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='''
dv/dt=(-gl*(v-el)-(g_ampa*w*v+g_gaba*(v-er)*w)+bgcurrent)/memc : volt
dg_ampa/dt = -g_ampa/tau_ampa : 1
dg_gaba/dt = -g_gaba/tau_gaba : 1
'''
namespace = {'tau_ampa':5.0*ms,'tau_gaba':10.0*ms,
'bgcurrent':200*pA,'memc':200.0*pF,
'el':-60*mV,'w':1.*nS,'gl':10.0*nS,'er':-80*mV}
eqs_neurons = Equations(eqs_neurons, ** namespace)
clock = Clock(dt)
neurons=NeuronGroup(NE+NI,model=eqs_neurons,clock=clock,
threshold=-50.*mV,reset=-60*mV,refractory=5*ms)
neurons.v = v_init
Pe=neurons.subgroup(NE)
Pi=neurons.subgroup(NI)
rme = PopulationRateMonitor(Pe,mon_bin)
rmi = PopulationRateMonitor(Pi,mon_bin)
con_e = Connection(Pe,neurons,'g_ampa')
con_ie = Connection(Pi,Pe,'g_gaba')
con_ii = Connection(Pi,Pi,'g_gaba')
con_e.connect_from_sparse(C_e, column_access=True)
con_ie.connect_from_sparse(C_ie, column_access=True)
con_ii.connect_from_sparse(C_ii, column_access=True)
eqs_istdp = '''
dA_pre/dt=-A_pre/tau_stdp : 1
dA_post/dt=-A_post/tau_stdp : 1
'''
stdp_params = {'tau_stdp':tau_stdp, 'eta':eta, 'alpha':alpha}
eqs_istdp = Equations(eqs_istdp, **stdp_params)
stdp_ie = STDP(con_ie, eqs=eqs_istdp,
pre='''A_pre+=1.
w+=(A_post-alpha)*eta''',
post='''A_post+=1.
w+=A_pre*eta''',
wmax=gmax)
net = Network(neurons, con_e, con_ie, con_ii, stdp_ie, rme, rmi)
net.run(runtime,report='text')
return (rme.times,rme.rate), (rmi.times,rmi.rate)
def test_incorrect_pre_reference():
G = NeuronGroup(1, '''v : 1
x : 1''', threshold='v>1', reset='v=0')
G.v = 1.1 # spikes
G2 = NeuronGroup(10, '')
# The pre-synaptic x variable should be 10 in the end, because 10 synapses
# were created and all of them increase the pre-synaptic variable by 1.
# This does not work in Brian 1 (but it does work in Brian 2), we therefore
# only throw an error to avoid this problem.
assert_raises(ValueError, lambda: Synapses(G, G2, pre='x_pre += 1'))
def test_incorrect_pre_reference():
G = NeuronGroup(1,
'''v : 1
x : 1''',
threshold='v>1',
reset='v=0')
G.v = 1.1 # spikes
G2 = NeuronGroup(10, '')
# The pre-synaptic x variable should be 10 in the end, because 10 synapses
# were created and all of them increase the pre-synaptic variable by 1.
# This does not work in Brian 1 (but it does work in Brian 2), we therefore
# only throw an error to avoid this problem.
assert_raises(ValueError, lambda: Synapses(G, G2, pre='x_pre += 1'))
