def run(self, morphodata):
"""run """
# Set Biophysics
neuron = self.biophysics(morphodata)
neuron.run_regularly('Mgblock = 1./(1.+ exp(-0.062*vu2)/3.57)',
dt=br2.defaultclock.dt)
# Record in every section
# monitor = self.v_record(neuron)
monitor = br2.StateMonitor(neuron,
'v',
record=True,
dt=20 * defaultclock.dt)
morph_data = morphodata
axo = len(morph_data['axon'])
bsl = list(morph_data['basal'])
apc = list(morph_data['apical'])
# dc = distal_compartments_Branco_eff
# pc = proximal_compartments_Branco
dc = distal_compartments_Acker_eff
pc = proximal_compartments_Acker
# nrComp = 10 #len(bsl)
#####################################################
# Input Neuron
#####################################################
Theta_low = morph_data['thetalow'] * br2.mV
V_rest = 0. * br2.mV
V_thresh = 0.5 * br2.mV
NrInGroups = 4
Nr_clust_dist = NrInGroups #5 # Nr of clustered ensembles distally
Nr_clust_prox = 0 #5 # Nr of clustered ensembles proximally
Nr_scattered = 0 #5 # Nr of scattered ensembles
Ens_size = 20 # Ensemble size
GrpSize = Ens_size * NrInGroups
NrEnsembles = (Nr_clust_dist + Nr_clust_prox + Nr_scattered)
NrGroups = int(NrEnsembles / NrInGroups)
NrIn = NrEnsembles * Ens_size # nr of input neurons
init_weight = .2 # initial weight
signal_rate = 30. * br2.Hz # activation rate of synapses
t_stim = 50 * br2.ms # stimulation length
buffertime = 50 * br2.ms # resting time between stimulations
reps = 1 # nr of activations of each ensemble
# Equations input neuron
eqs_in = '''
dv/dt = (V_rest-v)/ms: volt
v2 = rand()<rate_v*dt :1 (constant over dt)
rate_v :Hz
ds_trace/dt = -s_trace/taux :1
'''
#####################################################
# Create neurons
#####################################################
N_input = br2.NeuronGroup(NrIn,
eqs_in,
threshold='v+v2*2*V_thresh>V_thresh',
reset='v=V_rest;s_trace+=x_reset*(taux/ms)',
method='linear') #
Syn_1 = br2.Synapses(N_input,
neuron,
model=eq_1_nonPlast,
on_pre=eq_2_nonPlast,
method='heun')
# distally clustered ensembles
c_ndx = np.floor(np.random.rand(Nr_clust_dist) * len(dc))
for cc in range(Nr_clust_dist):
Syn_1.connect(i=range(cc * Ens_size, (cc + 1) * Ens_size),
j=neuron[dc[int(c_ndx[cc])]:dc[int(c_ndx[cc])] + 1])
# proximally clustered ensembles
c_ndx2 = np.floor(np.random.rand(Nr_clust_prox) * len(pc))
for cc in range(Nr_clust_prox):
Syn_1.connect(
i=range(cc * Ens_size + Nr_clust_dist * Ens_size,
(cc + 1) * Ens_size + Nr_clust_dist * Ens_size),
j=neuron[pc[int(c_ndx2[cc])]:pc[int(c_ndx2[cc])] + 1])
# distributed ensembles
rand_post_comp = np.floor(
np.random.rand(Ens_size * Nr_scattered) * (len(bsl) - axo - 1) +
axo + 1)
for pp in range(Ens_size * (Nr_clust_dist + Nr_clust_prox), NrIn):
Syn_1.connect(
i=pp,
j=neuron[int(rand_post_comp[pp - Ens_size *
(Nr_clust_dist + Nr_clust_prox)]):
int(rand_post_comp[pp - Ens_size *
(Nr_clust_dist + Nr_clust_prox)] +
1)])
# Initialize the model
neuron.v = EL
neuron.I = 0. * br2.nA
# neuron.I[0] = 0.2*nA
set_init_syn(Syn_1, init_weight)
set_init_nrn(neuron, Theta_low)
N_input.v = V_rest
#####################################################
# Run
#####################################################
print('Simulating ...')
for tt in range(reps * NrGroups):
print(tt)
N_input.rate_v[np.mod(tt, NrGroups) *
GrpSize:np.mod(tt, NrGroups) * GrpSize +
GrpSize] = signal_rate
br2.run(t_stim)
N_input.rate_v = np.zeros(NrIn)
br2.run(buffertime)
print('Simulation Finished!')
#store data in sec[1]
for sec in self.sections.values():
kk = sec[2]
sec[1] = monitor.v[kk:kk + len(sec[0].x)]
# sec[1] = monitor.gKL[kk:kk+len(sec[0].x)]
plt.figure()
plt.plot(monitor.t / br2.ms, monitor.v[0] / br2.mV)
return monitor, neuron
on_pre=eq_2_plastAMPA,
method='heun')
#
#
Syn_4 = Synapses(neuron2,
neuron,
model=eq_1_plastAMPA,
on_pre=eq_2_plastAMPA,
method='heun')
#
for rr in range(NrSyn):
Syn_1.connect(
i=0,
j=neuron2[compv1[int(syn_loc1[rr]
)]:compv1[int(syn_loc1[rr])] + 1])
set_init_syn(Syn_1, init_weight)
#
#
for rr in range(NrSyn):
Syn_4.connect(
i=0,
j=neuron[compv2[int(syn_loc2[rr]
)]:compv2[int(syn_loc2[rr])] + 1])
set_init_syn(Syn_4, init_weight)
#
print('Synapses created...')
#
#####################################################
# Monitors
#####################################################
for ggg in range(len(dendList)):
if dendList[ggg] < nrD:
Syn_1.connect(
i=ggg * nrIn + np.arange(nrIn),
j=neuron[compartments[dendList[ggg]]:compartments[dendList[ggg]] +
1])
else:
print('ERROR, wrong synaptic organisation')
#####################################################
# Initial Values
#####################################################
N_input.v = V_rest
set_init_nrn(neuron, Theta_low)
set_init_syn(Syn_1, init_weight)
print('Synapses created...')
#####################################################
# Monitors
#####################################################
M_post_soma = StateMonitor(neuron.main, ['v'], record=True)
M_post_dend = StateMonitor(neuron, ['v'], record=compartments)
######################################################
## Simulation
######################################################
print('Start ' + morph[12:-4])
t_init = 0
times_mtx = zeros(len(dendList) + 1)