def Add_NMDA_SingleSynapticEventToSegment(segment, activationTime,
synapseWeight, exc_inh):
if exc_inh == 0: # inhibitory
synapse = h.ProbGABAAB_EMS(segment) #GABAA/B
synapse.tau_r_GABAA = 0.2
synapse.tau_d_GABAA = 8
synapse.tau_r_GABAB = 3.5
synapse.tau_d_GABAB = 260.9
# synapse.gmax = .001
synapse.e_GABAA = -80
synapse.e_GABAB = -97
synapse.GABAB_ratio = 0.0
else: # excitatory
synapse = h.ProbAMPANMDA2(segment)
synapse.gmax = .0004
synapse.Use = 1.0
synapse.Dep = 0
synapse.Fac = 0
netStimulation = h.NetStim()
netStimulation.number = 1
netStimulation.start = activationTime
netConnection = h.NetCon(netStimulation, synapse)
netConnection.delay = 0
netConnection.weight[0] = synapseWeight
return netStimulation, netConnection, synapse
def DefineSynapse_NMDA(segment, gMax=0.0004):
synapse = h.ProbAMPANMDA2(segment)
synapse.tau_r_AMPA = 0.3
synapse.tau_d_AMPA = 3.0
synapse.tau_r_NMDA = 2.0
synapse.tau_d_NMDA = 70.0
synapse.gmax = gMax
synapse.e = 0
synapse.Use = 1
synapse.u0 = 0
synapse.Dep = 0
synapse.Fac = 0
return synapse
def Add_NMDA_SingleSynapticEventToSegment(segment, activationTime,
synapseWeight):
# synapse = h.ProbAMPANMDA_EMS(segLoc,sec=section)
synapse = h.ProbAMPANMDA2(segment)
synapse.gmax = .0004
synapse.Use = 1.0
synapse.Dep = 0
synapse.Fac = 0
netStimulation = h.NetStim()
netStimulation.number = 1
netStimulation.start = activationTime
netConnection = h.NetCon(netStimulation, synapse)
netConnection.delay = 0
netConnection.weight[0] = synapseWeight
return netStimulation, netConnection, synapse
def AMPANMDA(syn_params, sec_x, sec_id):
"""Create a bg2pyr synapse
:param syn_params: parameters of a synapse
:param sec_x: normalized distance along the section
:param sec_id: target section
:return: NEURON synapse object
"""
lsyn = h.ProbAMPANMDA2(sec_x, sec=sec_id)
if syn_params.get('tau_r_AMPA'):
lsyn.tau_r_AMPA = float(syn_params['tau_r_AMPA'])
if syn_params.get('tau_d_AMPA'):
lsyn.tau_d_AMPA = float(syn_params['tau_d_AMPA'])
if syn_params.get('tau_r_NMDA'):
lsyn.tau_r_NMDA = float(syn_params['tau_r_NMDA'])
if syn_params.get('tau_d_NMDA'):
lsyn.tau_d_NMDA = float(syn_params['tau_d_NMDA'])
if syn_params.get('Use'):
lsyn.Use = float(syn_params['Use'])
if syn_params.get('Dep'):
lsyn.Dep = float(syn_params['Dep'])
if syn_params.get('Fac'):
lsyn.Fac = float(syn_params['Fac'])
if syn_params.get('e'):
lsyn.e = float(syn_params['e'])
if syn_params.get('initW'):
h.distance(sec=sec_id.cell().soma[0])
dist = h.distance(sec_id(sec_x))
fullsecname = sec_id.name()
sec_type = fullsecname.split(".")[1][:4]
sec_id = int(fullsecname.split("[")[-1].split("]")[0])
# if pyrWeight_s == 0:
# base = float(pyrWeight_m)
# else:
# base = float(np.clip(lognormal(pyrWeight_m, pyrWeight_s), 0, 5))
####OLD
# dend = lambda x: 0.9278403931213186 * ( 1.0022024845737223 ** x )
# close_apic = lambda x: 0.9131511669645764 * ( 1.0019436631560847 ** x )
# far_apic = lambda x: 0.16857988107990907 * ( 1.0039628707324273 ** x )
#############
#distance based conductance scaling functions.
#dend = lambda x: 0.9475625702815389 * ( 1.001318965242205 ** x )
#close_apic = lambda x: 0.8522367331040966 * ( 1.0020433032052223 ** x )
#far_apic = lambda x: 0.09043087364217033 * ( 1.004632615014859 ** x )
dend = lambda x: (1.001**x)
close_apic = lambda x: (1.002**x)
#far_apic = lambda x: ( 1.002 ** x )
far_apic = lambda x: 1
if sec_type == "dend":
base = float(np.clip(lognormal(pyrWeight_m, pyrWeight_s), 0, 5))
lsyn.initW = base * dend(dist)
elif sec_type == "apic":
if dist < 750:
base = float(np.clip(lognormal(pyrWeight_m, pyrWeight_s), 0,
5))
lsyn.initW = base * close_apic(dist)
else:
base = float(np.clip(lognormal(0.17, 0.2), 0, 5))
lsyn.initW = base * far_apic(dist)
lsyn.initW = np.clip(float(lsyn.initW), 0, 5)
if syn_params.get('u0'):
lsyn.u0 = float(syn_params['u0'])
return lsyn