def init_connectivity(self):
# Init connections from contralaterally tuned neurons in the opposite
# hemisphere to ipsilaterally tuned neurons in this hemisphere
left_ec_vis_ei_vis_ampa=init_connection(self.right_lip.e_contra_vis, self.left_lip.e_ipsi_vis, 'g_ampa_r',
self.params.w_ampa_min, self.params.w_ampa_max, self.params.p_ec_vis_ei_vis, delay=20*ms)
#left_ec_ei_nmda=DelayConnection(self.right_lip.e_contra, self.left_lip.e_ipsi, 'g_nmda',
# sparseness=self.params.p_ec_ei, weight=self.params.w_nmda_max, delay=(10*ms, 20*ms))
self.connections.append(left_ec_vis_ei_vis_ampa)
#self.connections.append(left_ec_ei_nmda)
left_ec_mem_ei_mem_ampa=init_connection(self.right_lip.e_contra_mem, self.left_lip.e_ipsi_mem, 'g_ampa_r',
self.params.w_ampa_min, self.params.w_ampa_max, self.params.p_ec_mem_ei_mem, delay=20*ms)
self.connections.append(left_ec_mem_ei_mem_ampa)
# Init connections from contralaterally tuned pyramidal cells in the opposite
# hemisphere to ipsilaterally tuned interneurons in this hemisphere
left_ec_ii_ampa=init_connection(self.right_lip.e_contra, self.left_lip.i_ipsi, 'g_ampa_r',
self.params.w_ampa_min, self.params.w_ampa_max, self.params.p_ec_ii, delay=20*ms)
#left_ec_ii_nmda=DelayConnection(self.right_lip.e_contra, self.left_lip.i_ipsi, 'g_nmda',
# sparseness=self.params.p_ec_ii, weight=self.params.w_nmda_max, delay=(10*ms, 20*ms))
self.connections.append(left_ec_ii_ampa)
#self.connections.append(left_ec_ii_nmda)
# Init connections from contralaterally tuned neurons in the opposite
# hemisphere to ipsilaterally tuned neurons in this hemisphere
right_ec_vis_ei_vis_ampa=init_connection(self.left_lip.e_contra_vis, self.right_lip.e_ipsi_vis, 'g_ampa_r',
self.params.w_ampa_min, self.params.w_ampa_max, self.params.p_ec_vis_ei_vis, delay=20*ms)
#right_ec_ei_nmda=DelayConnection(self.left_lip.e_contra, self.right_lip.e_ipsi, 'g_nmda',
# sparseness=self.params.p_ec_ei, weight=self.params.w_nmda_max, delay=(10*ms, 20*ms))
self.connections.append(right_ec_vis_ei_vis_ampa)
#self.connections.append(right_ec_ei_nmda)
right_ec_mem_ei_mem_ampa=init_connection(self.left_lip.e_contra_mem, self.right_lip.e_ipsi_mem, 'g_ampa_r',
self.params.w_ampa_min, self.params.w_ampa_max, self.params.p_ec_mem_ei_mem, delay=20*ms)
self.connections.append(right_ec_mem_ei_mem_ampa)
# Init connections from contralaterally tuned pyramidal cells in the opposite
# hemisphere to ipsilaterally tuned interneurons in this hemisphere
right_ec_ii_ampa=init_connection(self.left_lip.e_contra, self.right_lip.i_ipsi, 'g_ampa_r',
self.params.w_ampa_min, self.params.w_ampa_max, self.params.p_ec_ii, delay=20*ms)
#right_ec_ii_nmda=DelayConnection(self.left_lip.e_contra, self.right_lip.i_ipsi, 'g_nmda',
# sparseness=self.params.p_ec_ii, weight=self.params.w_nmda_max, delay=(10*ms, 20*ms))
self.connections.append(right_ec_ii_ampa)
#self.connections.append(right_ec_ii_nmda)
self.connections.extend(self.left_lip.connections)
self.connections.extend(self.right_lip.connections)
def init_connectivity(self, clock):
self.connections = {}
self.stdp = {}
# Iterate over input groups
for i in range(self.params.num_groups):
# E population - recurrent connections
self.connections['e%d->e%d_ampa' % (i, i)] = init_connection(
self.groups_e[i],
self.groups_e[i],
'g_ampa_r',
self.pyr_params.w_ampa_rec,
self.params.p_e_e,
delay=.5 * ms,
allow_self_conn=False)
self.connections['e%d->e%d_nmda' % (i, i)] = init_connection(
self.groups_e[i],
self.groups_e[i],
'g_nmda',
self.pyr_params.w_nmda,
self.params.p_e_e,
delay=.5 * ms,
allow_self_conn=False)
# E -> I excitatory connections
self.connections['e->i_ampa'] = init_connection(
self.group_e,
self.group_i,
'g_ampa_r',
self.inh_params.w_ampa_rec,
self.params.p_e_i,
delay=.5 * ms)
self.connections['e->i_nmda'] = init_connection(self.group_e,
self.group_i,
'g_nmda',
self.inh_params.w_nmda,
self.params.p_e_i,
delay=.5 * ms)
# I -> E - inhibitory connections
self.connections['i->e_gabaa'] = init_connection(
self.group_i,
self.group_e,
'g_gaba_a',
self.pyr_params.w_gaba,
self.params.p_i_e,
delay=.5 * ms)
# I population - recurrent connections
self.connections['i->i_gabaa'] = init_connection(
self.group_i,
self.group_i,
'g_gaba_a',
self.inh_params.w_gaba,
self.params.p_i_i,
delay=.5 * ms,
allow_self_conn=False)
if self.background_input is not None:
# Background -> E+I population connections
self.connections['b->ampa'] = DelayConnection(
self.background_input, self, 'g_ampa_b', delay=.5 * ms)
self.connections['b->ampa'][:, :] = 0
for i in xrange(len(self.background_input)):
if i < self.e_size:
self.connections['b->ampa'][i,
i] = self.pyr_params.w_ampa_bak
else:
self.connections['b->ampa'][i,
i] = self.inh_params.w_ampa_bak
if self.task_inputs is not None:
# Task input -> E population connections
for i in range(self.params.num_groups):
self.connections['t%d->e%d_ampa' % (i, i)] = DelayConnection(
self.task_inputs[i], self.groups_e[i], 'g_ampa_x')
self.connections['t%d->e%d_ampa' % (i, i)].connect_one_to_one(
weight=self.pyr_params.w_ampa_ext_correct, delay=.5 * ms)
self.connections['t%d->e%d_ampa' %
(i, 1 - i)] = DelayConnection(
self.task_inputs[i], self.groups_e[1 - i],
'g_ampa_x')
self.connections[
't%d->e%d_ampa' % (i, 1 - i)].connect_one_to_one(
weight=self.pyr_params.w_ampa_ext_incorrect,
delay=.5 * ms)
# Input projections plasticity
self.stdp['stdp%d_%d' % (i, i)] = ExponentialSTDP(
self.connections['t%d->e%d_ampa' % (i, i)],
self.plasticity_params.tau_pre,
self.plasticity_params.tau_post,
self.plasticity_params.dA_pre,
self.plasticity_params.dA_post,
wmax=self.plasticity_params.gmax,
update='additive',
clock=clock)
self.stdp['stdp%d_%d' % (i, 1 - i)] = ExponentialSTDP(
self.connections['t%d->e%d_ampa' % (i, 1 - i)],
self.plasticity_params.tau_pre,
self.plasticity_params.tau_post,
self.plasticity_params.dA_pre,
self.plasticity_params.dA_post,
wmax=self.plasticity_params.gmax,
update='additive',
clock=clock)
def init_connectivity(self):
# Init connections from contralaterally tuned pyramidal cells to other
# contralaterally tuned pyramidal cells in the same hemisphere
self.connections.append(
init_connection(self.e_contra_mem,
self.e_contra_mem,
'g_ampa_r',
self.params.w_ampa_min,
self.params.w_ampa_max,
self.params.p_ec_mem_ec_mem,
delay=5 * ms,
allow_self_conn=False))
self.connections.append(
init_connection(self.e_contra_mem,
self.e_contra_mem,
'g_nmda',
self.params.w_nmda_min,
self.params.w_nmda_max,
self.params.p_ec_mem_ec_mem,
delay=5 * ms,
allow_self_conn=False))
self.connections.append(
init_connection(self.e_contra_vis,
self.e_contra_vis,
'g_ampa_r',
self.params.w_ampa_min,
self.params.w_ampa_max,
self.params.p_ec_vis_ec_vis,
delay=5 * ms,
allow_self_conn=False))
self.connections.append(
init_connection(self.e_contra_vis,
self.e_contra_vis,
'g_nmda',
self.params.w_nmda_min,
self.params.w_nmda_max,
self.params.p_ec_vis_ec_vis,
delay=5 * ms,
allow_self_conn=False))
ec_vis_ec_mem_ampa = init_connection(self.e_contra_vis,
self.e_contra_mem,
'g_ampa_r',
self.params.w_ampa_min,
self.params.w_ampa_max,
self.params.p_ec_vis_ec_mem,
delay=5 * ms)
ec_vis_ec_mem_nmda = init_connection(self.e_contra_vis,
self.e_contra_mem,
'g_nmda',
self.params.w_nmda_min,
self.params.w_nmda_max,
self.params.p_ec_vis_ec_mem,
delay=5 * ms)
self.connections.append(ec_vis_ec_mem_ampa)
self.connections.append(ec_vis_ec_mem_nmda)
# Init connections from ipsilaterally tuned interneurons to contralaterally
# tuned pyramidal cells in the same hemisphere
ii_ec_gabaa = init_connection(self.i_ipsi,
self.e_contra,
'g_gaba_a',
self.params.w_gaba_a_min,
self.params.w_gaba_a_max,
self.params.p_ii_ec,
delay=5 * ms)
ii_ec_gabab = init_connection(self.i_ipsi,
self.e_contra,
'g_gaba_b',
self.params.w_gaba_b_min,
self.params.w_gaba_b_max,
self.params.p_ii_ec,
delay=5 * ms)
self.connections.append(ii_ec_gabaa)
self.connections.append(ii_ec_gabab)
# Init connections from ipsilaterally tuned pyramidal cells to other
# ipsilaterally tuned pyramidal cells in the same hemisphere
self.connections.append(
init_connection(self.e_ipsi_mem,
self.e_ipsi_mem,
'g_ampa_r',
self.params.w_ampa_min,
self.params.w_ampa_max,
self.params.p_ei_mem_ei_mem,
delay=5 * ms,
allow_self_conn=False))
self.connections.append(
init_connection(self.e_ipsi_mem,
self.e_ipsi_mem,
'g_nmda',
self.params.w_nmda_min,
self.params.w_nmda_max,
self.params.p_ei_mem_ei_mem,
delay=5 * ms,
allow_self_conn=False))
self.connections.append(
init_connection(self.e_ipsi_vis,
self.e_ipsi_vis,
'g_ampa_r',
self.params.w_ampa_min,
self.params.w_ampa_max,
self.params.p_ei_vis_ei_vis,
delay=5 * ms,
allow_self_conn=False))
self.connections.append(
init_connection(self.e_ipsi_vis,
self.e_ipsi_vis,
'g_nmda',
self.params.w_nmda_min,
self.params.w_nmda_max,
self.params.p_ei_vis_ei_vis,
delay=5 * ms,
allow_self_conn=False))
ei_vis_ei_mem_ampa = init_connection(self.e_ipsi_vis,
self.e_ipsi_mem,
'g_ampa_r',
self.params.w_ampa_min,
self.params.w_ampa_max,
self.params.p_ei_vis_ei_mem,
delay=5 * ms)
ei_vis_ei_mem_nmda = init_connection(self.e_ipsi_vis,
self.e_ipsi_mem,
'g_nmda',
self.params.w_nmda_min,
self.params.w_nmda_max,
self.params.p_ei_vis_ei_mem,
delay=5 * ms)
self.connections.append(ei_vis_ei_mem_ampa)
self.connections.append(ei_vis_ei_mem_nmda)
# Init connections from contralaterally tuned interneurons to ipsilaterally
# tuned pyramidal cells in the same hemisphere
ic_ei_gabaa = init_connection(self.i_contra,
self.e_ipsi,
'g_gaba_a',
self.params.w_gaba_a_min,
self.params.w_gaba_a_max,
self.params.p_ic_ei,
delay=5 * ms)
ic_ei_gabab = init_connection(self.i_contra,
self.e_ipsi,
'g_gaba_b',
self.params.w_gaba_b_min,
self.params.w_gaba_b_max,
self.params.p_ic_ei,
delay=5 * ms)
self.connections.append(ic_ei_gabaa)
self.connections.append(ic_ei_gabab)
# Init connections from ipsilaterally tuned pyramidal cells to ipsilaterally
# tuned interneurons in the same hemisphere
ei_ii_ampa = init_connection(self.e_ipsi,
self.i_ipsi,
'g_ampa_r',
self.params.w_ampa_min,
self.params.w_ampa_max,
self.params.p_ei_ii,
delay=5 * ms)
#ei_ii_nmda=DelayConnection(self.e_ipsi, self.i_ipsi, 'g_nmda', sparseness=self.params.p_ei_ii,
# weight=self.params.w_nmda_max, delay=delay=5*ms)
#ei_ii_nmda=process_connection(ei_ii_nmda, self.e_ipsi, self.i_ipsi, self.params.w_nmda_min,
# self.params.w_nmda_max)
self.connections.append(ei_ii_ampa)
#self.connections.append(ei_ii_nmda)
# Init connections from contralaterally tuned pyramidal cells to
# contralaterally tuned interneurons in the same hemisphere
ec_ic_ampa = init_connection(self.e_contra,
self.i_contra,
'g_ampa_r',
self.params.w_ampa_min,
self.params.w_ampa_max,
self.params.p_ec_ic,
delay=5 * ms)
#ec_ic_nmda=DelayConnection(self.e_contra, self.i_contra, 'g_nmda', sparseness=self.params.p_ec_ic,
# weight=self.params.w_nmda_max, delay=delay=5*ms)
#ec_ic_nmda=process_connection(ec_ic_nmda, self.e_contra, self.i_contra, self.params.w_nmda_min,
# self.params.w_nmda_max)
self.connections.append(ec_ic_ampa)
def init_connectivity(self):
# Init connections from contralaterally tuned neurons in the opposite
# hemisphere to ipsilaterally tuned neurons in this hemisphere
left_ec_vis_ei_vis_ampa = init_connection(self.right_lip.e_contra_vis,
self.left_lip.e_ipsi_vis,
'g_ampa_r',
self.params.w_ampa_min,
self.params.w_ampa_max,
self.params.p_ec_vis_ei_vis,
delay=20 * ms)
#left_ec_ei_nmda=DelayConnection(self.right_lip.e_contra, self.left_lip.e_ipsi, 'g_nmda',
# sparseness=self.params.p_ec_ei, weight=self.params.w_nmda_max, delay=(10*ms, 20*ms))
self.connections.append(left_ec_vis_ei_vis_ampa)
#self.connections.append(left_ec_ei_nmda)
left_ec_mem_ei_mem_ampa = init_connection(self.right_lip.e_contra_mem,
self.left_lip.e_ipsi_mem,
'g_ampa_r',
self.params.w_ampa_min,
self.params.w_ampa_max,
self.params.p_ec_mem_ei_mem,
delay=20 * ms)
self.connections.append(left_ec_mem_ei_mem_ampa)
# Init connections from contralaterally tuned pyramidal cells in the opposite
# hemisphere to ipsilaterally tuned interneurons in this hemisphere
left_ec_ii_ampa = init_connection(self.right_lip.e_contra,
self.left_lip.i_ipsi,
'g_ampa_r',
self.params.w_ampa_min,
self.params.w_ampa_max,
self.params.p_ec_ii,
delay=20 * ms)
#left_ec_ii_nmda=DelayConnection(self.right_lip.e_contra, self.left_lip.i_ipsi, 'g_nmda',
# sparseness=self.params.p_ec_ii, weight=self.params.w_nmda_max, delay=(10*ms, 20*ms))
self.connections.append(left_ec_ii_ampa)
#self.connections.append(left_ec_ii_nmda)
# Init connections from contralaterally tuned neurons in the opposite
# hemisphere to ipsilaterally tuned neurons in this hemisphere
right_ec_vis_ei_vis_ampa = init_connection(self.left_lip.e_contra_vis,
self.right_lip.e_ipsi_vis,
'g_ampa_r',
self.params.w_ampa_min,
self.params.w_ampa_max,
self.params.p_ec_vis_ei_vis,
delay=20 * ms)
#right_ec_ei_nmda=DelayConnection(self.left_lip.e_contra, self.right_lip.e_ipsi, 'g_nmda',
# sparseness=self.params.p_ec_ei, weight=self.params.w_nmda_max, delay=(10*ms, 20*ms))
self.connections.append(right_ec_vis_ei_vis_ampa)
#self.connections.append(right_ec_ei_nmda)
right_ec_mem_ei_mem_ampa = init_connection(self.left_lip.e_contra_mem,
self.right_lip.e_ipsi_mem,
'g_ampa_r',
self.params.w_ampa_min,
self.params.w_ampa_max,
self.params.p_ec_mem_ei_mem,
delay=20 * ms)
self.connections.append(right_ec_mem_ei_mem_ampa)
# Init connections from contralaterally tuned pyramidal cells in the opposite
# hemisphere to ipsilaterally tuned interneurons in this hemisphere
right_ec_ii_ampa = init_connection(self.left_lip.e_contra,
self.right_lip.i_ipsi,
'g_ampa_r',
self.params.w_ampa_min,
self.params.w_ampa_max,
self.params.p_ec_ii,
delay=20 * ms)
#right_ec_ii_nmda=DelayConnection(self.left_lip.e_contra, self.right_lip.i_ipsi, 'g_nmda',
# sparseness=self.params.p_ec_ii, weight=self.params.w_nmda_max, delay=(10*ms, 20*ms))
self.connections.append(right_ec_ii_ampa)
#self.connections.append(right_ec_ii_nmda)
self.connections.extend(self.left_lip.connections)
self.connections.extend(self.right_lip.connections)
def __init__(self,
lip_size,
params,
background_inputs=None,
visual_cortex_input=None,
go_input=None):
self.lip_size = lip_size
self.N = 2 * self.lip_size
self.params = params
self.background_inputs = background_inputs
self.visual_cortex_input = visual_cortex_input
self.go_input = go_input
## Set up equations
# Exponential integrate-and-fire neuron
eqs = exp_IF(params.C, params.gL, params.EL, params.VT, params.DeltaT)
# AMPA conductance - recurrent input current
eqs += exp_synapse('g_ampa_r', params.tau_ampa, siemens)
eqs += Current('I_ampa_r=g_ampa_r*(E-vm): amp', E=params.E_ampa)
# AMPA conductance - background input current
eqs += exp_synapse('g_ampa_b', params.tau_ampa, siemens)
eqs += Current('I_ampa_b=g_ampa_b*(E-vm): amp', E=params.E_ampa)
# AMPA conductance - task input current
eqs += exp_synapse('g_ampa_x', params.tau_ampa, siemens)
eqs += Current('I_ampa_x=g_ampa_x*(E-vm): amp', E=params.E_ampa)
# AMPA conductance - go input current
eqs += exp_synapse('g_ampa_g', params.tau_ampa, siemens)
eqs += Current('I_ampa_g=g_ampa_g*(E-vm): amp', E=params.E_ampa)
# Voltage-dependent NMDA conductance
eqs += biexp_synapse('g_nmda', params.tau1_nmda, params.tau2_nmda,
siemens)
eqs += Equations('g_V = 1/(1+(Mg/3.57)*exp(-0.062 *vm/mV)) : 1 ',
Mg=params.Mg)
eqs += Current('I_nmda=g_V*g_nmda*(E-vm): amp', E=params.E_nmda)
# GABA-A conductance
eqs += exp_synapse('g_gaba_a', params.tau_gaba_a, siemens)
eqs += Current('I_gaba_a=g_gaba_a*(E-vm): amp', E=params.E_gaba_a)
# GABA-B conductance
eqs += biexp_synapse('g_gaba_b', params.tau1_gaba_b,
params.tau2_gaba_b, siemens)
eqs += Current('I_gaba_b=g_gaba_b*(E-vm): amp', E=params.E_gaba_b)
# Total synaptic conductance
eqs += Equations(
'g_syn=g_ampa_r+g_ampa_x+g_ampa_g+g_ampa_b+g_V*g_nmda+g_gaba_a+g_gaba_b : siemens'
)
eqs += Equations(
'g_syn_exc=g_ampa_r+g_ampa_x+g_ampa_g+g_ampa_b+g_V*g_nmda : siemens'
)
# Total synaptic current
eqs += Equations(
'I_abs=abs(I_ampa_r)+abs(I_ampa_b)+abs(I_ampa_x)+abs(I_ampa_g)+abs(I_nmda)+abs(I_gaba_a) : amp'
)
NeuronGroup.__init__(self,
self.N,
model=eqs,
threshold=-20 * mV,
reset=params.EL,
compile=True)
self.init_subpopulations()
self.connections = []
self.init_connectivity()
if self.background_inputs is not None:
# Background -> E+I population connections
background_left_ampa = init_connection(self.background_inputs[0],
self.left_lip.neuron_group,
'g_ampa_b',
self.params.w_ampa_min,
self.params.w_ampa_max,
self.params.p_b_e,
delay=5 * ms)
background_right_ampa = init_connection(
self.background_inputs[1],
self.right_lip.neuron_group,
'g_ampa_b',
self.params.w_ampa_min,
self.params.w_ampa_max,
self.params.p_b_e,
delay=5 * ms)
self.connections.append(background_left_ampa)
self.connections.append(background_right_ampa)
if self.visual_cortex_input is not None:
# Task input -> E population connections
vc_left_lip_ampa = init_connection(self.visual_cortex_input[0],
self.left_lip.e_contra_vis,
'g_ampa_x',
self.params.w_ampa_min,
self.params.w_ampa_max,
self.params.p_v_ec_vis,
delay=270 * ms)
vc_right_lip_ampa = init_connection(self.visual_cortex_input[1],
self.right_lip.e_contra_vis,
'g_ampa_x',
self.params.w_ampa_min,
self.params.w_ampa_max,
self.params.p_v_ec_vis,
delay=270 * ms)
self.connections.append(vc_left_lip_ampa)
self.connections.append(vc_right_lip_ampa)
if self.go_input is not None:
go_left_lip_i_ampa = init_connection(self.go_input,
self.left_lip.i_group,
'g_ampa_g',
self.params.w_ampa_min,
self.params.w_ampa_max,
self.params.p_g_i,
delay=5 * ms)
go_right_lip_i_ampa = init_connection(self.go_input,
self.right_lip.i_group,
'g_ampa_g',
self.params.w_ampa_min,
self.params.w_ampa_max,
self.params.p_g_i,
delay=5 * ms)
go_left_lip_e_ampa = init_connection(self.go_input,
self.left_lip.e_group,
'g_ampa_g',
self.params.w_ampa_min,
self.params.w_ampa_max,
self.params.p_g_e,
delay=5 * ms)
go_right_lip_e_ampa = init_connection(self.go_input,
self.right_lip.e_group,
'g_ampa_g',
self.params.w_ampa_min,
self.params.w_ampa_max,
self.params.p_g_e,
delay=5 * ms)
self.connections.append(go_left_lip_i_ampa)
self.connections.append(go_right_lip_i_ampa)
self.connections.append(go_left_lip_e_ampa)
self.connections.append(go_right_lip_e_ampa)
def init_connectivity(self):
# Init connections from contralaterally tuned pyramidal cells to other
# contralaterally tuned pyramidal cells in the same hemisphere
self.connections.append(init_connection(self.e_contra_mem, self.e_contra_mem, 'g_ampa_r', self.params.w_ampa_min,
self.params.w_ampa_max, self.params.p_ec_mem_ec_mem, delay=5*ms, allow_self_conn=False))
self.connections.append(init_connection(self.e_contra_mem, self.e_contra_mem, 'g_nmda', self.params.w_nmda_min,
self.params.w_nmda_max, self.params.p_ec_mem_ec_mem, delay=5*ms, allow_self_conn=False))
self.connections.append(init_connection(self.e_contra_vis, self.e_contra_vis, 'g_ampa_r', self.params.w_ampa_min,
self.params.w_ampa_max, self.params.p_ec_vis_ec_vis, delay=5*ms, allow_self_conn=False))
self.connections.append(init_connection(self.e_contra_vis, self.e_contra_vis, 'g_nmda', self.params.w_nmda_min,
self.params.w_nmda_max, self.params.p_ec_vis_ec_vis, delay=5*ms, allow_self_conn=False))
ec_vis_ec_mem_ampa=init_connection(self.e_contra_vis, self.e_contra_mem, 'g_ampa_r', self.params.w_ampa_min,
self.params.w_ampa_max, self.params.p_ec_vis_ec_mem, delay=5*ms)
ec_vis_ec_mem_nmda=init_connection(self.e_contra_vis, self.e_contra_mem, 'g_nmda', self.params.w_nmda_min,
self.params.w_nmda_max, self.params.p_ec_vis_ec_mem, delay=5*ms)
self.connections.append(ec_vis_ec_mem_ampa)
self.connections.append(ec_vis_ec_mem_nmda)
# Init connections from ipsilaterally tuned interneurons to contralaterally
# tuned pyramidal cells in the same hemisphere
ii_ec_gabaa=init_connection(self.i_ipsi, self.e_contra, 'g_gaba_a', self.params.w_gaba_a_min,
self.params.w_gaba_a_max, self.params.p_ii_ec, delay=5*ms)
ii_ec_gabab=init_connection(self.i_ipsi, self.e_contra, 'g_gaba_b', self.params.w_gaba_b_min,
self.params.w_gaba_b_max, self.params.p_ii_ec, delay=5*ms)
self.connections.append(ii_ec_gabaa)
self.connections.append(ii_ec_gabab)
# Init connections from ipsilaterally tuned pyramidal cells to other
# ipsilaterally tuned pyramidal cells in the same hemisphere
self.connections.append(init_connection(self.e_ipsi_mem, self.e_ipsi_mem, 'g_ampa_r', self.params.w_ampa_min,
self.params.w_ampa_max, self.params.p_ei_mem_ei_mem, delay=5*ms, allow_self_conn=False))
self.connections.append(init_connection(self.e_ipsi_mem, self.e_ipsi_mem, 'g_nmda', self.params.w_nmda_min,
self.params.w_nmda_max, self.params.p_ei_mem_ei_mem, delay=5*ms, allow_self_conn=False))
self.connections.append(init_connection(self.e_ipsi_vis, self.e_ipsi_vis, 'g_ampa_r', self.params.w_ampa_min,
self.params.w_ampa_max, self.params.p_ei_vis_ei_vis, delay=5*ms, allow_self_conn=False))
self.connections.append(init_connection(self.e_ipsi_vis, self.e_ipsi_vis, 'g_nmda', self.params.w_nmda_min,
self.params.w_nmda_max, self.params.p_ei_vis_ei_vis, delay=5*ms, allow_self_conn=False))
ei_vis_ei_mem_ampa=init_connection(self.e_ipsi_vis, self.e_ipsi_mem, 'g_ampa_r', self.params.w_ampa_min,
self.params.w_ampa_max, self.params.p_ei_vis_ei_mem, delay=5*ms)
ei_vis_ei_mem_nmda=init_connection(self.e_ipsi_vis, self.e_ipsi_mem, 'g_nmda', self.params.w_nmda_min,
self.params.w_nmda_max, self.params.p_ei_vis_ei_mem, delay=5*ms)
self.connections.append(ei_vis_ei_mem_ampa)
self.connections.append(ei_vis_ei_mem_nmda)
# Init connections from contralaterally tuned interneurons to ipsilaterally
# tuned pyramidal cells in the same hemisphere
ic_ei_gabaa=init_connection(self.i_contra, self.e_ipsi, 'g_gaba_a', self.params.w_gaba_a_min,
self.params.w_gaba_a_max, self.params.p_ic_ei, delay=5*ms)
ic_ei_gabab=init_connection(self.i_contra, self.e_ipsi, 'g_gaba_b', self.params.w_gaba_b_min,
self.params.w_gaba_b_max, self.params.p_ic_ei, delay=5*ms)
self.connections.append(ic_ei_gabaa)
self.connections.append(ic_ei_gabab)
# Init connections from ipsilaterally tuned pyramidal cells to ipsilaterally
# tuned interneurons in the same hemisphere
ei_ii_ampa=init_connection(self.e_ipsi, self.i_ipsi, 'g_ampa_r', self.params.w_ampa_min,
self.params.w_ampa_max, self.params.p_ei_ii, delay=5*ms)
#ei_ii_nmda=DelayConnection(self.e_ipsi, self.i_ipsi, 'g_nmda', sparseness=self.params.p_ei_ii,
# weight=self.params.w_nmda_max, delay=delay=5*ms)
#ei_ii_nmda=process_connection(ei_ii_nmda, self.e_ipsi, self.i_ipsi, self.params.w_nmda_min,
# self.params.w_nmda_max)
self.connections.append(ei_ii_ampa)
#self.connections.append(ei_ii_nmda)
# Init connections from contralaterally tuned pyramidal cells to
# contralaterally tuned interneurons in the same hemisphere
ec_ic_ampa=init_connection(self.e_contra, self.i_contra, 'g_ampa_r', self.params.w_ampa_min,
self.params.w_ampa_max, self.params.p_ec_ic, delay=5*ms)
#ec_ic_nmda=DelayConnection(self.e_contra, self.i_contra, 'g_nmda', sparseness=self.params.p_ec_ic,
# weight=self.params.w_nmda_max, delay=delay=5*ms)
#ec_ic_nmda=process_connection(ec_ic_nmda, self.e_contra, self.i_contra, self.params.w_nmda_min,
# self.params.w_nmda_max)
self.connections.append(ec_ic_ampa)
def __init__(self, lip_size, params, background_inputs=None, visual_cortex_input=None,
go_input=None):
self.lip_size=lip_size
self.N=2*self.lip_size
self.params=params
self.background_inputs=background_inputs
self.visual_cortex_input=visual_cortex_input
self.go_input=go_input
## Set up equations
# Exponential integrate-and-fire neuron
eqs = exp_IF(params.C, params.gL, params.EL, params.VT, params.DeltaT)
# AMPA conductance - recurrent input current
eqs += exp_synapse('g_ampa_r', params.tau_ampa, siemens)
eqs += Current('I_ampa_r=g_ampa_r*(E-vm): amp', E=params.E_ampa)
# AMPA conductance - background input current
eqs += exp_synapse('g_ampa_b', params.tau_ampa, siemens)
eqs += Current('I_ampa_b=g_ampa_b*(E-vm): amp', E=params.E_ampa)
# AMPA conductance - task input current
eqs += exp_synapse('g_ampa_x', params.tau_ampa, siemens)
eqs += Current('I_ampa_x=g_ampa_x*(E-vm): amp', E=params.E_ampa)
# AMPA conductance - go input current
eqs += exp_synapse('g_ampa_g', params.tau_ampa, siemens)
eqs += Current('I_ampa_g=g_ampa_g*(E-vm): amp', E=params.E_ampa)
# Voltage-dependent NMDA conductance
eqs += biexp_synapse('g_nmda', params.tau1_nmda, params.tau2_nmda, siemens)
eqs += Equations('g_V = 1/(1+(Mg/3.57)*exp(-0.062 *vm/mV)) : 1 ', Mg=params.Mg)
eqs += Current('I_nmda=g_V*g_nmda*(E-vm): amp', E=params.E_nmda)
# GABA-A conductance
eqs += exp_synapse('g_gaba_a', params.tau_gaba_a, siemens)
eqs += Current('I_gaba_a=g_gaba_a*(E-vm): amp', E=params.E_gaba_a)
# GABA-B conductance
eqs += biexp_synapse('g_gaba_b', params.tau1_gaba_b, params.tau2_gaba_b, siemens)
eqs += Current('I_gaba_b=g_gaba_b*(E-vm): amp', E=params.E_gaba_b)
# Total synaptic conductance
eqs += Equations('g_syn=g_ampa_r+g_ampa_x+g_ampa_g+g_ampa_b+g_V*g_nmda+g_gaba_a+g_gaba_b : siemens')
eqs += Equations('g_syn_exc=g_ampa_r+g_ampa_x+g_ampa_g+g_ampa_b+g_V*g_nmda : siemens')
# Total synaptic current
eqs += Equations('I_abs=abs(I_ampa_r)+abs(I_ampa_b)+abs(I_ampa_x)+abs(I_ampa_g)+abs(I_nmda)+abs(I_gaba_a) : amp')
NeuronGroup.__init__(self, self.N, model=eqs, threshold=-20*mV, reset=params.EL, compile=True)
self.init_subpopulations()
self.connections=[]
self.init_connectivity()
if self.background_inputs is not None:
# Background -> E+I population connections
background_left_ampa=init_connection(self.background_inputs[0], self.left_lip.neuron_group, 'g_ampa_b',
self.params.w_ampa_min, self.params.w_ampa_max, self.params.p_b_e, delay=5*ms)
background_right_ampa=init_connection(self.background_inputs[1], self.right_lip.neuron_group, 'g_ampa_b',
self.params.w_ampa_min, self.params.w_ampa_max, self.params.p_b_e, delay=5*ms)
self.connections.append(background_left_ampa)
self.connections.append(background_right_ampa)
if self.visual_cortex_input is not None:
# Task input -> E population connections
vc_left_lip_ampa=init_connection(self.visual_cortex_input[0], self.left_lip.e_contra_vis, 'g_ampa_x',
self.params.w_ampa_min, self.params.w_ampa_max, self.params.p_v_ec_vis, delay=270*ms)
vc_right_lip_ampa=init_connection(self.visual_cortex_input[1], self.right_lip.e_contra_vis, 'g_ampa_x',
self.params.w_ampa_min, self.params.w_ampa_max, self.params.p_v_ec_vis, delay=270*ms)
self.connections.append(vc_left_lip_ampa)
self.connections.append(vc_right_lip_ampa)
if self.go_input is not None:
go_left_lip_i_ampa=init_connection(self.go_input, self.left_lip.i_group, 'g_ampa_g', self.params.w_ampa_min,
self.params.w_ampa_max, self.params.p_g_i, delay=5*ms)
go_right_lip_i_ampa=init_connection(self.go_input, self.right_lip.i_group, 'g_ampa_g', self.params.w_ampa_min,
self.params.w_ampa_max, self.params.p_g_i, delay=5*ms)
go_left_lip_e_ampa=init_connection(self.go_input, self.left_lip.e_group, 'g_ampa_g', self.params.w_ampa_min,
self.params.w_ampa_max, self.params.p_g_e, delay=5*ms)
go_right_lip_e_ampa=init_connection(self.go_input, self.right_lip.e_group, 'g_ampa_g', self.params.w_ampa_min,
self.params.w_ampa_max, self.params.p_g_e, delay=5*ms)
self.connections.append(go_left_lip_i_ampa)
self.connections.append(go_right_lip_i_ampa)
self.connections.append(go_left_lip_e_ampa)
self.connections.append(go_right_lip_e_ampa)
def init_connectivity(self, clock):
self.connections = {}
self.stdp = {}
# Iterate over input groups
for i in range(self.params.num_groups):
# E population - recurrent connections
self.connections["e%d->e%d_ampa" % (i, i)] = init_connection(
self.groups_e[i],
self.groups_e[i],
"g_ampa_r",
self.pyr_params.w_ampa_rec,
self.params.p_e_e,
delay=0.5 * ms,
allow_self_conn=False,
)
self.connections["e%d->e%d_nmda" % (i, i)] = init_connection(
self.groups_e[i],
self.groups_e[i],
"g_nmda",
self.pyr_params.w_nmda,
self.params.p_e_e,
delay=0.5 * ms,
allow_self_conn=False,
)
# E -> I excitatory connections
self.connections["e->i_ampa"] = init_connection(
self.group_e, self.group_i, "g_ampa_r", self.inh_params.w_ampa_rec, self.params.p_e_i, delay=0.5 * ms
)
self.connections["e->i_nmda"] = init_connection(
self.group_e, self.group_i, "g_nmda", self.inh_params.w_nmda, self.params.p_e_i, delay=0.5 * ms
)
# I -> E - inhibitory connections
self.connections["i->e_gabaa"] = init_connection(
self.group_i, self.group_e, "g_gaba_a", self.pyr_params.w_gaba, self.params.p_i_e, delay=0.5 * ms
)
# I population - recurrent connections
self.connections["i->i_gabaa"] = init_connection(
self.group_i,
self.group_i,
"g_gaba_a",
self.inh_params.w_gaba,
self.params.p_i_i,
delay=0.5 * ms,
allow_self_conn=False,
)
if self.background_input is not None:
# Background -> E+I population connections
self.connections["b->ampa"] = DelayConnection(self.background_input, self, "g_ampa_b", delay=0.5 * ms)
self.connections["b->ampa"][:, :] = 0
for i in xrange(len(self.background_input)):
if i < self.e_size:
self.connections["b->ampa"][i, i] = self.pyr_params.w_ampa_bak
else:
self.connections["b->ampa"][i, i] = self.inh_params.w_ampa_bak
if self.task_inputs is not None:
# Task input -> E population connections
for i in range(self.params.num_groups):
self.connections["t%d->e%d_ampa" % (i, i)] = DelayConnection(
self.task_inputs[i], self.groups_e[i], "g_ampa_x"
)
self.connections["t%d->e%d_ampa" % (i, i)].connect_one_to_one(
weight=self.pyr_params.w_ampa_ext_correct, delay=0.5 * ms
)
self.connections["t%d->e%d_ampa" % (i, 1 - i)] = DelayConnection(
self.task_inputs[i], self.groups_e[1 - i], "g_ampa_x"
)
self.connections["t%d->e%d_ampa" % (i, 1 - i)].connect_one_to_one(
weight=self.pyr_params.w_ampa_ext_incorrect, delay=0.5 * ms
)
# Input projections plasticity
self.stdp["stdp%d_%d" % (i, i)] = ExponentialSTDP(
self.connections["t%d->e%d_ampa" % (i, i)],
self.plasticity_params.tau_pre,
self.plasticity_params.tau_post,
self.plasticity_params.dA_pre,
self.plasticity_params.dA_post,
wmax=self.plasticity_params.gmax,
update="additive",
clock=clock,
)
self.stdp["stdp%d_%d" % (i, 1 - i)] = ExponentialSTDP(
self.connections["t%d->e%d_ampa" % (i, 1 - i)],
self.plasticity_params.tau_pre,
self.plasticity_params.tau_post,
self.plasticity_params.dA_pre,
self.plasticity_params.dA_post,
wmax=self.plasticity_params.gmax,
update="additive",
clock=clock,
)
