# List of machines external IP addresses
machines = []
local_machines = [('localhost', 2718+i) for i in xrange(nlocal)]
machines.extend(local_machines)
for m in local_machines:
Process(target=open_server, args=(m[1],1,0)).start()
equations = Equations('''
dV/dt=(R*I-V)/tau : 1
I : 1
R : 1
tau : second
''')
input = loadtxt('current.txt')
spikes = loadtxt('spikes.txt')
results = modelfitting( model = equations,
reset = 0,
threshold = 1,
data = spikes,
input = input,
dt = .1*ms,
popsize = 1000,
maxiter = 5,
delta = 4*ms,
unit_type = 'CPU',
machines = machines,
R = [1.0e9, 9.0e9],
tau = [10*ms, 40*ms],
refractory = [0*ms, 10*ms])
Example showing how to fit a single model
with different target spike trains (several groups).
'''
from brian import loadtxt, ms, Equations, second
from brian.library.modelfitting import *
if __name__ == '__main__':
model = Equations('''
dV/dt=(R*I-V)/tau : 1
I : 1
R : 1
tau : second
''')
input = loadtxt('examples/modelfitting/current.txt')
spikes0 = loadtxt('examples/modelfitting/spikes.txt')
spikes = []
for i in xrange(2):
spikes.extend([(i, spike * second + 5 * i * ms) for spike in spikes0])
results = modelfitting(model=model,
reset=0,
threshold=1,
data=spikes,
input=input,
dt=.1 * ms,
popsize=1000,
maxiter=3,
cpu=1,
delta=4 * ms,
'''
Model fitting example.
Fit an integrate-and-fire model to an in-vitro electrophysiological
recording during one second.
'''
from brian import loadtxt, ms, Equations
from brian.library.modelfitting import *
if __name__ == '__main__':
equations = Equations('''
dV/dt=(R*I-V)/tau : 1
I : 1
R : 1
tau : second
''')
input = loadtxt('current.txt')
spikes = loadtxt('spikes.txt')
results = modelfitting(model=equations,
reset=0,
threshold=1,
data=spikes,
input=input,
dt=.1 * ms,
popsize=1000,
maxiter=3,
delta=4 * ms,
R=[1.0e9, 9.0e9],
tau=[10 * ms, 40 * ms],
refractory=[0 * ms, 10 * ms])
print_table(results)
return criterion_values
if __name__ == '__main__':
from brian import loadtxt, ms, savetxt, loadtxt, Equations, NeuronGroup, run, SpikeMonitor,\
StateMonitor, Network
from pylab import *
equations = Equations('''
dV/dt=(R*I-V)/tau : 1
I : 1
R : 1
tau : second
''')
input = loadtxt('current.txt')
trace = loadtxt('trace_artificial.txt')
spikes = loadtxt('spikes_artificial.txt')
# GAMMA FACTOR
criterion = GammaFactor(delta=2 * ms)
data = spikes
# data[:,1] += 50*ms
# LP ERROR
criterion = LpError(p=2, varname='V')
data = trace
# SIMULATE, EVALUATE CRITERION AND RECORD TRACES ON GPU
neurons = 5
g.I = TimedArray(input, dt=.1*ms)
g.tau = 25*ms
g.R = 3e9
SpM = SpikeMonitor(g)
StM = StateMonitor(g, 'V', record=True)
net = Network(g, SpM, StM)
net.run(1*second)
return StM.values[0], SpM.spikes
equations = Equations('''
dV/dt=(R*I-V)/tau : 1
I : 1
R : 1
tau : second
''')
input = loadtxt('current.txt')
# ARTIFICIAL DATA: R=3e9, tau=25*ms
# spikes = loadtxt('spikes.txt') # real data
# trace, spikes = generate_data()
# savetxt('trace_artificial.txt', trace)
# savetxt('spikes_artificial.txt', spikes)
trace = loadtxt('trace_artificial.txt')
spikes= loadtxt('spikes_artificial.txt')
# GAMMA FACTOR
criterion = GammaFactor(delta=2*ms)
data = spikes
# data[:,1] += 50*ms
SpM = SpikeMonitor(g)
StM = StateMonitor(g, "V", record=True)
net = Network(g, SpM, StM)
net.run(1 * second)
return StM.values[0], SpM.spikes
equations = Equations(
"""
dV/dt=(R*I-V)/tau : 1
I : 1
R : 1
tau : second
#tau_metric : second
"""
)
input = loadtxt("current.txt")
# ARTIFICIAL DATA: R=3e9, tau=25*ms
# spikes = loadtxt('spikes.txt') # real data
trace, spikes = generate_data()
# savetxt('trace_artificial.txt', trace)
# savetxt('spikes_artificial.txt', spikes)
# trace = loadtxt('trace_artificial.txt')
overlap = 10 * ms
slices = 1
dt = 0.1 * ms
input, trace = slice_trace(input, trace, slices=slices, dt=dt, overlap=overlap)
# GAMMA FACTOR
# criterion = GammaFactor(delta=2*ms)
g.tau = 25 * ms
g.R = 3e9
SpM = SpikeMonitor(g)
StM = StateMonitor(g, 'V', record=True)
net = Network(g, SpM, StM)
net.run(1 * second)
return StM.values[0], SpM.spikes
equations = Equations('''
dV/dt=(R*I-V)/tau : 1
I : 1
R : 1
tau : second
#tau_metric : second
''')
input = loadtxt('current.txt')
# ARTIFICIAL DATA: R=3e9, tau=25*ms
# spikes = loadtxt('spikes.txt') # real data
trace, spikes = generate_data()
# savetxt('trace_artificial.txt', trace)
# savetxt('spikes_artificial.txt', spikes)
# trace = loadtxt('trace_artificial.txt')
overlap = 10 * ms
slices = 1
dt = .1 * ms
input, trace = slice_trace(input,
trace,
slices=slices,
dt=dt,
if __name__ == "__main__":
from brian import loadtxt, ms, savetxt, loadtxt, Equations, NeuronGroup, run, SpikeMonitor, StateMonitor, Network
from pylab import *
equations = Equations(
"""
dV/dt=(R*I-V)/tau : 1
I : 1
R : 1
tau : second
"""
)
input = loadtxt("current.txt")
trace = loadtxt("trace_artificial.txt")
spikes = loadtxt("spikes_artificial.txt")
# GAMMA FACTOR
criterion = GammaFactor(delta=2 * ms)
data = spikes
# data[:,1] += 50*ms
# LP ERROR
criterion = LpError(p=2, varname="V")
data = trace
# SIMULATE, EVALUATE CRITERION AND RECORD TRACES ON GPU
neurons = 5