def test_group_variable_set_conditional_copy_to_host():
G = NeuronGroup(1, 'v : 1')
# uses group_variable_set_conditional template
G.v['i < 1'] = '50'
# connect template runs on host, requiring G.v on host after the group_variable_set
# template call above (this tests that data is copied from device to host)
S = Synapses(G, G)
S.connect(condition='v_pre == 50')
run(0 * second)
assert len(S) == 1, len(S)
def mk_sencircuit(task_info):
"""
Creates balanced network representing sensory circuit.
returns:
groups, synapses, subgroups
groups and synapses have to be added to the "Network" in order to run the simulation
subgroups is used for establishing connections between the sensory and integration circuit;
do not add subgroups to the "Network"
psr following the published Brian1 code in DB model from Wimmer et al. 2015
- brain2 code
- implementation runs in cpp_standalone mode, compatible with SNEP
"""
# -------------------------------------
# Sensory circuit parameters
# -------------------------------------
# populations
N_E = task_info['sen']['populations']['N_E'] # total number of E neurons
sub = task_info['sen']['populations']['sub'] # fraction corresponding to subpops 1,2
N_E1 = int(N_E * sub) # size of exc subpops that responds to the stimuli
N_I = task_info['sen']['populations']['N_I'] # total number of I neurons
N_X = task_info['sen']['populations']['N_X'] # size of external pop
# local recurrent connections
eps = task_info['sen']['connectivity']['eps'] # connection probability for EE, EI, IE and II
w_p = task_info['sen']['connectivity']['w_p'] # relative synaptic strength within pop E1 and E2
w_m = 2 - w_p # relative synaptic strength across pop E1 and E2
gEE = task_info['sen']['connectivity']['gEE'] # weight of EE synapses, prev: 0.7589*nS
gEI = task_info['sen']['connectivity']['gEI'] # weight of EI synapses, prev: 1.5179*nS
gIE = task_info['sen']['connectivity']['gIE'] # weight of IE synapses, prev: 12.6491*nS
gII = task_info['sen']['connectivity']['gII'] # weight of II synapses
gmax = task_info['sen']['connectivity']['gmax'] # maximum synaptic weight
dE = '0.5*ms + rand()*1.0*ms' # range of transmission delays of E synapses, (0.5:1.5)
dI = '0.1*ms + rand()*0.8*ms' # range of transmission delays of I synapses, (0.1:0.9)
# external connections
epsX = task_info['sen']['connectivity']['epsX'] # connection probability for ext synapses
alphaX = task_info['sen']['connectivity']['alphaX'] # 0: global input, 1: local input
gXE = task_info['sen']['connectivity']['gXE'] # weight of ext to E synapses
gXI = task_info['sen']['connectivity']['gXI'] # weight of ext to I synapses
dX = '0.5*ms + rand()*1.0*ms' # range of transmission delays of X synapses, (0.5:1.5)
# neuron models
CmE = task_info['sen']['neuron']['CmE'] # membrane capacitance of E neurons
CmI = task_info['sen']['neuron']['CmI'] # membrane capacitance of I neurons
gleakE = task_info['sen']['neuron']['gleakE'] # leak conductance of E neurons
gleakI = task_info['sen']['neuron']['gleakI'] # leak conductance of I neurons
Vl = task_info['sen']['neuron']['Vl'] # resting potential
Vt = task_info['sen']['neuron']['Vt'] # spiking threshold
Vr = task_info['sen']['neuron']['Vr'] # reset potential
tau_refE = task_info['sen']['neuron']['tau_refE'] # absolute refractory period of E neurons
tau_refI = task_info['sen']['neuron']['tau_refI'] # absolute refractory period of I neurons
nu_ext = task_info['sen']['neuron']['nu_ext'] # rate of external Poisson neurons, prev: 12.5*Hz
# synapse models
VrevE = task_info['sen']['synapse']['VrevE'] # reversal potential for E synapses
VrevI = task_info['sen']['synapse']['VrevI'] # reversal potential for I synapses
tau_decay = task_info['sen']['synapse']['tau_decay'] # decay constants of AMPA and GABA conductances
tau_rise = task_info['sen']['synapse']['tau_rise'] # rise constants of AMPA and GABA conductances
# namespace with params
paramsen = {'gEE': gEE, 'gEI': gEI, 'gIE': gIE, 'gII': gII, 'gmax': gmax, 'gXE': gXE, 'gXI': gXI,
'gleakE': gleakE, 'gleakI': gleakI, 'CmE': CmE, 'CmI': CmI, 'Vl': Vl, 'Vt': Vt, 'Vr': Vr,
'VrevE': VrevE, 'VrevI': VrevI, 'tau_decay': tau_decay, 'tau_rise': tau_rise,
'w_p': w_p, 'w_m': w_m, 'sub': sub, 'eps': eps, 'epsX': epsX, 'alphaX': alphaX}
# numerical integration method
nummethod = task_info['simulation']['nummethod']
# -------------------------------------
# Set up the model and connections
# -------------------------------------
# neuron equations
eqsE = '''
dV/dt = (-g_ea*(V-VrevE) - g_i*(V-VrevI) - (V-Vl)) / tau + I/Cm : volt (unless refractory)
dg_ea/dt = (-g_ea + x_ea) / tau_decay : 1
dx_ea/dt = -x_ea / tau_rise : 1
dg_i/dt = (-g_i + x_i) / tau_decay : 1
dx_i/dt = -x_i / tau_rise : 1
tau = CmE/gleakE : second
Cm = CmE : farad
I = Irec(t, i) : amp
'''
eqsI = '''
dV/dt = (-g_ea*(V-VrevE) - g_i*(V-VrevI) - (V-Vl)) / tau + I/Cm : volt (unless refractory)
dg_ea/dt = (-g_ea + x_ea) / tau_decay : 1
dx_ea/dt = -x_ea / tau_rise : 1
dg_i/dt = (-g_i + x_i) / tau_decay : 1
dx_i/dt = -x_i / tau_rise : 1
tau = CmI/gleakI : second
Cm = CmI : farad
I : amp
'''
# neuron groups
senE = NeuronGroup(N_E, model=eqsE, method=nummethod, threshold='V>=Vt', reset='V=Vr',
refractory=tau_refE, namespace=paramsen, name='senE')
senE1 = senE[:N_E1]
senE2 = senE[N_E1:]
senI = NeuronGroup(N_I, model=eqsI, method=nummethod, threshold='V>=Vt', reset='V=Vr',
refractory=tau_refI, namespace=paramsen, name='senI')
# synapses
# weight according the different subgroups
condsame = '(i<N_pre*sub and j<N_post*sub) or (i>=N_pre*sub and j>=N_post*sub)'
conddiff = '(i<N_pre*sub and j>=N_post*sub) or (i>=N_pre*sub and j<N_post*sub)'
# AMPA: exc --> exc
synSESE = Synapses(senE, senE, model='w : 1', method=nummethod,
on_pre='''x_ea += w
w = clip(w, 0, gmax)''',
namespace=paramsen, name='synSESE')
synSESE.connect(p='eps')
synSESE.w[condsame] = 'w_p * gEE/gleakE * (1 + randn()*0.5)'
synSESE.w[conddiff] = 'w_m * gEE/gleakE * (1 + randn()*0.5)'
synSESE.delay = dE
# AMPA: exc --> inh
synSESI = Synapses(senE, senI, model='w : 1', method=nummethod,
on_pre='''x_ea += w
w = clip(w, 0, gmax)''',
namespace=paramsen, name='synSESI')
synSESI.connect(p='eps')
synSESI.w = 'gEI/gleakI * (1 + randn()*0.5)'
synSESI.delay = dE
# GABA: inh --> exc
synSISE = Synapses(senI, senE, model='w : 1', method=nummethod,
on_pre='''x_i += w
w = clip(w, 0, gmax)''',
namespace=paramsen, name='synSISE')
synSISE.connect(p='eps')
synSISE.w = 'gIE/gleakE * (1 + randn()*0.5)'
synSISE.delay = dI
# GABA: inh --> inh
synSISI = Synapses(senI, senI, model='w : 1', method=nummethod,
on_pre='''x_i += w
w = clip(w, 0, gmax)''',
namespace=paramsen, name='synSISI')
synSISI.connect(p='eps')
synSISI.w = 'gII/gleakI * (1 + randn()*0.5)'
synSISI.delay = dI
# external inputs and synapses
extS = PoissonGroup(N_X, rates=nu_ext)
synSXSE = Synapses(extS, senE, model='w : 1', method=nummethod,
on_pre='''x_ea += w
w = clip(w, 0, gmax)''',
namespace=paramsen, name='synSXSE')
synSXSE.connect(condition=condsame, p='epsX * (1 + alphaX)')
synSXSE.connect(condition=conddiff, p='epsX * (1 - alphaX)')
synSXSE.w = 'gXE/gleakE * (1 + randn()*0.5)'
synSXSE.delay = dX
synSXSI = Synapses(extS, senI, model='w : 1', method=nummethod,
on_pre='''x_ea += w
w = clip(w, 0, gmax)''',
namespace=paramsen, name='synSXSI')
synSXSI.connect(p='epsX')
synSXSI.w = 'gXI/gleakI * (1 + randn()*0.5)'
synSXSI.delay = dX
# variables to return
groups = {'SE': senE, 'SI': senI, 'SX': extS}
subgroups = {'SE1': senE1, 'SE2': senE2}
synapses = {'synSESE': synSESE, 'synSESI': synSESI,
'synSISE': synSISE, 'synSISI': synSISI,
'synSXSI': synSXSI, 'synSXSE': synSXSE}
return groups, synapses, subgroups
def mk_sencircuit_2cplastic(task_info):
"""
Creates balanced network representing sensory circuit with 2c model in the excitatory neurons.
returns:
groups, synapses, subgroups
groups and synapses have to be added to the "Network" in order to run the simulation
subgroups is used for establishing connections between the sensory and integration circuit;
do not add subgroups to the "Network"
psr following the published Brian1 code in DB model from Wimmer et al. 2015
- brain2 code
- two-compartmental model following Naud & Sprekeler 2018
- implementation runs in cpp_standalone mode, compatible with SNEP
"""
from numpy import log as np_log
# -------------------------------------
# Sensory circuit parameters
# -------------------------------------
# populations
N_E = task_info['sen']['populations']['N_E'] # total number of E neurons
sub = task_info['sen']['populations']['sub'] # fraction corresponding to subpops 1,2
N_E1 = int(N_E * sub) # size of exc subpops that responds to the stimuli
N_I = task_info['sen']['populations']['N_I'] # total number of I neurons
N_X = task_info['sen']['populations']['N_X'] # size of external pop
# local recurrent connections
eps = task_info['sen']['connectivity']['eps'] # connection probability for EE, EI, IE and II
w_p = task_info['sen']['connectivity']['w_p'] # relative synaptic strength within pop E1 and E2
w_m = 2 - w_p # relative synaptic strength across pop E1 and E2
gEEs = task_info['sen']['2c']['gEEs'] # weight of EE synapses, prev: 0.7589*nS
gEI = task_info['sen']['connectivity']['gEI'] # weight of EI synapses, prev: 1.5179*nS
gIEs = task_info['sen']['2c']['gIEs'] # weight of IE synapses, prev: 12.6491*nS
gII = task_info['sen']['connectivity']['gII'] # weight of II synapses
gmax = task_info['sen']['connectivity']['gmax'] # maximum synaptic weight
dE = '0.5*ms + rand()*1.0*ms' # range of transmission delays of E synapses, (0.5:1.5)
dI = '0.1*ms + rand()*0.8*ms' # range of transmission delays of I synapses, (0.1:0.9)
# external connections
epsX = task_info['sen']['connectivity']['epsX'] # connection probability for ext synapses
alphaX = task_info['sen']['connectivity']['alphaX'] # 0: global input, 1: local input
gXEs = task_info['sen']['2c']['gXEs'] # weight of ext to E synapses of soma
gXI = task_info['sen']['connectivity']['gXI'] # weight of ext to I synapses
dX = '0.5*ms + rand()*1.0*ms' # range of transmission delays of X synapses, (0.5:1.5)
# neuron model
CmI = task_info['sen']['neuron']['CmI'] # membrane capacitance of I neurons
gleakI = task_info['sen']['neuron']['gleakI'] # leak conductance of I neurons
Vl = task_info['sen']['neuron']['Vl'] # reversal potential
Vt = task_info['sen']['neuron']['Vt'] # threshold potential
Vr = task_info['sen']['neuron']['Vr'] # reset potential, only for inh
tau_refI = task_info['sen']['neuron']['tau_refI'] # absolute refractory period of I neurons
nu_ext = task_info['sen']['neuron']['nu_ext'] # rate of external Poisson neurons, prev: 12.5*Hz
# soma
taus = task_info['sen']['2c']['taus'] # timescale of membrane potential
Cms = task_info['sen']['2c']['Cms'] # capacitance
gleakEs = Cms/taus
tauws = task_info['sen']['2c']['tauws'] # timescale of recovery ("slow") variable
bws = task_info['sen']['2c']['bws'] # strength of spike-triggered facilitation (bws < 0)
gCas = task_info['sen']['2c']['gCas'] # strenght of forward calcium spike propagation
tau_refE = task_info['sen']['2c']['tau_refE'] # refractory period
# dendrite
taud = task_info['sen']['2c']['taud']
Cmd = task_info['sen']['2c']['Cmd']
gleakEd = Cmd/taud
tauwd = task_info['sen']['2c']['tauwd'] # timescale of recovery ("slow") variable
awd = task_info['sen']['2c']['awd'] # stregth of subthreshold facilitations (awd < 0)
gCad = task_info['sen']['2c']['gCad'] # strength of local regenerative activity
bpA = task_info['sen']['2c']['bpA'] # strength of backpropagation activity (c variable)
k1 = task_info['sen']['2c']['k1'] # rectangular kernel for backpropagating activity
k2 = task_info['sen']['2c']['k2'] # if t in [0.5, 2.0] we'll have backpropagating current
muOUs = task_info['sen']['2c']['muOUs'] # OU parameters for background noise of soma
#muOUd = task_info['sen']['2c']['muOUd'] # OU parameters for background noise of dendrites
sigmaOU = task_info['sen']['2c']['sigmaOU']
tauOU = task_info['sen']['2c']['tauOU']
# synapse models
VrevE = task_info['sen']['synapse']['VrevE'] # reversal potential for E synapses
VrevI = task_info['sen']['synapse']['VrevI'] # reversal potential for I synapses in inh
tau_decay = task_info['sen']['synapse']['tau_decay'] # decay constant of AMPA, GABA
tau_rise = task_info['sen']['synapse']['tau_rise'] # rise constant of AMPA, GABA for inh
VrevIsd = task_info['sen']['synapse']['VrevIsd'] # rev potential for I synapses in soma, dend
# plasticity in dendrites
eta0 = task_info['sen']['2c']['eta0']
tauB = task_info['sen']['2c']['tauB']
targetB = task_info['targetB']
B0 = tauB*targetB
tau_update = task_info['sen']['2c']['tau_update']
eta = eta0 * tau_update / tauB
validburst = task_info['sen']['2c']['validburst'] # if tau_burst = 4*ms, at 16 ms stopburst = 0.0183
min_burst_stop = task_info['sen']['2c']['min_burst_stop'] # thus, we will set it at critrefburst = 0.02
tau_burst = -validburst / np_log(min_burst_stop) * second
param2c = {'gEE': gEEs, 'gEI': gEI, 'gIE': gIEs, 'gII': gII, 'gmax': gmax, 'gXEs': gXEs, 'gXI': gXI,
'gleakEs': gleakEs, 'gleakEd': gleakEd, 'gleakI': gleakI, 'Cms': Cms, 'Cmd': Cmd, 'CmI': CmI,
'Vl': Vl, 'Vt': Vt, 'Vr': Vr, 'VrevE': VrevE, 'VrevIsd': VrevIsd, 'VrevI': VrevI,
'taus': taus, 'taud': taud, 'tau_refE': tau_refE, 'tau_refI': tau_refI,
'tau_decay': tau_decay, 'tau_rise': tau_rise, 'tau_ws': tauws, 'tau_wd': tauwd,
'bws': bws, 'awd': awd, 'gCas': gCas, 'gCad': gCad, 'bpA': bpA, 'k1': k1, 'k2': k2,
'muOUs': muOUs, 'tauOU': tauOU, 'sigmaOU': sigmaOU,
'w_p': w_p, 'w_m': w_m, 'sub': sub, 'eps': eps, 'epsX': epsX, 'alphaX': alphaX,
'eta': eta, 'B0': B0, 'tauB': tauB, 'tau_burst': tau_burst, 'min_burst_stop': min_burst_stop}
# numerical integration method
nummethod = task_info['simulation']['nummethod']
# -------------------------------------
# Set up model and connections
# -------------------------------------
# neuron equations
eqss = '''
dV/dt = (-g_ea*(V-VrevE) -g_i*(V-VrevIsd) -(V-Vl))/tau + (gCas/(1+exp(-(V_d/mV + 38)/6)) + w_s + I)/Cm : volt (unless refractory)
dg_ea/dt = (-g_ea + x_ea) / tau_decay : 1
dx_ea/dt = -x_ea / tau_rise : 1
dg_i/dt = (-g_i + x_i) / tau_decay : 1
dx_i/dt = -x_i / tau_rise : 1
dw_s/dt = -w_s / tau_ws : amp
tau = taus : second
Cm = Cms : farad
I = Irec(t, i) : amp
V_d : volt (linked)
B : 1 (linked)
burst_start : 1 (linked)
burst_stop : 1 (linked)
muOUd : amp (linked)
'''
# dIbg/dt = (muOUs - Ibg) / tauOU + (sigmaOU * xi) / sqrt(tauOU / 2) : amp --> they have I_Ext
eqsd = '''
dV_d/dt = (-g_ea*(V_d-VrevE) -(V_d-Vl))/tau + (gCad/(1+exp(-(V_d/mV + 38)/6)) + w_d + K + Ibg)/Cm : volt
dg_ea/dt = (-g_ea + x_ea) / tau_decay : 1
dx_ea/dt = -x_ea / tau_rise : 1
dw_d/dt = (-w_d + awd*(V_d - Vl))/tau_wd : amp
dIbg/dt = (muOUd - Ibg) / tauOU + (sigmaOU * xi) / sqrt(tauOU / 2) : amp
K = bpA * (((t-lastspike_soma) >= k1) * ((t-lastspike_soma) <= k2)) : amp
dB/dt = -B / tauB : 1
dburst_start/dt = -burst_start / tau_burst : 1 (unless refractory)
dburst_stop/dt = -burst_stop / tau_burst : 1
tau = taud : second
Cm = Cmd : farad
muOUd : amp
lastspike_soma : second (linked)
'''
eqsI = '''
dV/dt = (-g_ea*(V-VrevE) -g_i*(V-VrevI) -(V-Vl))/tau + I/Cm : volt (unless refractory)
dg_ea/dt = (-g_ea + x_ea) / tau_decay : 1
dx_ea/dt = -x_ea / tau_rise : 1
dg_i/dt = (-g_i + x_i) / tau_decay : 1
dx_i/dt = -x_i / tau_rise : 1
tau = CmI/gleakI : second
Cm = CmI : farad
I : amp
'''
# neuron groups
soma = NeuronGroup(N_E, model=eqss, method=nummethod, threshold='V>=Vt',
reset='''V = Vl
w_s += bws
burst_start += 1
burst_stop = 1''',
refractory=tau_refE, namespace=param2c, name='soma')
dend = NeuronGroup(N_E, model=eqsd, method=nummethod, threshold='burst_start > 1 + min_burst_stop',
reset='''B += 1
burst_start = 0''',
refractory='burst_stop >= min_burst_stop',
namespace=param2c, name='dend')
senI = NeuronGroup(N_I, model=eqsI, method=nummethod, threshold='V>=Vt', reset='V=Vr',
refractory=tau_refI, namespace=param2c, name='senI')
# linked variables
soma.V_d = linked_var(dend, 'V_d')
soma.B = linked_var(dend, 'B')
soma.burst_start = linked_var(dend, 'burst_start')
soma.burst_stop = linked_var(dend, 'burst_stop')
soma.muOUd = linked_var(dend, 'muOUd')
dend.lastspike_soma = linked_var(soma, 'lastspike')
# subgroups
soma1 = soma[:N_E1]
dend1 = dend[:N_E1]
soma2 = soma[N_E1:]
dend2 = dend[N_E1:]
# update muOUd with plasticity rule
dend1.muOUd = '-50*pA - rand()*100*pA' # random initialisation of weights -50:-150 pA
dend1.run_regularly('muOUd = clip(muOUd - eta * (B - B0), -100*amp, 0)', dt=tau_update)
# TODO: check the target vs burst rate convergence
# TODO: try tau_B = 50,000 sec, but mayb\e this is not the case because you do reach the target
# synapses
# weight according the different subgroups
condsame = '(i<N_pre*sub and j<N_post*sub) or (i>=N_pre*sub and j>=N_post*sub)'
conddiff = '(i<N_pre*sub and j>=N_post*sub) or (i>=N_pre*sub and j<N_post*sub)'
# AMPA: exc --> exc
synSESE = Synapses(soma, soma, model='w : 1', method=nummethod,
on_pre='''x_ea += w
w = clip(w, 0, gmax)''',
namespace=param2c, name='synSESE')
synSESE.connect(p='eps')
synSESE.w[condsame] = 'w_p * gEE/gleakEs * (1 + randn()*0.5)'
synSESE.w[conddiff] = 'w_m * gEE/gleakEs * (1 + randn()*0.5)'
synSESE.delay = dE
# AMPA: exc --> inh
synSESI = Synapses(soma, senI, model='w : 1', method=nummethod,
on_pre='''x_ea += w
w = clip(w, 0, gmax)''',
namespace=param2c, name='synSESI')
synSESI.connect(p='eps')
synSESI.w = 'gEI/gleakI * (1 + randn()*0.5)'
synSESI.delay = dE
# GABA: inh --> exc
synSISE = Synapses(senI, soma, model='w : 1', method=nummethod,
on_pre='''x_i += w
w = clip(w, 0, gmax)''',
namespace=param2c, name='synSISE')
synSISE.connect(p='eps')
synSISE.w = 'gIE/gleakEs * (1 + randn()*0.5)'
synSISE.delay = dI
# GABA: inh --> inh
synSISI = Synapses(senI, senI, model='w : 1', method=nummethod,
on_pre='''x_i += w
w = clip(w, 0, gmax)''',
namespace=param2c, name='synSISI')
synSISI.connect(p='eps')
synSISI.w = 'gII/gleakI * (1 + randn()*0.5)'
synSISI.delay = dI
# external inputs and synapses
extS = PoissonGroup(N_X, rates=nu_ext)
synSXSEs = Synapses(extS, soma, model='w : 1', method=nummethod,
on_pre='''x_ea += w
w = clip(w, 0, gmax)''',
namespace=param2c, name='synSXSEs')
synSXSEs.connect(condition=condsame, p='epsX * (1 + alphaX)')
synSXSEs.connect(condition=conddiff, p='epsX * (1 - alphaX)')
synSXSEs.w = 'gXEs/gleakEs * (1 + randn()*0.5)'
synSXSEs.delay = dX
synSXSI = Synapses(extS, senI, model='w : 1', method=nummethod,
on_pre='''x_ea += w
w = clip(w, 0, gmax)''',
namespace=param2c, name='synSXSI')
synSXSI.connect(p='epsX')
synSXSI.w = 'gXI/gleakI * (1 + randn()*0.5)'
synSXSI.delay = dX
# external inputs to dendrites mimicking decision circuit
subDE = 240
rateDE = 40*Hz # TODO: lower rate --> 40 or 30
d = 1*ms
wDS = 0.06
XDE = PoissonGroup(subDE, rates=rateDE)
synXDEdend = Synapses(XDE, dend1, model='w : 1', method=nummethod, delay=d,
on_pre='x_ea += w',
namespace=param2c, name='synXDEdend')
synXDEdend.connect(p=0.2)
synXDEdend.w = 'wDS'
# variables to return
groups = {'soma': soma, 'dend': dend, 'SI': senI, 'SX': extS, 'XDE': XDE}
subgroups = {'soma1': soma1, 'soma2': soma2,
'dend1': dend1, 'dend2': dend2}
synapses = {'synSESE': synSESE, 'synSESI': synSESI,
'synSISE': synSISE, 'synSISI': synSISI,
'synSXSI': synSXSI, 'synSXSEs': synSXSEs,
'synXDEdend': synXDEdend}
return groups, synapses, subgroups
def mk_sencircuit_2c(task_info):
"""
Creates balanced network representing sensory circuit with 2c model in the excitatory neurons.
returns:
groups, synapses, subgroups
groups and synapses have to be added to the "Network" in order to run the simulation
subgroups is used for establishing connections between the sensory and integration circuit;
do not add subgroups to the "Network"
psr following the published Brian1 code in DB model from Wimmer et al. 2015
- brain2 code
- two-compartmental model following Naud & Sprekeler 2018
- implementation runs in cpp_standalone mode, compatible with SNEP
"""
# -------------------------------------
# Sensory circuit parameters
# -------------------------------------
# populations
N_E = task_info['sen']['populations']['N_E'] # total number of E neurons
sub = task_info['sen']['populations']['sub'] # fraction corresponding to subpops 1,2
N_E1 = int(N_E * sub) # size of exc subpops that responds to the stimuli
N_I = task_info['sen']['populations']['N_I'] # total number of I neurons
N_X = task_info['sen']['populations']['N_X'] # size of external pop
# local recurrent connections
eps = task_info['sen']['connectivity']['eps'] # connection probability for EE, EI, IE and II
w_p = task_info['sen']['connectivity']['w_p'] # relative synaptic strength within pop E1 and E2
w_m = 2 - w_p # relative synaptic strength across pop E1 and E2
gEEs = task_info['sen']['2c']['gEEs'] # weight of EE synapses, prev: 0.7589*nS
gEI = task_info['sen']['connectivity']['gEI'] # weight of EI synapses, prev: 1.5179*nS
gIEs = task_info['sen']['2c']['gIEs'] # weight of IE synapses, prev: 12.6491*nS
gII = task_info['sen']['connectivity']['gII'] # weight of II synapses
gmax = task_info['sen']['connectivity']['gmax'] # maximum synaptic weight
dE = '0.5*ms + rand()*1.0*ms' # range of transmission delays of E synapses, (0.5:1.5)
dI = '0.1*ms + rand()*0.8*ms' # range of transmission delays of I synapses, (0.1:0.9)
# external connections
epsX = task_info['sen']['connectivity']['epsX'] # connection probability for ext synapses
alphaX = task_info['sen']['connectivity']['alphaX'] # 0: global input, 1: local input
gXEs = task_info['sen']['2c']['gXEs'] # weight of ext to E synapses of soma
gXI = task_info['sen']['connectivity']['gXI'] # weight of ext to I synapses
dX = '0.5*ms + rand()*1.0*ms' # range of transmission delays of X synapses, (0.5:1.5)
# neuron model
CmI = task_info['sen']['neuron']['CmI'] # membrane capacitance of I neurons
gleakI = task_info['sen']['neuron']['gleakI'] # leak conductance of I neurons
Vl = task_info['sen']['neuron']['Vl'] # reversal potential
Vt = task_info['sen']['neuron']['Vt'] # threshold potential
Vr = task_info['sen']['neuron']['Vr'] # reset potential, only for inh
tau_refI = task_info['sen']['neuron']['tau_refI'] # absolute refractory period of I neurons
nu_ext = task_info['sen']['neuron']['nu_ext'] # rate of external Poisson neurons, prev: 12.5*Hz
# soma
taus = task_info['sen']['2c']['taus'] # timescale of membrane potential
Cms = task_info['sen']['2c']['Cms'] # capacitance
gleakEs = Cms/taus
tauws = task_info['sen']['2c']['tauws'] # timescale of recovery ("slow") variable
bws = task_info['sen']['2c']['bws'] # strength of spike-triggered facilitation (bws < 0)
gCas = task_info['sen']['2c']['gCas'] # strenght of forward calcium spike propagation
tau_refE = task_info['sen']['2c']['tau_refE'] # refractory period
# dendrite
taud = task_info['sen']['2c']['taud']
Cmd = task_info['sen']['2c']['Cmd']
gleakEd = Cmd/taud
tauwd = task_info['sen']['2c']['tauwd'] # timescale of recovery ("slow") variable
awd = task_info['sen']['2c']['awd'] # stregth of subthreshold facilitations (awd < 0)
gCad = task_info['sen']['2c']['gCad'] # strength of local regenerative activity
bpA = task_info['sen']['2c']['bpA'] # strength of backpropagation activity (c variable)
k1 = task_info['sen']['2c']['k1'] # rectangular kernel for backpropagating activity
k2 = task_info['sen']['2c']['k2'] # if t in [0.5, 2.0] we'll have backpropagating current
muOUd = task_info['sen']['2c']['muOUd'] # OU parameters for background noise of dendrites
sigmaOU = task_info['sen']['2c']['sigmaOU']
tauOU = task_info['sen']['2c']['tauOU']
# synapse models
VrevE = task_info['sen']['synapse']['VrevE'] # reversal potential for E synapses
VrevI = task_info['sen']['synapse']['VrevI'] # reversal potential for I synapses in inh
tau_decay = task_info['sen']['synapse']['tau_decay'] # decay constant of AMPA, GABA
tau_rise = task_info['sen']['synapse']['tau_rise'] # rise constant of AMPA, GABA for inh
VrevIsd = task_info['sen']['synapse']['VrevIsd'] # rev potential for I synapses in soma, dend
#tau_decayE = task_info['sen']['synapse']['tau_decaysd'] # rise constants of AMPA conductances
# in Wimmer are decayE = decayI = 5, Naud decayE = 1, decayI = 5 !
# TODO: try both approaches and decide which one to use.
param2c = {'gEE': gEEs, 'gEI': gEI, 'gIE': gIEs, 'gII': gII, 'gmax': gmax, 'gXEs': gXEs, 'gXI': gXI,
'gleakEs': gleakEs, 'gleakEd': gleakEd, 'gleakI': gleakI, 'Cms': Cms, 'Cmd': Cmd, 'CmI': CmI,
'Vl': Vl, 'Vt': Vt, 'Vr': Vr, 'VrevE': VrevE, 'VrevIsd': VrevIsd, 'VrevI': VrevI,
'taus': taus, 'taud': taud, 'tau_refE': tau_refE, 'tau_refI': tau_refI,
'tau_decay': tau_decay, 'tau_rise': tau_rise, 'tau_ws': tauws, 'tau_wd': tauwd,
'bws': bws, 'awd': awd, 'gCas': gCas, 'gCad': gCad, 'bpA': bpA, 'k1': k1, 'k2': k2,
'muOUd': muOUd, 'tauOU': tauOU, 'sigmaOU': sigmaOU,
'w_p': w_p, 'w_m': w_m, 'sub': sub, 'eps': eps, 'epsX': epsX, 'alphaX': alphaX}
# numerical integration method
nummethod = task_info['simulation']['nummethod']
# -------------------------------------
# Set up model and connections
# -------------------------------------
# neuron equations
eqss = '''
dV/dt = (-g_ea*(V-VrevE) -g_i*(V-VrevIsd) -(V-Vl))/tau + (gCas/(1+exp(-(V_d/mV + 38)/6)) + w_s + I)/Cm : volt (unless refractory)
dg_ea/dt = (-g_ea + x_ea) / tau_decay : 1
dx_ea/dt = -x_ea / tau_rise : 1
dg_i/dt = (-g_i + x_i) / tau_decay : 1
dx_i/dt = -x_i / tau_rise : 1
dw_s/dt = -w_s / tau_ws : amp
tau = taus : second
Cm = Cms : farad
I = Irec(t, i) : amp
V_d : volt (linked)
'''
eqsd = '''
dV_d/dt = (-g_ea*(V_d-VrevE) -(V_d-Vl))/tau + (gCad/(1+exp(-(V_d/mV + 38)/6)) + w_d + K + Ibg)/Cm : volt
dg_ea/dt = (-g_ea + x_ea) / tau_decay : 1
dx_ea/dt = -x_ea / tau_rise : 1
dw_d/dt = (-w_d + awd*(V_d - Vl))/tau_wd : amp
dIbg/dt = (muOUd - Ibg) / tauOU + (sigmaOU * xi) / sqrt(tauOU / 2) : amp
K = bpA * (((t-lastspike_soma) >= k1) * ((t-lastspike_soma) <= k2)) : amp
tau = taud : second
Cm = Cmd : farad
muOUd : amp
lastspike_soma : second (linked)
'''
eqsI = '''
dV/dt = (-g_ea*(V-VrevE) -g_i*(V-VrevI) -(V-Vl))/tau + I/Cm : volt (unless refractory)
dg_ea/dt = (-g_ea + x_ea) / tau_decay : 1
dx_ea/dt = -x_ea / tau_rise : 1
dg_i/dt = (-g_i + x_i) / tau_decay : 1
dx_i/dt = -x_i / tau_rise : 1
tau = CmI/gleakI : second
Cm = CmI : farad
I : amp
'''
# neuron groups
soma = NeuronGroup(N_E, model=eqss, method=nummethod, threshold='V>=Vt',
reset='''V = Vl
w_s += bws''',
refractory=tau_refE, namespace=param2c, name='soma')
dend = NeuronGroup(N_E, model=eqsd, method=nummethod, namespace=param2c, name='dend')
senI = NeuronGroup(N_I, model=eqsI, method=nummethod, threshold='V>=Vt', reset='V=Vr',
refractory=tau_refI, namespace=param2c, name='senI')
# linked variables
soma.V_d = linked_var(dend, 'V_d')
dend.lastspike_soma = linked_var(soma, 'lastspike')
# subgroups
soma1 = soma[:N_E1]
dend1 = dend[:N_E1]
soma2 = soma[N_E1:]
dend2 = dend[N_E1:]
# synapses
# weight according the different subgroups
condsame = '(i<N_pre*sub and j<N_post*sub) or (i>=N_pre*sub and j>=N_post*sub)'
conddiff = '(i<N_pre*sub and j>=N_post*sub) or (i>=N_pre*sub and j<N_post*sub)'
# AMPA: exc --> exc
synSESE = Synapses(soma, soma, model='w : 1', method=nummethod,
on_pre='''x_ea += w
w = clip(w, 0, gmax)''',
namespace=param2c, name='synSESE')
synSESE.connect(p='eps')
synSESE.w[condsame] = 'w_p * gEE/gleakEs * (1 + randn()*0.5)'
synSESE.w[conddiff] = 'w_m * gEE/gleakEs * (1 + randn()*0.5)'
synSESE.delay = dE
# AMPA: exc --> inh
synSESI = Synapses(soma, senI, model='w : 1', method=nummethod,
on_pre='''x_ea += w
w = clip(w, 0, gmax)''',
namespace=param2c, name='synSESI')
synSESI.connect(p='eps')
synSESI.w = 'gEI/gleakI * (1 + randn()*0.5)'
synSESI.delay = dE
# GABA: inh --> exc
synSISE = Synapses(senI, soma, model='w : 1', method=nummethod,
on_pre='''x_i += w
w = clip(w, 0, gmax)''',
namespace=param2c, name='synSISE')
synSISE.connect(p='eps')
synSISE.w = 'gIE/gleakEs * (1 + randn()*0.5)'
synSISE.delay = dI
# GABA: inh --> inh
synSISI = Synapses(senI, senI, model='w : 1', method=nummethod,
on_pre='''x_i += w
w = clip(w, 0, gmax)''',
namespace=param2c, name='synSISI')
synSISI.connect(p='eps')
synSISI.w = 'gII/gleakI * (1 + randn()*0.5)'
synSISI.delay = dI
# external inputs and synapses
extS = PoissonGroup(N_X, rates=nu_ext)
synSXSEs = Synapses(extS, soma, model='w : 1', method=nummethod,
on_pre='''x_ea += w
w = clip(w, 0, gmax)''',
namespace=param2c, name='synSXSEs')
synSXSEs.connect(condition=condsame, p='epsX * (1 + alphaX)')
synSXSEs.connect(condition=conddiff, p='epsX * (1 - alphaX)')
synSXSEs.w = 'gXEs/gleakEs * (1 + randn()*0.5)'
synSXSEs.delay = dX
synSXSI = Synapses(extS, senI, model='w : 1', method=nummethod,
on_pre='''x_ea += w
w = clip(w, 0, gmax)''',
namespace=param2c, name='synSXSI')
synSXSI.connect(p='epsX')
synSXSI.w = 'gXI/gleakI * (1 + randn()*0.5)'
synSXSI.delay = dX
# variables to return
groups = {'soma': soma, 'dend': dend, 'SI': senI, 'SX': extS}
subgroups = {'soma1': soma1, 'soma2': soma2,
'dend1': dend1, 'dend2': dend2}
synapses = {'synSESE': synSESE, 'synSESI': synSESI,
'synSISE': synSISE, 'synSISI': synSISI,
'synSXSI': synSXSI, 'synSXSEs': synSXSEs}
return groups, synapses, subgroups
def mk_intcircuit(task_info):
"""
Creates the 'winner-takes-all' network described in Wang 2002.
returns:
groups, synapses, update_nmda, subgroups
groups, synapses and update_nmda have to be added to the "Network" in order to run the simulation
subgroups is used for establishing connections between the sensory and integration circuit;
do not add subgroups to the "Network"
psr following the published Brian1 code in DB model from Wimmer et al. 2015
- brian2 code
- PoissonInput for external connections
- no more network_operations
- implementation runs in cpp_standalone mode, compatible with SNEP
"""
# -------------------------------------
# Decision circuit parameters
# -------------------------------------
# populations
N_E = task_info['dec']['populations'][
'N_E'] # number of exc neurons (1600)
N_I = task_info['dec']['populations']['N_I'] # number of inh neurons (400)
sub = task_info['dec']['populations'][
'sub'] # fraction of stim-selective exc neurons
N_D1 = int(N_E * sub) # size of exc pop D1
N_D2 = N_D1 # size of exc pop D2
N_D3 = int(N_E * (1 - 2 * sub)) # size of exc pop D3, the rest
# local recurrent connections
w_p = task_info['dec']['connectivity'][
'w_p'] # relative synaptic strength of synapses within pop D1 and D2
w_m = 1 - sub * (w_p - 1) / (
1 - sub) # relative synaptic strength of synapses across pop D1 and D2
gEEa = task_info['dec']['connectivity'][
'gEEa'] # AMPA weight of EE synapses
gEEn = task_info['dec']['connectivity'][
'gEEn'] # NMDA weight of EE synapses
gEIa = task_info['dec']['connectivity'][
'gEIa'] # AMPA weight of EI synapses
gEIn = task_info['dec']['connectivity'][
'gEIn'] # NMDA weight of EI synapses
gIE = task_info['dec']['connectivity'][
'gIE'] # GABA weight of IE synapses, vs 1.3*nS from before
gII = task_info['dec']['connectivity']['gII'] # GABA weight of II synapses
d = task_info['dec']['connectivity'][
'delay'] # transmission delays of E synapses
# external connections
gXE = task_info['dec']['connectivity'][
'gXE'] # weight of XE (ext to exc) synapses
gXI = task_info['dec']['connectivity'][
'gXI'] # weight of XI (ext to inh) synapses
# neuron models
CmE = task_info['dec']['neuron'][
'CmE'] # membrane capacitance of E neurons
CmI = task_info['dec']['neuron'][
'CmI'] # membrane capacitance of I neurons
gleakE = task_info['dec']['neuron'][
'gleakE'] # leak conductance of E neurons
gleakI = task_info['dec']['neuron'][
'gleakI'] # leak conductance of I neurons
Vl = task_info['dec']['neuron']['Vl'] # resting potential
Vt = task_info['dec']['neuron']['Vt'] # spiking threshold
Vr = task_info['dec']['neuron']['Vr'] # reset potential
tau_refE = task_info['dec']['neuron'][
'tau_refE'] # absolute refractory period of E neurons
tau_refI = task_info['dec']['neuron'][
'tau_refI'] # absolute refractory period of I neurons
nu_ext = task_info['dec']['neuron'][
'nu_ext'] # firing rate of ext Poisson input to D1 and D2
nu_ext1 = task_info['dec']['neuron'][
'nu_ext1'] # firing rate of ext Poisson input to D3 and DI
# synapse models
VrevE = task_info['dec']['synapse'][
'VrevE'] # reversal potential for E synapses
VrevI = task_info['dec']['synapse'][
'VrevI'] # reversal potential for I synapses
tau_ampa = task_info['dec']['synapse'][
'tau_ampa'] # decay constant of AMPA conductances
tau_gaba = task_info['dec']['synapse'][
'tau_gaba'] # decay constant of GABA conductances
tau_nmda_d = task_info['dec']['synapse'][
'tau_nmda_d'] # decay constant of NMDA conductances
tau_nmda_r = task_info['dec']['synapse'][
'tau_nmda_r'] # rise constant of NMDA conductances
alpha_nmda = task_info['dec']['synapse'][
'alpha_nmda'] # saturation constant of NMDA conductances
# namespace with params
paramint = {
'w_p': w_p,
'w_m': w_m,
'gEEa': gEEa,
'gEEn': gEEn,
'gEIa': gEIa,
'gEIn': gEIn,
'gIE': gIE,
'gII': gII,
'gXE': gXE,
'gXI': gXI,
'gleakE': gleakE,
'gleakI': gleakI,
'Vl': Vl,
'Vt': Vt,
'Vr': Vr,
'VrevE': VrevE,
'VrevI': VrevI,
'tau_ampa': tau_ampa,
'tau_gaba': tau_gaba,
'tau_nmda_d': tau_nmda_d,
'tau_nmda_r': tau_nmda_r,
'alpha_nmda': alpha_nmda,
'sub': sub,
'CmE': CmE,
'CmI': CmI
}
# numerical integration method
nummethod = task_info['simulation']['nummethod']
# -------------------------------------
# Set up the model and connections
# -------------------------------------
# neuron equations
eqsE = '''
dV/dt = (-g_ea*(V-VrevE) - g_ent*(V-VrevE)/(1+exp(-V/mV*0.062)/3.57) - g_i*(V-VrevI) - (V-Vl)) / tau : volt (unless refractory)
dg_ea/dt = -g_ea / tau_ampa : 1
dg_i/dt = -g_i / tau_gaba : 1
dg_en/dt = -g_en / tau_nmda_d + alpha_nmda * x_en *(1-g_en) : 1
dx_en/dt = -x_en / tau_nmda_r : 1
g_ent : 1
tau = CmE/gleakE : second
label : integer (constant)
'''
eqsI = '''
dV/dt = (-g_ea*(V-VrevE) - g_entI*(V-VrevE)/(1+exp(-V/mV*0.062)/3.57) - g_i*(V-VrevI) - (V-Vl)) / tau : volt (unless refractory)
dg_ea/dt = -g_ea/tau_ampa : 1
dg_i/dt = -g_i/tau_gaba : 1
g_entI = w_nmda * g_ent : 1
g_ent : 1 (linked)
w_nmda : 1
tau = CmI/gleakI : second
'''
# setup of integration circuit
decE = NeuronGroup(N_E,
model=eqsE,
method=nummethod,
threshold='V>=Vt',
reset='V=Vr',
refractory=tau_refE,
namespace=paramint,
name='decE')
decE1 = decE[:N_D1]
decE2 = decE[N_D1:N_D1 + N_D2]
decE3 = decE[-N_D3:]
decE1.label = 1
decE2.label = 2
decE3.label = 3
decI = NeuronGroup(N_I,
model=eqsI,
method=nummethod,
threshold='V>=Vt',
reset='V=Vr',
refractory=tau_refI,
namespace=paramint,
name='decI')
# weight according the different subgroups
condsame = '(label_pre == label_post and label_pre != 3)'
conddiff = '(label_pre != label_post and label_pre != 3) or (label_pre == 3 and label_post != 3)'
condrest = '(label_post == 3)'
# NMDA: exc --> exc
eqsNMDA = '''
g_ent_post = w_nmda * g_en_pre : 1 (summed)
w_nmda : 1 (constant)
w : 1 (constant)
'''
synDEDEn = Synapses(decE,
decE,
model=eqsNMDA,
method=nummethod,
on_pre='x_en += w',
delay=d,
namespace=paramint,
name='synDEDEn')
synDEDEn.connect()
synDEDEn.w['i == j'] = 1
synDEDEn.w['i != j'] = 0
synDEDEn.w_nmda[condsame] = 'w_p * gEEn/gleakE'
synDEDEn.w_nmda[conddiff] = 'w_m * gEEn/gleakE'
synDEDEn.w_nmda[condrest] = 'gEEn/gleakE'
# NMDA: exc --> inh
decI.w_nmda = '(gEIn/gleakI) / (gEEn/gleakE)'
decI.g_ent = linked_var(decE3, 'g_ent', index=range(N_I))
# AMPA: exc --> exc
synDEDEa = Synapses(decE,
decE,
model='w : 1',
method=nummethod,
on_pre='g_ea += w',
delay=d,
namespace=paramint,
name='synDEDEa')
synDEDEa.connect()
synDEDEa.w[condsame] = 'w_p * gEEa/gleakE'
synDEDEa.w[conddiff] = 'w_m * gEEa/gleakE'
synDEDEa.w[condrest] = 'gEEa/gleakE'
# AMPA: exc --> inh
synDEDIa = Synapses(decE,
decI,
model='w : 1',
method=nummethod,
on_pre='g_ea += w',
delay=d,
namespace=paramint,
name='synDEDIa')
synDEDIa.connect()
synDEDIa.w = 'gEIa/gleakI'
# GABA: inh --> exc
synDIDE = Synapses(decI,
decE,
model='w : 1',
method=nummethod,
on_pre='g_i += w',
delay=d,
namespace=paramint,
name='synDIDE')
synDIDE.connect()
synDIDE.w = 'gIE/gleakE'
# GABA: inh --> inh
synDIDI = Synapses(decI,
decI,
model='w : 1',
method=nummethod,
on_pre='g_i += w',
delay=d,
namespace=paramint,
name='synDIDI')
synDIDI.connect()
synDIDI.w = 'gII/gleakI'
# external inputs and connections
extE = PoissonInput(decE[:N_D1 + N_D2],
'g_ea',
N=1,
rate=nu_ext1,
weight='gXE/gleakE')
extE3 = PoissonInput(decE3, 'g_ea', N=1, rate=nu_ext, weight='gXE/gleakE')
extI = PoissonInput(decI, 'g_ea', N=1, rate=nu_ext, weight='gXI/gleakI')
# variables to return
groups = {'DE': decE, 'DI': decI, 'DX': extE, 'DX3': extE3, 'DXI': extI}
subgroups = {'DE1': decE1, 'DE2': decE2, 'DE3': decE3}
synapses = {
'synDEDEn': synDEDEn,
'synDEDEa': synDEDEa,
'synDEDIa': synDEDIa,
'synDIDE': synDIDE,
'synDIDI': synDIDI
} # 'synDEDIn': synDEDIn,
return groups, synapses, subgroups
def run_task_hierarchical(task_info, taskdir, tempdir):
# imports
from brian2 import defaultclock, set_device, seed, TimedArray, Network, profiling_summary
from brian2.monitors import SpikeMonitor, PopulationRateMonitor, StateMonitor
from brian2.synapses import Synapses
from brian2.core.magic import start_scope
from brian2.units import second, ms, amp
from integration_circuit import mk_intcircuit
from sensory_circuit import mk_sencircuit, mk_sencircuit_2c, mk_sencircuit_2cplastic
from burstQuant import spks2neurometric
from scipy import interpolate
# if you want to put something in the taskdir, you must create it first
os.mkdir(taskdir)
print(taskdir)
# parallel code and flag to start
set_device('cpp_standalone', directory=tempdir)
#prefs.devices.cpp_standalone.openmp_threads = max_tasks
start_scope()
# simulation parameters
seedcon = task_info['simulation']['seedcon']
runtime = task_info['simulation']['runtime']
runtime_ = runtime / second
settletime = task_info['simulation']['settletime']
settletime_ = settletime / second
stimon = task_info['simulation']['stimon']
stimoff = task_info['simulation']['stimoff']
stimoff_ = stimoff / second
stimdur = stimoff - stimon
smoothwin = task_info['simulation']['smoothwin']
nummethod = task_info['simulation']['nummethod']
# -------------------------------------
# Construct hierarchical network
# -------------------------------------
# set connection seed
seed(
seedcon
) # set specific seed to test the same network, this way we also have the same synapses!
# decision circuit
Dgroups, Dsynapses, Dsubgroups = mk_intcircuit(task_info)
decE = Dgroups['DE']
decI = Dgroups['DI']
decE1 = Dsubgroups['DE1']
decE2 = Dsubgroups['DE2']
# sensory circuit, ff and fb connections
eps = 0.2 # connection probability
d = 1 * ms # transmission delays of E synapses
if task_info['simulation']['2cmodel']:
if task_info['simulation']['plasticdend']:
# plasticity rule in dendrites --> FB synapses will be removed from the network!
Sgroups, Ssynapses, Ssubgroups = mk_sencircuit_2cplastic(task_info)
else:
# 2c model (Naud)
Sgroups, Ssynapses, Ssubgroups = mk_sencircuit_2c(task_info)
senE = Sgroups['soma']
dend = Sgroups['dend']
senI = Sgroups['SI']
senE1 = Ssubgroups['soma1']
senE2 = Ssubgroups['soma2']
dend1 = Ssubgroups['dend1']
dend2 = Ssubgroups['dend2']
# FB
wDS = 0.003 # synaptic weight of FB synapses, 0.0668 nS when scaled by gleakE of sencircuit_2c
synDE1SE1 = Synapses(decE1,
dend1,
model='w : 1',
method=nummethod,
on_pre='x_ea += w',
delay=d)
synDE2SE2 = Synapses(decE2,
dend2,
model='w : 1',
method=nummethod,
on_pre='x_ea += w',
delay=d)
else:
# normal sensory circuit (Wimmer)
Sgroups, Ssynapses, Ssubgroups = mk_sencircuit(task_info)
senE = Sgroups['SE']
senI = Sgroups['SI']
senE1 = Ssubgroups['SE1']
senE2 = Ssubgroups['SE2']
# FB
wDS = 0.004 # synaptic weight of FB synapses, 0.0668 nS when scaled by gleakE of sencircuit
synDE1SE1 = Synapses(decE1,
senE1,
model='w : 1',
method=nummethod,
on_pre='x_ea += w',
delay=d)
synDE2SE2 = Synapses(decE2,
senE2,
model='w : 1',
method=nummethod,
on_pre='x_ea += w',
delay=d)
# feedforward synapses from sensory to integration
wSD = 0.0036 # synaptic weight of FF synapses, 0.09 nS when scaled by gleakE of intcircuit
synSE1DE1 = Synapses(senE1,
decE1,
model='w : 1',
method=nummethod,
on_pre='g_ea += w',
delay=d)
synSE1DE1.connect(p='eps')
synSE1DE1.w = 'wSD'
synSE2DE2 = Synapses(senE2,
decE2,
model='w : 1',
method=nummethod,
on_pre='g_ea += w',
delay=d)
synSE2DE2.connect(p='eps')
synSE2DE2.w = 'wSD'
# feedback synapses from integration to sensory
b_fb = task_info['bfb'] # feedback strength, between 0 and 6
wDS *= b_fb # synaptic weight of FB synapses, 0.0668 nS when scaled by gleakE of sencircuit
synDE1SE1.connect(p='eps')
synDE1SE1.w = 'wDS'
synDE2SE2.connect(p='eps')
synDE2SE2.w = 'wDS'
# -------------------------------------
# Create stimuli
# -------------------------------------
if task_info['stimulus']['replicate']:
# replicated stimuli across iters()
np.random.seed(task_info['seed']) # numpy seed for OU process
else:
# every trials has different stimuli
np.random.seed()
# Note that in standalone we need to specify np seed because it's not taken care with Brian's seed() function!
if task_info['simulation']['2cmodel']:
I0 = task_info['stimulus']['I0s']
last_muOUd = np.loadtxt("last_muOUd.csv") # save the mean
else:
I0 = task_info['stimulus'][
'I0'] # mean input current for zero-coherence stim
c = task_info['c'] # stim coherence (between 0 and 1)
mu1 = task_info['stimulus'][
'mu1'] # av. additional input current to senE1 at highest coherence (c=1)
mu2 = task_info['stimulus'][
'mu2'] # av. additional input current to senE2 at highest coherence (c=1)
sigma = task_info['stimulus'][
'sigma'] # amplitude of temporal modulations of stim
sigmastim = 0.212 * sigma # std of modulation of stim inputs
sigmaind = 0.212 * sigma # std of modulations in individual inputs
taustim = task_info['stimulus'][
'taustim'] # correlation time constant of Ornstein-Uhlenbeck process
# generate stim from OU process
N_stim = int(senE1.__len__())
z1, z2, zk1, zk2 = generate_stim(N_stim, stimdur, taustim)
# stim2exc
i1 = I0 * (1 + c * mu1 + sigmastim * z1 + sigmaind * zk1)
i2 = I0 * (1 + c * mu2 + sigmastim * z2 + sigmaind * zk2)
stim_dt = 1 * ms
i1t = np.concatenate((np.zeros((int(stimon / ms), N_stim)), i1.T,
np.zeros((int(
(runtime - stimoff) / stim_dt), N_stim))),
axis=0)
i2t = np.concatenate((np.zeros((int(stimon / ms), N_stim)), i2.T,
np.zeros((int(
(runtime - stimoff) / stim_dt), N_stim))),
axis=0)
Irec = TimedArray(np.concatenate((i1t, i2t), axis=1) * amp, dt=stim_dt)
# -------------------------------------
# Simulation
# -------------------------------------
# set initial conditions (different for evert trial)
seed()
decE.g_ea = '0.2 * rand()'
decI.g_ea = '0.2 * rand()'
decE.V = '-52*mV + 2*mV * rand()'
decI.V = '-52*mV + 2*mV * rand()' # random initialization near 0, prevent an early decision!
senE.g_ea = '0.05 * (1 + 0.2*rand())'
senI.g_ea = '0.05 * (1 + 0.2*rand())'
senE.V = '-52*mV + 2*mV*rand()' # random initialization near Vt, avoid initial bump!
senI.V = '-52*mV + 2*mV*rand()'
if task_info['simulation']['2cmodel']:
dend.g_ea = '0.05 * (1 + 0.2*rand())'
dend.V_d = '-72*mV + 2*mV*rand()'
dend.muOUd = np.tile(last_muOUd, 2) * amp
# create monitors
rateDE1 = PopulationRateMonitor(decE1)
rateDE2 = PopulationRateMonitor(decE2)
rateSE1 = PopulationRateMonitor(senE1)
rateSE2 = PopulationRateMonitor(senE2)
subSE = int(senE1.__len__())
spksSE = SpikeMonitor(senE[subSE - 100:subSE +
100]) # last 100 of SE1 and first 100 of SE2
# construct network
net = Network(Dgroups.values(),
Dsynapses.values(),
Sgroups.values(),
Ssynapses.values(),
synSE1DE1,
synSE2DE2,
synDE1SE1,
synDE2SE2,
rateDE1,
rateDE2,
rateSE1,
rateSE2,
spksSE,
name='hierarchicalnet')
# create more monitors for plot
if task_info['simulation']['pltfig1']:
# inh
rateDI = PopulationRateMonitor(decI)
rateSI = PopulationRateMonitor(senI)
# spk monitors
subDE = int(decE1.__len__() * 2)
spksDE = SpikeMonitor(decE[:subDE])
spksSE = SpikeMonitor(senE)
# state mons no more, just the arrays
stim1 = i1t.T
stim2 = i2t.T
stimtime = np.linspace(0, runtime_, stim1.shape[1])
# construct network
net = Network(Dgroups.values(),
Dsynapses.values(),
Sgroups.values(),
Ssynapses.values(),
synSE1DE1,
synSE2DE2,
synDE1SE1,
synDE2SE2,
spksDE,
rateDE1,
rateDE2,
rateDI,
spksSE,
rateSE1,
rateSE2,
rateSI,
name='hierarchicalnet')
if task_info['simulation']['plasticdend']:
# create state monitor to follow muOUd and add it to the networks
dend_mon = StateMonitor(dend1,
variables=['muOUd', 'Ibg', 'g_ea', 'B'],
record=True,
dt=1 * ms)
net.add(dend_mon)
# remove FB synapses!
net.remove([synDE1SE1, synDE2SE2, Dsynapses.values()])
print(
" FB synapses and synapses of decision circuit are ignored in this simulation!"
)
# run hierarchical net
net.run(runtime, report='stdout', profile=True)
print(profiling_summary(net=net, show=10))
# nice plots on cluster
if task_info['simulation']['pltfig1']:
plot_fig1b([
rateDE1, rateDE2, rateDI, spksDE, rateSE1, rateSE2, rateSI, spksSE,
stim1, stim2, stimtime
], smoothwin, taskdir)
# -------------------------------------
# Burst quantification
# -------------------------------------
events = np.zeros(1)
bursts = np.zeros(1)
singles = np.zeros(1)
spikes = np.zeros(1)
last_muOUd = np.zeros(1)
# neurometric params
dt = spksSE.clock.dt
validburst = task_info['sen']['2c']['validburst']
smoothwin_ = smoothwin / second
if task_info['simulation']['burstanalysis']:
if task_info['simulation']['2cmodel']:
last_muOUd = np.array(dend_mon.muOUd[:, -int(1e3):].mean(axis=1))
if task_info['simulation']['plasticdend']:
# calculate neurometric info per population
events, bursts, singles, spikes, isis = spks2neurometric(
spksSE,
runtime,
settletime,
validburst,
smoothwin=smoothwin_,
raster=False)
# plot & save weigths after convergence
eta0 = task_info['sen']['2c']['eta0']
tauB = task_info['sen']['2c']['tauB']
targetB = task_info['targetB']
B0 = tauB * targetB
tau_update = task_info['sen']['2c']['tau_update']
eta = eta0 * tau_update / tauB
plot_weights(dend_mon, events, bursts, spikes,
[targetB, B0, eta, tauB, tau_update, smoothwin_],
taskdir)
plot_rasters(spksSE, bursts, targetB, isis, runtime_, taskdir)
else:
# calculate neurometric per neuron
events, bursts, singles, spikes, isis = spks2neurometric(
spksSE,
runtime,
settletime,
validburst,
smoothwin=smoothwin_,
raster=True)
plot_neurometric(events, bursts, spikes, stim1, stim2, stimtime,
(settletime_, runtime_), taskdir, smoothwin_)
plot_isis(isis, bursts, events, (settletime_, runtime_), taskdir)
# -------------------------------------
# Choice selection
# -------------------------------------
# population rates and downsample
originaltime = rateDE1.t / second
interptime = np.linspace(0, originaltime[-1],
originaltime[-1] * 100) # every 10 ms
fDE1 = interpolate.interp1d(
originaltime, rateDE1.smooth_rate(window='flat', width=smoothwin))
fDE2 = interpolate.interp1d(
originaltime, rateDE2.smooth_rate(window='flat', width=smoothwin))
fSE1 = interpolate.interp1d(
originaltime, rateSE1.smooth_rate(window='flat', width=smoothwin))
fSE2 = interpolate.interp1d(
originaltime, rateSE2.smooth_rate(window='flat', width=smoothwin))
rateDE = np.array([f(interptime) for f in [fDE1, fDE2]])
rateSE = np.array([f(interptime) for f in [fSE1, fSE2]])
# select the last half second of the stimulus
newdt = runtime_ / rateDE.shape[1]
settletimeidx = int(settletime_ / newdt)
dec_ival = np.array([(stimoff_ - 0.5) / newdt, stimoff_ / newdt],
dtype=int)
who_wins = rateDE[:, dec_ival[0]:dec_ival[1]].mean(axis=1)
# divide trls into preferred and non-preferred
pref_msk = np.argmax(who_wins)
poprates_dec = np.array([rateDE[pref_msk],
rateDE[~pref_msk]]) # 0: pref, 1: npref
poprates_sen = np.array([rateSE[pref_msk], rateSE[~pref_msk]])
results = {
'raw_data': {
'poprates_dec': poprates_dec[:, settletimeidx:],
'poprates_sen': poprates_sen[:, settletimeidx:],
'pref_msk': np.array([pref_msk]),
'last_muOUd': last_muOUd
},
'sim_state': np.zeros(1),
'computed': {
'events': events,
'bursts': bursts,
'singles': singles,
'spikes': spikes,
'isis': np.array(isis)
}
}
return results