import brian_no_units
from brian.globalprefs import set_global_preferences
set_global_preferences(useweave=True,
usecodegen=True,
usecodegenweave=True,
usecodegenstateupdate = True,
usenewpropagate = True,
usecstdp = True,
gcc_options = ['-O3', '-ffast-math','-march=core2']
)
def run_stdp(NE,NI,v_init,C_e,C_ii,C_ie,mon_bin,dt):
from brian.neurongroup import NeuronGroup
from brian.monitor import PopulationRateMonitor
from brian.stdunits import mV, ms, nS, pF, pA, Hz
from brian.units import second
from brian.equations import Equations
from brian.network import Network
from brian.connections import Connection
from brian.stdp import STDP
from brian.clock import Clock
runtime = 10*second
eta = 1e-2 # Learning rate
tau_stdp = 20*ms # STDP time constant
alpha = 3*Hz*tau_stdp*2 # Target rate parameter
gmax = 100 # Maximum inhibitory weight
eqs_neurons='''
'siemens')
self._store_array_with_unit(
outfile, group, 'times',
np.hstack((self.input_data.root.weights.excitatory.times,
self.input_data.root.weights.excitatory.times[-1] +
(self.dt * self.weights_timestep +
self.m_exc_weights.times) / b.second)),
'second', "Times of the recorded synaptic weights.")
if __name__ == '__main__':
import argparse
import os.path
import tables
from brian.globalprefs import set_global_preferences
set_global_preferences(useweave=True)
logging.basicConfig()
logger.setLevel(logging.INFO)
parser = argparse.ArgumentParser(
description="Run the Vogels et al. 2011 single cell model.")
parser.add_argument(
'-i', '--input', type=str, nargs=1, required=True,
help="Path to the input file for which to continue the simulation.")
parser.add_argument(
'-t', '--time', type=float, nargs=1, required=True,
help="Additional time to simulate in seconds.")
parser.add_argument(
'output', nargs=1, type=str,
help="Filename of the HDF5 output file.")
pass
def reset(self):
pass
def __len__(self):
return self._length
def num_states(self):
return self._numstates
def __repr__(self):
return 'Virtual group of ' + str(len(self)) + ' neurons'
class ServerNetwork(Network):
'''
Network class for running a simulation over a cluster.
The server manages the clients.
'''
pass
# Identification
myid = pypar.rank() # id of this process
nproc = pypar.size() # number of processors
if myid > 0: # client
import sys
run_client()
sys.exit(0)
# Server
#Network=ServerNetwork
set_global_preferences(cluster_server=True)