def __init__(self, filterbank, targetvar, *args, **kwds):
self.targetvar = targetvar
self.filterbank = filterbank
filterbank.buffer_init()
# update level keyword
kwds['level'] = kwds.get('level', 0)+1
# Sanitize the clock - does it have the right dt value?
if 'clock' in kwds:
if int(1/kwds['clock'].dt)!=int(filterbank.samplerate):
raise ValueError('Clock should have 1/dt=samplerate')
else:
kwds['clock'] = Clock(dt=1/filterbank.samplerate)
buffersize = kwds.pop('buffersize', 32)
if not isinstance(buffersize, int):
buffersize = int(buffersize*self.samplerate)
self.buffersize = buffersize
self.buffer_pointer = buffersize
self.buffer_start = -buffersize
NeuronGroup.__init__(self, filterbank.nchannels, *args, **kwds)
@network_operation(when='start', clock=self.clock)
def apply_filterbank_output():
if self.buffer_pointer>=self.buffersize:
self.buffer_pointer = 0
self.buffer_start += self.buffersize
self.buffer = self.filterbank.buffer_fetch(self.buffer_start, self.buffer_start+self.buffersize)
setattr(self, targetvar, self.buffer[self.buffer_pointer, :])
self.buffer_pointer += 1
self.contained_objects.append(apply_filterbank_output)
def __init__(self, params=zheng_params, network=None):
eqs=Equations('''
G_total : siemens
G_total_exc : siemens
cmr_o : 1
cb : 1
g : 1
c_ab : 1
cb_0 : 1
g_0 : 1
''')
NeuronGroup.__init__(self, 1, model=eqs, compile=True, freeze=True)
self.params=params
self.c_ab=self.params.c_ab
self.cb_0=self.params.cb_0
self.g_0=self.params.g_0
self.cb=self.cb_0
self.g=self.g_0
if network is not None:
self.G_total = linked_var(network, 'g_syn', func=sum)
self.G_total_exc = linked_var(network, 'g_syn_exc', func=sum)
def __init__(self, filterbank, targetvar, *args, **kwds):
self.targetvar = targetvar
self.filterbank = filterbank
filterbank.buffer_init()
# update level keyword
kwds['level'] = kwds.get('level', 0) + 1
# Sanitize the clock - does it have the right dt value?
if 'clock' in kwds:
if int(1 / kwds['clock'].dt) != int(filterbank.samplerate):
raise ValueError('Clock should have 1/dt=samplerate')
else:
kwds['clock'] = Clock(dt=1 / filterbank.samplerate)
buffersize = kwds.pop('buffersize', 32)
if not isinstance(buffersize, int):
buffersize = int(buffersize * self.samplerate)
self.buffersize = buffersize
self.buffer_pointer = buffersize
self.buffer_start = -buffersize
NeuronGroup.__init__(self, filterbank.nchannels, *args, **kwds)
@network_operation(when='start', clock=self.clock)
def apply_filterbank_output():
if self.buffer_pointer >= self.buffersize:
self.buffer_pointer = 0
self.buffer_start += self.buffersize
self.buffer = self.filterbank.buffer_fetch(
self.buffer_start, self.buffer_start + self.buffersize)
setattr(self, targetvar, self.buffer[self.buffer_pointer, :])
self.buffer_pointer += 1
self.contained_objects.append(apply_filterbank_output)
def __init__(self, params=zheng_params, network=None):
eqs = Equations('''
G_total : siemens
G_total_exc : siemens
cmr_o : 1
cb : 1
g : 1
c_ab : 1
cb_0 : 1
g_0 : 1
''')
NeuronGroup.__init__(self, 1, model=eqs, compile=True, freeze=True)
self.params = params
self.c_ab = self.params.c_ab
self.cb_0 = self.params.cb_0
self.g_0 = self.params.g_0
self.cb = self.cb_0
self.g = self.g_0
if network is not None:
self.G_total = linked_var(network, 'g_syn', func=sum)
self.G_total_exc = linked_var(network, 'g_syn_exc', func=sum)
def __init__(self, pyramidal_group, clock=defaultclock):
eqs = Equations('''
LFP : amp
''')
NeuronGroup.__init__(self,
1,
model=eqs,
compile=True,
freeze=True,
clock=clock)
self.LFP = linked_var(pyramidal_group, 'I_abs', func=sum)
def __init__(self, clock, params=default_params, network=None):
eqs = Equations('''
G_total : siemens
G_total_exc : siemens
ds/dt=eta*(G_total-G_base)/G_base-s/tau_s-(f_in-1.0)/tau_f : 1
df_in/dt=s/second : 1
dv/dt=1/tau_o*(f_in-f_out) : 1
f_out=v**(1.0/alpha) : 1
o_e=1-(1-e_base)**(1/f_in) : 1
dq/dt=1/tau_o*((f_in*o_e/e_base)-f_out*q/v) : 1
y=v_base*((k1+k2)*(1-q)-(k2+k3)*(1-v)) : 1
G_base : siemens
eta : 1/second
tau_s : second
tau_f : second
alpha : 1
tau_o : second
e_base : 1
v_base : 1
k1 : 1
k2 : 1
k3 : 1
''')
NeuronGroup.__init__(self,
1,
model=eqs,
clock=clock,
compile=True,
freeze=True)
self.params = params
self.G_base = params.G_base
self.eta = params.eta
self.tau_s = params.tau_s
self.tau_f = params.tau_f
self.alpha = params.alpha
self.tau_o = params.tau_o
self.e_base = params.e_base
self.v_base = params.v_base
self.k1 = params.k1
self.params.s_e = params.s_e_0 * exp(-params.TE / params.T_2E)
self.params.s_i = params.s_i_0 * exp(-params.TE / params.T_2I)
self.params.beta = self.params.s_e / self.params.s_i
self.k2 = self.params.beta * params.r_0 * self.e_base * params.TE
self.k3 = self.params.beta - 1
self.f_in = 1
self.s = 0
self.v = 1
self.q = 1
if network is not None:
self.G_total = linked_var(network, 'g_syn', func=sum)
self.G_total_exc = linked_var(network, 'g_syn_exc', func=sum)
def __init__(self, clock, params=default_params, network=None):
eqs=Equations('''
G_total : siemens
G_total_exc : siemens
ds/dt=eta*(G_total-G_base)/G_base-s/tau_s-(f_in-1.0)/tau_f : 1
df_in/dt=s/second : 1
dv/dt=1/tau_o*(f_in-f_out) : 1
f_out=v**(1.0/alpha) : 1
o_e=1-(1-e_base)**(1/f_in) : 1
dq/dt=1/tau_o*((f_in*o_e/e_base)-f_out*q/v) : 1
y=v_base*((k1+k2)*(1-q)-(k2+k3)*(1-v)) : 1
G_base : siemens
eta : 1/second
tau_s : second
tau_f : second
alpha : 1
tau_o : second
e_base : 1
v_base : 1
k1 : 1
k2 : 1
k3 : 1
''')
NeuronGroup.__init__(self, 1, model=eqs, clock=clock, compile=True, freeze=True)
self.params=params
self.G_base=params.G_base
self.eta=params.eta
self.tau_s=params.tau_s
self.tau_f=params.tau_f
self.alpha=params.alpha
self.tau_o=params.tau_o
self.e_base=params.e_base
self.v_base=params.v_base
self.k1=params.k1
self.params.s_e=params.s_e_0*exp(-params.TE/params.T_2E)
self.params.s_i=params.s_i_0*exp(-params.TE/params.T_2I)
self.params.beta=self.params.s_e/self.params.s_i
self.k2=self.params.beta*params.r_0*self.e_base*params.TE
self.k3=self.params.beta-1
self.f_in=1
self.s=0
self.v=1
self.q=1
if network is not None:
self.G_total = linked_var(network, 'g_syn', func=sum)
self.G_total_exc = linked_var(network, 'g_syn_exc', func=sum)
def __init__(self, pyramidal_group, clock=defaultclock):
eqs=Equations('''
LFP : amp
''')
NeuronGroup.__init__(self, 1, model=eqs, compile=True, freeze=True, clock=clock)
self.LFP=linked_var(pyramidal_group, 'I_abs', func=sum)
def __init__(self, clock, params=zheng_params, network=None):
eqs=Equations('''
G_total : siemens
G_total_exc : siemens
ds/dt=eta*(G_total-G_base)/G_base-s/tau_s-(f_in-1.0)/tau_f : 1
df_in/dt=s/second : 1.0
dv/dt=1/tau_o*(f_in-f_out) : 1
f_out=v**(1.0/alpha) : 1
do_e/dt=1.0/(phi/f_in)*(-o_e+(1.0-g)*(1.0-(1.0-e_base/(1.0-g_0))**(1.0/f_in))) : %.4f
dcb/dt=1.0/(phi/f_in)*(-cb-(c_ab*o_e)/oe_log+c_ab*g) : 1
oe_log : 1
cmr_o=(cb-g*c_ab)/(cb_0-g_0*c_ab) : 1
dg/dt=1.0/(j*v_ratio*((r*transitTime)/e_base))*((cmr_o-1.0)-k*s) : %.4f
dq/dt=1/tau_o*((f_in*o_e/e_base)-f_out*q/v) : 1
y=v_0*((k1+k2)*(1-q)-(k2+k3)*(1-v)) : 1
G_base : siemens
eta : 1/second
tau_s : second
tau_f : second
alpha : 1
tau_o : second
v_0 : 1
k1 : 1
k2 : 1
k3 : 1
phi : %.4f*second
e_base : %.4f
g_0 : %.4f
c_ab : 1
cb_0 : 1
v_ratio : 1
j : 1
transitTime : second
k : 1
r : 1
''' % (params.e_base, params.g_0, params.phi, params.e_base, params.g_0))
NeuronGroup.__init__(self, 1, model=eqs, clock=clock, compile=True, freeze=True)
self.params=params
self.G_base=params.G_base
self.eta=params.eta
self.tau_s=params.tau_s
self.tau_f=params.tau_f
self.alpha=params.alpha
self.tau_o=params.tau_o
self.e_base=params.e_base
self.v_0=params.v_0
self.k1=params.k1
self.params.s_e=params.s_e_0*exp(-params.TE/params.T_2E)
self.params.s_i=params.s_i_0*exp(-params.TE/params.T_2I)
self.params.beta=self.params.s_e/self.params.s_i
self.k2=self.params.beta*params.r_0*self.e_base*params.TE
self.k3=self.params.beta-1.0
self.c_ab=self.params.c_ab
self.cb_0=self.params.cb_0
self.g_0=self.params.g_0
self.phi=self.params.phi
self.v_ratio=self.params.v_ratio
self.j=self.params.j
self.transitTime=self.params.transitTime
self.k=self.params.k
self.r=self.params.r
self.f_in=1.0
self.s=0.0
self.v=1.0
self.o_e=self.e_base
self.cb=self.cb_0
self.g=self.g_0
self.oe_log=np.log(1.0-self.o_e/(1.0-self.g))
self.q=1.0
if network is not None:
self.G_total = linked_var(network, 'g_syn', func=sum)
self.G_total_exc = linked_var(network, 'g_syn_exc', func=sum)
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__(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__(self, clock, params=zheng_params, network=None):
eqs = Equations(
'''
G_total : siemens
G_total_exc : siemens
ds/dt=eta*(G_total-G_base)/G_base-s/tau_s-(f_in-1.0)/tau_f : 1
df_in/dt=s/second : 1.0
dv/dt=1/tau_o*(f_in-f_out) : 1
f_out=v**(1.0/alpha) : 1
do_e/dt=1.0/(phi/f_in)*(-o_e+(1.0-g)*(1.0-(1.0-e_base/(1.0-g_0))**(1.0/f_in))) : %.4f
dcb/dt=1.0/(phi/f_in)*(-cb-(c_ab*o_e)/oe_log+c_ab*g) : 1
oe_log : 1
cmr_o=(cb-g*c_ab)/(cb_0-g_0*c_ab) : 1
dg/dt=1.0/(j*v_ratio*((r*transitTime)/e_base))*((cmr_o-1.0)-k*s) : %.4f
dq/dt=1/tau_o*((f_in*o_e/e_base)-f_out*q/v) : 1
y=v_0*((k1+k2)*(1-q)-(k2+k3)*(1-v)) : 1
G_base : siemens
eta : 1/second
tau_s : second
tau_f : second
alpha : 1
tau_o : second
v_0 : 1
k1 : 1
k2 : 1
k3 : 1
phi : %.4f*second
e_base : %.4f
g_0 : %.4f
c_ab : 1
cb_0 : 1
v_ratio : 1
j : 1
transitTime : second
k : 1
r : 1
''' %
(params.e_base, params.g_0, params.phi, params.e_base, params.g_0))
NeuronGroup.__init__(self,
1,
model=eqs,
clock=clock,
compile=True,
freeze=True)
self.params = params
self.G_base = params.G_base
self.eta = params.eta
self.tau_s = params.tau_s
self.tau_f = params.tau_f
self.alpha = params.alpha
self.tau_o = params.tau_o
self.e_base = params.e_base
self.v_0 = params.v_0
self.k1 = params.k1
self.params.s_e = params.s_e_0 * exp(-params.TE / params.T_2E)
self.params.s_i = params.s_i_0 * exp(-params.TE / params.T_2I)
self.params.beta = self.params.s_e / self.params.s_i
self.k2 = self.params.beta * params.r_0 * self.e_base * params.TE
self.k3 = self.params.beta - 1.0
self.c_ab = self.params.c_ab
self.cb_0 = self.params.cb_0
self.g_0 = self.params.g_0
self.phi = self.params.phi
self.v_ratio = self.params.v_ratio
self.j = self.params.j
self.transitTime = self.params.transitTime
self.k = self.params.k
self.r = self.params.r
self.f_in = 1.0
self.s = 0.0
self.v = 1.0
self.o_e = self.e_base
self.cb = self.cb_0
self.g = self.g_0
self.oe_log = np.log(1.0 - self.o_e / (1.0 - self.g))
self.q = 1.0
if network is not None:
self.G_total = linked_var(network, 'g_syn', func=sum)
self.G_total_exc = linked_var(network, 'g_syn_exc', func=sum)
