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])
print_table(results)
close_servers(local_machines)
'''
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)
G.tau = 20*ms
M = SpikeMonitor(G)
G.I = TimedArray(input)
run(duration)
i, t = zip(*M.spikes)
clear(True)
reinit_default_clock()
return t
spikes0 = getspikes()
spikes = []
for i in xrange(1):
spikes.extend([(i, spike + 5*i*ms) for spike in spikes0])
results = modelfitting( model = model,
reset = 0,
threshold = 1,
data = spikes,
input = input,
dt = .1*ms,
popsize = 150000,
maxiter = 50,
gpu = 1,
algorithm = CMAES,
delta = 1*ms,
R = [1.0e9, 9.0e9],
tau = [10*ms, 40*ms],
delays = [-20*ms, 20*ms],
refractory=[0*ms, 0*ms, 10*ms, 10*ms])
print_table(results)
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,
R=[1.0e9, 9.0e9],
tau=[10 * ms, 40 * ms],
delays=[-10 * ms, 10 * ms])
print_table(results)
if __name__ == '__main__':
model = Equations('''
dV/dt=(R*I-V)/tau : 1
I : 1
R : 1
tau : second
''')
input = loadtxt('current.txt')
spikes0 = loadtxt('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,
R = [1.0e9, 9.0e9],
tau = [10*ms, 40*ms],
delays = [-10*ms, 10*ms])
print_table(results)