def generate_data(label):
spikesTrain = []
organisedData = {}
for i in range(input_class):
for j in range(input_len):
neuid = (i, j)
organisedData[neuid] = []
for i in range(input_len):
neuid = (label, i)
organisedData[neuid].append(i * v_co)
# if neuid not in organisedData:
# organisedData[neuid]=[i*v_co]
# else:
# organisedData[neuid].append(i*v_co)
for i in range(input_class):
for j in range(input_len):
neuid = (i, j)
organisedData[neuid].sort()
spikesTrain.append(organisedData[neuid])
runTime = int(max(max(spikesTrain)))
sim.setup(timestep=1)
noise = sim.Population(input_size, sim.SpikeSourcePoisson(), label='noise')
noise.record(['spikes']) #noise
sim.run(runTime)
neonoise = noise.get_data(["spikes"])
spikesnoise = neonoise.segments[0].spiketrains #noise
sim.end()
for i in range(input_size):
for noisespike in spikesnoise[i]:
spikesTrain[i].append(noisespike)
spikesTrain[i].sort()
return spikesTrain
import pyNN.brian as sim
import numpy as np
import matplotlib.pyplot as plt
run_time = 500
no_run = 3
sim.setup()
Excinp = sim.Population(10, sim.SpikeSourcePoisson(rate = 20.0, start = 0, duration = run_time * no_run))
# cell_type_parameters = {'tau_refrac': 0.1, 'v_thresh': -50.0, 'tau_m': 20.0, 'tau_syn_E': 0.5, 'v_rest': -65.0,\
# 'cm': 1.0, 'v_reset': -65.0, 'tau_syn_I': 0.5, 'i_offset': 0.0}
# print(sim.IF_curr_alpha.default_parameters)
# cell_type = sim.IF_cond_exp(**cell_type_parameters) # neuron type of population
Pexc = sim.Population(10, sim.EIF_cond_exp_isfa_ista(), label = "excitotary neurons")
# Pexc.set(tau_refrac = 0.1, v_thresh = -50.0, tau_m = 20.0, tau_syn_E = 0.5, v_rest = -65.0, \
# cm = 1.0, v_reset = -65, tau_syn_I = 0.5, i_offset = 0.0)
# Pexc.initialize(**cell_type_parameters)
# print Pexc.celltype.default_initial_values
# print Pexc.get('tau_m')
# syn = sim.StaticSynapse(weight = 0.05, delay = 0.5)
# depressing_synapse_ee = sim.TsodyksMarkramSynapse(weight = 0.05, delay = 0.2, U = 0.5, tau_rec = 800.0, tau_facil = 0.01)
facilitating_synapse_ee = sim.TsodyksMarkramSynapse(weight = 0.05, delay = 0.5, U = 0.04, tau_rec = 100.0, tau_facil = 1000)
connection = sim.Projection(Excinp, Pexc, sim.AllToAllConnector(), facilitating_synapse_ee, receptor_type = 'excitatory')
# E_E_connection = sim.Projection(Pexc, Pexc, sim.FixedProbabilityConnector(p_connect = 0.5), depressing_synapse_ee, receptor_type = 'excitatory')
Excinp.record('spikes')
# Excinp[1].record('v')
Pexc.record('spikes')
import pyNN.brian as sim
from pyNN.random import RandomDistribution, NumpyRNG
import numpy as np
import matplotlib.pyplot as plt
N_e = 75 # number of excitatory neurons
N_i = 25 # number of inhibitatory neurons
Exc_in = 32 # number of excitatory inputs
Inh_in = 32 # number of inhibitatory inputs
weight_ini = 0.16 # define the initial value of the input signal weight
run_time = 500 # define the simulation time per run
sim.setup()
# ==========generate OR read in the input spikes data=====================
Excinp = sim.Population(Exc_in, sim.SpikeSourcePoisson(rate=15))
Inhinp = sim.Population(Inh_in, sim.SpikeSourcePoisson(rate=15))
# ==========create neuron population=====================
# todo: the initail parameters of neurons might be modified
cell_type_parameters = {'tau_refrac': 0.1, 'v_thresh': -50.0, 'tau_m': 20.0, 'tau_syn_E': 0.5, 'v_rest': -65.0,\
'cm': 1.0, 'v_reset': -65.0, 'tau_syn_I': 0.5, 'i_offset': 0.0}
# print(sim.IF_curr_alpha.default_parameters)
cell_type = sim.IF_cond_exp(**
cell_type_parameters) # neuron type of population
Pexc = sim.Population(
N_e, cell_type, label="excitotary neurons") # excitatory neuron population
Pinh = sim.Population(
N_i, cell_type,
label="inhibitatory neurons") # inhibitoty neuron population
p[0, 2, 4].set(tau_m=10)
print(p.get('tau_m'))
print(p[0].tau_m)
#random value
from pyNN.random import RandomDistribution, NumpyRNG
gbar_na_distr = RandomDistribution('normal', (20.0, 2.0),
rng=NumpyRNG(seed=85524))
p = sim.Population(7, sim.HH_cond_exp(gbar_Na=gbar_na_distr))
print(p.get('gbar_Na'))
print(p[0].gbar_Na)
#setting from an array
import numpy as np
p = sim.Population(6,
sim.SpikeSourcePoisson(rate=np.linspace(10.0, 20.0, num=6)))
print(p.get('rate'))
#using function to calculate
from numpy import sin, pi
p = sim.Population(8, sim.IF_cond_exp(i_offset=lambda i: sin(i * pi / 8)))
print(p.get('i_offset'))
#Setting parameters as a function of spatial position
from pyNN.space import Grid2D
grid = Grid2D(dx=10.0, dy=10.0)
p = sim.Population(16, sim.IF_cond_alpha(), structure=grid)
def f_v_thresh(pos):
x, y, z = pos.T
import pyNN.brian as sim # can of course replace `neuron` with `nest`, `brian`, etc.
import matplotlib.pyplot as plt
import numpy as np
sim.setup(timestep=0.01)
p_in = sim.Population(10, sim.SpikeSourcePoisson(rate=10.0), label="input")
p_out = sim.Population(10, sim.EIF_cond_exp_isfa_ista(), label="AdExp neurons")
syn = sim.StaticSynapse(weight=0.05)
random = sim.FixedProbabilityConnector(p_connect=0.5)
connections = sim.Projection(p_in,
p_out,
random,
syn,
receptor_type='excitatory')
p_in.record('spikes')
p_out.record('spikes') # record spikes from all neurons
p_out[0:2].record(['v', 'w', 'gsyn_exc'
]) # record other variables from first two neurons
for i in range(2):
sim.run(500.0)
spikes_in = p_in.get_data()
data_out = p_out.get_data()
sim.reset()
connections.set(weight=0.05)
sim.end()
print 'finish simulation'
eta = 0.5 # learning rate
iter_no = 5 # learning iteration
source_rate = [20, 40]
w1_1 = 0.1
w1_2 = 0.1
w2_1 = 0.1
w2_2 = 0.1
# set up the classifier network
for i in range(len(training_label)):
# for i in range(1):
lg.info('iteration number %d' % i)
sim.setup()
In_1 = sim.Population(
10, sim.SpikeSourcePoisson(rate=source_rate[training_label[i]]))
In_2 = sim.Population(
10, sim.SpikeSourcePoisson(rate=source_rate[1 - training_label[i]]))
In = In_1 + In_2
Out_1 = sim.Population(10, sim.IF_cond_exp())
Out_2 = sim.Population(10, sim.IF_cond_exp())
Out = Out_1 + Out_2
syn_1_1 = sim.StaticSynapse(weight=w1_1, delay=0.5)
syn_1_2 = sim.StaticSynapse(weight=w1_2, delay=0.5)
syn_2_1 = sim.StaticSynapse(weight=w2_1, delay=0.5)
syn_2_2 = sim.StaticSynapse(weight=w2_2, delay=0.5)
prj_1_1 = sim.Projection(In_1,
Out_1,
import pyNN.brian as sim
import numpy as np
import matplotlib.pyplot as plt
sim.setup()
Excinp = sim.Population(
10, sim.SpikeSourcePoisson(rate=20.0, start=0, duration=500))
# cell_type_parameters = {'tau_refrac': 0.1, 'v_thresh': -50.0, 'tau_m': 20.0, 'tau_syn_E': 0.5, 'v_rest': -65.0,\
# 'cm': 1.0, 'v_reset': -65.0, 'tau_syn_I': 0.5, 'i_offset': 0.0}
# print(sim.IF_curr_alpha.default_parameters)
# cell_type = sim.IF_cond_exp(**cell_type_parameters) # neuron type of population
Pexc = sim.Population(10,
sim.EIF_cond_exp_isfa_ista(),
label="excitotary neurons")
# Pexc.set(tau_refrac = 0.1, v_thresh = -50.0, tau_m = 20.0, tau_syn_E = 0.5, v_rest = -65.0, \
# cm = 1.0, v_reset = -65, tau_syn_I = 0.5, i_offset = 0.0)
# Pexc.initialize(**cell_type_parameters)
# print Pexc.celltype.default_initial_values
# print Pexc.get('tau_m')
# syn = sim.StaticSynapse(weight = 0.05, delay = 0.5)
# depressing_synapse_ee = sim.TsodyksMarkramSynapse(weight = 0.05, delay = 0.2, U = 0.5, tau_rec = 800.0, tau_facil = 0.01)
facilitating_synapse_ee = sim.TsodyksMarkramSynapse(weight=0.05,
delay=0.5,
U=0.04,
tau_rec=100.0,
tau_facil=1000)
connection = sim.Projection(Excinp,
Pexc,
sim.AllToAllConnector(),
facilitating_synapse_ee,
import pyNN.brian as sim
import numpy as np
training_data = np.loadtxt('training_data_0_1.txt', delimiter=',')
training_label = training_data[:, -1]
training_rate = training_data[:, 0:64]
# print training_rate[1, :]
inputpop = []
sim.setup()
for i in range(np.size(training_rate, 1)):
inputpop.append(
sim.Population(1, sim.SpikeSourcePoisson(rate=abs(training_rate[0,
i]))))
# print inputpop[0].get('rate')
# inputpop[0].set(rate = 8)
# print inputpop[0].get('rate')
pop = sim.Population(1, sim.IF_cond_exp(), label='exc')
prj1 = sim.Projection(inputpop[0],
pop,
sim.OneToOneConnector(),
synapse_type=sim.StaticSynapse(weight=0.04, delay=0.5),
receptor_type='inhibitory')
print prj1.get('weight', format='list')
