spike_det, conn_dict, syn_dict3)
record_n = ngpu.CreateRecord("", ["spike"], [neuron[0]], [0])
record_sd = ngpu.CreateRecord("", ["spike_height"], [spike_det[0]], [0])
ngpu.Simulate()
data_n = ngpu.GetRecordData(record_n)
t_n = [row[0] for row in data_n]
spike_n = [row[1] for row in data_n]
def __create_neuronal_populations(self):
""" Creates the neuronal populations.
The neuronal populations are created and the parameters are assigned
to them. The initial membrane potential of the neurons is drawn from
normal distributions dependent on the parameter ``V0_type``.
The first and last neuron id of each population is written to file.
"""
if self.Rank == 0:
print('Creating neuronal populations.')
self.n_tot_neurons = 0
for i in np.arange(self.num_pops):
self.n_tot_neurons = self.n_tot_neurons + self.num_neurons[i]
self.neurons = ngpu.Create(self.net_dict['neuron_model'],
self.n_tot_neurons)
tau_syn = self.net_dict['neuron_params']['tau_syn']
E_L = self.net_dict['neuron_params']['E_L']
V_th = self.net_dict['neuron_params']['V_th']
V_reset = self.net_dict['neuron_params']['V_reset']
t_ref = self.net_dict['neuron_params']['t_ref']
ngpu.SetStatus(
self.neurons, {
"tau_syn": tau_syn,
"E_L": E_L,
"Theta_rel": V_th - E_L,
"V_reset_rel": V_reset - E_L,
"t_ref": t_ref
})
self.pops = []
for i in np.arange(self.num_pops):
if i == 0:
i_node_0 = 0
i_node_1 = i_node_0 + self.num_neurons[i]
#print("i_node_1 ", i_node_1)
population = self.neurons[i_node_0:i_node_1]
i_node_0 = i_node_1
I_e = self.DC_amp[i]
ngpu.SetStatus(population, {"I_e": I_e})
#print(population.i0)
#print(population.n)
if self.net_dict['V0_type'] == 'optimized':
V_rel_mean = self.net_dict['neuron_params']['V0_mean'] \
['optimized'][i] - E_L
V_std = self.net_dict['neuron_params']['V0_std'] \
['optimized'][i]
elif self.net_dict['V0_type'] == 'original':
V_rel_mean = self.net_dict['neuron_params']['V0_mean'] \
['original'] - E_L,
V_std = self.net_dict['neuron_params']['V0_std']['original']
else:
raise Exception(
'V0_type incorrect. ' +
'Valid options are "optimized" and "original".')
#print("V_rel_mean", V_rel_mean)
#print("V_std", V_std)
#print("pop size: ", len(population))
ngpu.SetStatus(
population, {
"V_m_rel": {
"distribution": "normal",
"mu": V_rel_mean,
"sigma": V_std
}
})
self.pops.append(population)
# write node ids to file
if self.Rank == 0:
fn = os.path.join(self.data_path, 'population_nodeids.dat')
with open(fn, 'w+') as f:
for pop in self.pops:
f.write('{} {}\n'.format(pop[0], pop[len(pop) - 1]))
import ctypes
import neurongpu as ngpu
N = 5
neuron = ngpu.Create("aeif_cond_beta", 2 * N)
neuron_even = []
neuron_odd = []
for i in range(N):
neuron_even.append(neuron[2 * i])
neuron_odd.append(neuron[2 * i + 1])
even_to_odd_delay = []
even_to_odd_weight = []
odd_to_even_delay = []
odd_to_even_weight = []
for itgt in range(N):
ite = 2 * itgt
ito = 2 * itgt + 1
for isrc in range(N):
ise = 2 * isrc
iso = 2 * isrc + 1
even_to_odd_delay.append(2.0 * N * ise + ito)
even_to_odd_weight.append(100.0 * (2.0 * N * ise + ito))
odd_to_even_delay.append(2.0 * N * iso + ite)
odd_to_even_weight.append(100.0 * (2.0 * N * iso + ite))
conn_dict = {"rule": "all_to_all"}
even_to_odd_syn_dict = {
"weight_array": even_to_odd_weight,
"delay_array": even_to_odd_delay
import sys
import neurongpu as ngpu
import numpy as np
tolerance = 0.0005
neuron = ngpu.Create('ext_neuron', 1, 3)
spike = ngpu.Create("spike_generator")
spike_times = [50.0, 100.0, 400.0, 600.0]
n_spikes = 4
# set spike times and heights
ngpu.SetStatus(spike, {"spike_times": spike_times})
delay = [1.0, 50.0, 100.0]
weight = [0.1, 0.2, 0.5]
conn_spec = {"rule": "all_to_all"}
for syn in range(3):
syn_spec = {'receptor': syn, 'weight': weight[syn], 'delay': delay[syn]}
ngpu.Connect(spike, neuron, conn_spec, syn_spec)
i_neuron_arr = [neuron[0], neuron[0], neuron[0]]
i_receptor_arr = [0, 1, 2]
var_name_arr = ["port_value", "port_value", "port_value"]
record = ngpu.CreateRecord("", var_name_arr, i_neuron_arr, i_receptor_arr)
ngpu.Simulate(800.0)
data_list = ngpu.GetRecordData(record)
t = [row[0] for row in data_list]
val1 = [row[1] for row in data_list]
val2 = [row[2] for row in data_list]
val3 = [row[3] for row in data_list]
NI = 1 * order # number of inhibitory neurons
n_neurons = NE + NI # number of neurons in total
CE = 800 # number of excitatory synapses per neuron
CI = CE // 4 # number of inhibitory synapses per neuron
Wex = 0.05
Win = 0.35
# poisson generator parameters
poiss_rate = 20000.0 # poisson signal rate in Hz
poiss_weight = 0.37
poiss_delay = 0.2 # poisson signal delay in ms
# create poisson generator
pg = ngpu.Create("poisson_generator")
ngpu.SetStatus(pg, "rate", poiss_rate)
# Create n_neurons neurons with n_receptor receptor ports
neuron = ngpu.Create("aeif_cond_beta", n_neurons, n_receptors)
exc_neuron = neuron[0:NE] # excitatory neurons
inh_neuron = neuron[NE:n_neurons] # inhibitory neurons
# receptor parameters
E_rev = [0.0, -85.0]
tau_decay = [1.0, 1.0]
tau_rise = [1.0, 1.0]
ngpu.SetStatus(neuron, {
"E_rev": E_rev,
"tau_decay": tau_decay,
import sys
import neurongpu as ngpu
n_neurons = 6
# create n_neurons neurons with 2 receptor ports
neuron = ngpu.Create('aeif_cond_beta', n_neurons, 2)
neuron_even = [neuron[0], neuron[2], neuron[4]]
neuron_odd = [neuron[3], neuron[5], neuron[1]]
ngpu.SetStatus(neuron_even, {
'tau_decay': [80.0, 40.0],
'tau_rise': [60.0, 20.0]
})
ngpu.SetStatus(neuron_odd, {
'tau_decay': [70.0, 30.0],
'tau_rise': [50.0, 10.0]
})
ngpu.SetStatus(neuron_even, {'V_m': -80.0})
ngpu.SetStatus(neuron_odd, {'V_m': -90.0})
ngpu.SetStatus(neuron_even, {'g1': [0.4, 0.2]})
ngpu.SetStatus(neuron_odd, {'g1': [0.3, 0.1]})
ngpu.SetStatus(neuron_even, {'V_th': -40.0})
ngpu.SetStatus(neuron_odd, {'V_th': -30.0})
# reading parameters and variables test
read_td = ngpu.GetNeuronStatus(neuron, "tau_decay")
read_tr = ngpu.GetNeuronStatus(neuron, "tau_rise")
read_Vm = ngpu.GetNeuronStatus(neuron, "V_m")
tau_plus = 20.0
tau_minus = 20.0
lambd = 1.0
alpha = 1.0
mu_plus = 1.0
mu_minus = 1.0
Wmax = 0.001
den_delay = 0.0
syn_group = ngpu.CreateSynGroup \
("stdp", {"tau_plus":tau_plus, "tau_minus":tau_minus, \
"lambda":lambd, "alpha":alpha, "mu_plus":mu_plus, \
"mu_minus":mu_minus, "Wmax":Wmax})
sg = ngpu.Create("spike_generator")
neuron0 = ngpu.Create("aeif_cond_beta")
neuron1 = ngpu.Create("aeif_cond_beta", N)
ngpu.SetStatus(neuron1, {"t_ref": 1000.0, "den_delay": den_delay})
time_diff = 400.0
dt_list = []
delay_stdp_list = []
for i in range(N):
dt_list.append(dt_step * (-0.5 * (N - 1) + i))
delay_stdp_list.append(time_diff - dt_list[i])
spike_times = [50.0]
n_spikes = 1
# set spike times and height
import sys
import neurongpu as ngpu
spike = ngpu.Create("spike_generator", 4)
spike0 = spike[0:2]
spike1 = spike[2:3]
spike2 = spike[3:4]
spike_time0 = [10.0, 400.0]
spike_height0 = [1.0, 0.5]
spike_time1 = [4.0]
spike_height1 = [2.0]
spike_time2 = [50.0, 20.0, 80.0]
spike_height2 = [0.1, 0.3, 0.2]
# set spike times and heights
ngpu.SetStatus(spike0, {
"spike_times": spike_time0,
"spike_heights": spike_height0
})
ngpu.SetStatus(spike1, {
"spike_times": spike_time1,
"spike_heights": spike_height1
})
ngpu.SetStatus(spike2, {
"spike_times": spike_time2,
"spike_heights": spike_height2
})
import neurongpu as ngpu
import nest
import ngpu_nest
import numpy as np
ngpu_neuron = ngpu.Create("aeif_cond_beta")
# receptor parameters
E_rev = [0.0]
tau_rise = [2.0]
tau_decay = [20.0]
ngpu.SetStatus(ngpu_neuron, {
"E_rev": E_rev,
"tau_decay": tau_decay,
"tau_rise": tau_rise
})
nest_neuron = nest.Create("aeif_cond_beta_multisynapse")
nest.SetStatus(nest_neuron, {
"E_rev": E_rev,
"tau_decay": tau_decay,
"tau_rise": tau_rise
})
neuron_image = ngpu_nest.CreateNeuronImage(nest_neuron, 2)
poiss_gen = ngpu.Create("poisson_generator")
ngpu.SetStatus(poiss_gen, "rate", 12000.0)
parrot = ngpu.Create("parrot_neuron")
ngpu.SetStatus(parrot, {'hold_spike_height': 1})
NI = 1 * order # number of inhibitory neurons
n_neurons = NE + NI # number of neurons in total
CE = 800 # number of excitatory synapses per neuron
CI = CE // 4 # number of inhibitory synapses per neuron
Wex = 0.05
Win = 0.35
# poisson generator parameters
poiss_rate = 20000.0 # poisson signal rate in Hz
poiss_weight = 0.37
poiss_delay = 0.2 # poisson signal delay in ms
# create poisson generator
pg = ngpu.Create("poisson_generator")
ngpu.SetStatus(pg, "rate", poiss_rate)
pg_list = pg.ToList()
# Create n_neurons neurons with n_receptor receptor ports
neuron = ngpu.Create("aeif_cond_beta", n_neurons, n_receptors)
exc_neuron = neuron[0:NE] # excitatory neurons
inh_neuron = neuron[NE:n_neurons] # inhibitory neurons
neuron_list = neuron.ToList()
exc_neuron_list = exc_neuron.ToList()
inh_neuron_list = exc_neuron.ToList()
# receptor parameters
E_rev = [0.0, -85.0]
tau_decay = [1.0, 1.0]
tau_rise = [1.0, 1.0]
import sys
import neurongpu as ngpu
neuron = ngpu.Create('aeif_psc_delta')
ngpu.SetStatus(neuron, {"V_peak": 0.0, "a": 4.0, "b":80.5, "E_L":-70.6, \
"g_L":300.0, "C_m":20000.0})
spike = ngpu.Create("spike_generator")
spike_times = [10.0, 400.0]
n_spikes = 2
# set spike times and height
ngpu.SetStatus(spike, {"spike_times": spike_times})
delay = [1.0, 100.0]
weight = [1.0, -2.0]
conn_spec={"rule": "all_to_all"}
syn_spec_ex={'weight': weight[0], 'delay': delay[0]}
syn_spec_in={'weight': weight[1], 'delay': delay[1]}
ngpu.Connect(spike, neuron, conn_spec, syn_spec_ex)
ngpu.Connect(spike, neuron, conn_spec, syn_spec_in)
record = ngpu.CreateRecord("", ["V_m"], [neuron[0]], [0])
#voltmeter = nest.Create('voltmeter')
#nest.Connect(voltmeter, neuron)
ngpu.Simulate(800.0)
data_list = ngpu.GetRecordData(record)
t=[row[0] for row in data_list]
import sys
import neurongpu as ngpu
import numpy as np
tolerance = 0.0005
E_L = -65.0
ngpu.SetVerbosityLevel(0)
neuron = ngpu.Create('iaf_psc_exp_hc', 1)
spike = ngpu.Create("spike_generator")
spike_times = [10.0, 400.0]
n_spikes = 2
# set spike times and height
ngpu.SetStatus(spike, {"spike_times": spike_times})
delay = [1.0, 100.0]
weight = [1.0, -2.0]
conn_spec = {"rule": "all_to_all"}
syn_spec_ex = {'weight': weight[0], 'delay': delay[0]}
syn_spec_in = {'weight': weight[1], 'delay': delay[1]}
ngpu.Connect(spike, neuron, conn_spec, syn_spec_ex)
ngpu.Connect(spike, neuron, conn_spec, syn_spec_in)
record = ngpu.CreateRecord("", ["V_m_rel"], [neuron[0]], [0])
#voltmeter = nest.Create('voltmeter')
#nest.Connect(voltmeter, neuron)
ngpu.Simulate(800.0)
import sys
import neurongpu as ngpu
import numpy as np
tolerance = 0.0005
E_L = -65.0
ngpu.SetVerbosityLevel(0)
neuron = ngpu.Create('fast_iaf_psc_exp', 1)
spike = ngpu.Create("spike_generator")
spike_times = [10.0, 400.0]
n_spikes = 2
# set spike times and height
ngpu.SetStatus(spike, {"spike_times": spike_times})
delay = [1.0, 100.0]
weight = [1.0, -2.0]
conn_spec = {"rule": "all_to_all"}
syn_spec_ex = {'weight': weight[0], 'delay': delay[0]}
syn_spec_in = {'weight': weight[1], 'delay': delay[1]}
ngpu.Connect(spike, neuron, conn_spec, syn_spec_ex)
ngpu.Connect(spike, neuron, conn_spec, syn_spec_in)
record = ngpu.CreateRecord("", ["V_m_rel"], [neuron[0]], [0])
#voltmeter = nest.Create('voltmeter')
#nest.Connect(voltmeter, neuron)
ngpu.Simulate(800.0)
import neurongpu as ngpu
import nest
import ngpu_nest
import numpy as np
neuron = ngpu.Create('aeif_cond_beta', 1, 4)
ngpu.SetStatus(neuron, {"V_peak": 0.0, "a": 4.0, "b": 80.5})
ngpu.SetStatus(
neuron, {
'E_rev': [0.0, 0.0, 0.0, -85.0],
'tau_decay': [50.0, 20.0, 20.0, 20.0],
'tau_rise': [10.0, 10.0, 1.0, 1.0]
})
spike = nest.Create('spike_generator',
params={'spike_times': np.array([10.0])})
spike_image = ngpu_nest.CreateNeuronImage(spike, 1)
delay = [1.0, 300.0, 500.0, 700.0]
w = [1.0, 1.0, 1.0, 1.0]
conn_spec = {"rule": "all_to_all"}
for syn in range(4):
syn_spec = {'receptor': syn, 'weight': w[syn], 'delay': delay[syn]}
ngpu.Connect(spike_image, neuron, conn_spec, syn_spec)
record = ngpu.CreateRecord("", ["V_m"], [neuron[0]], [0])
nest.Simulate(1000.0)
data_list = ngpu.GetRecordData(record)
import sys
import neurongpu as ngpu
import nest
import ngpu_nest
import numpy as np
if len(sys.argv) != 2:
print("Usage: python %s n_neurons" % sys.argv[0])
quit()
n_ngpu = int(sys.argv[1])
n_nest = 1000
indegree = 1000
ngpu_neuron = ngpu.Create("aeif_cond_beta", n_ngpu)
# receptor parameters
E_rev = [0.0]
tau_rise = [2.0]
tau_decay = [20.0]
ngpu.SetStatus(ngpu_neuron, {
"E_rev": E_rev,
"tau_decay": tau_decay,
"tau_rise": tau_rise
})
nest_neuron = nest.Create("aeif_cond_beta_multisynapse", n_nest)
nest.SetStatus(nest_neuron, {
"E_rev": E_rev,
"tau_decay": tau_decay,
"tau_rise": tau_rise
