def Pyr2Pyr(syn_params, sec_x, sec_id):
"""Create a pyr2pyr 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.pyr2pyr(sec_x, sec=sec_id)
if syn_params.get('AlphaTmax_ampa'):
lsyn.AlphaTmax_ampa = float(syn_params['AlphaTmax_ampa']) # par.x(21)
if syn_params.get('Beta_ampa'):
lsyn.Beta_ampa = float(syn_params['Beta_ampa']) # par.x(22)
if syn_params.get('Cdur_ampa'):
lsyn.Cdur_ampa = float(syn_params['Cdur_ampa']) # par.x(23)
if syn_params.get('gbar_ampa'):
lsyn.gbar_ampa = float(syn_params['gbar_ampa']) # par.x(24)
if syn_params.get('Erev_ampa'):
lsyn.Erev_ampa = float(syn_params['Erev_ampa']) # par.x(16)
if syn_params.get('AlphaTmax_nmda'):
lsyn.AlphaTmax_nmda = float(syn_params['AlphaTmax_nmda']) # par.x(25)
if syn_params.get('Beta_nmda'):
lsyn.Beta_nmda = float(syn_params['Beta_nmda']) # par.x(26)
if syn_params.get('Cdur_nmda'):
lsyn.Cdur_nmda = float(syn_params['Cdur_nmda']) # par.x(27)
if syn_params.get('gbar_nmda'):
lsyn.gbar_nmda = float(syn_params['gbar_nmda']) # par.x(28)
if syn_params.get('Erev_nmda'):
lsyn.Erev_nmda = float(syn_params['Erev_nmda']) # par.x(16)
if syn_params.get('initW'):
lsyn.initW = float(syn_params['initW']) * random.uniform(0.5,1.0) # par.x(0) * rC.uniform(0.5,1.0)//rand.normal(0.5,1.5) //`rand.repick()
if syn_params.get('Wmax'):
lsyn.Wmax = float(syn_params['Wmax']) * lsyn.initW # par.x(1) * lsyn.initW
if syn_params.get('Wmin'):
lsyn.Wmin = float(syn_params['Wmin']) * lsyn.initW # par.x(2) * lsyn.initW
#delay = float(syn_params['initW']) # par.x(3) + delayDistance
#lcon = new NetCon(&v(0.5), lsyn, 0, delay, 1)
if syn_params.get('lambda1'):
lsyn.lambda1 = float(syn_params['lambda1']) # par.x(6)
if syn_params.get('lambda2'):
lsyn.lambda2 = float(syn_params['lambda2']) # par.x(7)
if syn_params.get('threshold1'):
lsyn.threshold1 = float(syn_params['threshold1']) # par.x(8)
if syn_params.get('threshold2'):
lsyn.threshold2 = float(syn_params['threshold2']) # par.x(9)
if syn_params.get('tauD1'):
lsyn.tauD1 = float(syn_params['tauD1']) # par.x(10)
if syn_params.get('d1'):
lsyn.d1 = float(syn_params['d1']) # par.x(11)
if syn_params.get('tauD2'):
lsyn.tauD2 = float(syn_params['tauD2']) # par.x(12)
if syn_params.get('d2'):
lsyn.d2 = float(syn_params['d2']) # par.x(13)
if syn_params.get('tauF'):
lsyn.tauF = float(syn_params['tauF']) # par.x(14)
if syn_params.get('f'):
lsyn.f = float(syn_params['f']) # par.x(15)
if syn_params.get('bACH'):
lsyn.bACH = float(syn_params['bACH']) # par.x(17)
if syn_params.get('aDA'):
lsyn.aDA = float(syn_params['aDA']) # par.x(18)
if syn_params.get('bDA'):
lsyn.bDA = float(syn_params['bDA']) # par.x(19)
if syn_params.get('wACH'):
lsyn.wACH = float(syn_params['wACH']) # par.x(20)
return lsyn
def Pyr2Pyr(syn_params, sec_x, sec_id):
"""Create a pyr2pyr 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
"""
# #import pdb; pdb.set_trace()
# trg_cell_nid = int(str(sec_id).split("[")[1].split("]")[0])
# # if trg_cell_nid > 0:
# # import pdb; pdb.set_trace()
# if trg_cell_nid in weight_means["exc"].keys():
# mean_weight = weight_means["exc"][trg_cell_nid]
# else:
# weight_means["exc"][trg_cell_nid] = mean_weight = np.random.uniform(0.18181829517744805 - 0.18, 0.18181829517744805 + 0.28)
# #weight_means["exc"][trg_cell_nid] = mean_weight = np.random.uniform(0.18181829517744805 + 0.32, 0.18181829517744805 + 0.35)
lsyn = h.pyr2pyr(sec_x, sec=sec_id)
#Assigns random generator of release probability.
r = h.Random()
r.MCellRan4()
r.uniform(0,1)
lsyn.setRandObjRef(r)
generators.append(r)
#lsyn.P_0 = 0.1
#import pdb; pdb.set_trace()
if syn_params.get('AlphaTmax_ampa'):
lsyn.AlphaTmax_ampa = float(syn_params['AlphaTmax_ampa']) # par.x(21)
if syn_params.get('Beta_ampa'):
lsyn.Beta_ampa = float(syn_params['Beta_ampa']) # par.x(22)
if syn_params.get('Cdur_ampa'):
lsyn.Cdur_ampa = float(syn_params['Cdur_ampa']) # par.x(23)
if syn_params.get('gbar_ampa'):
lsyn.gbar_ampa = float(syn_params['gbar_ampa']) # par.x(24)
if syn_params.get('Erev_ampa'):
lsyn.Erev_ampa = float(syn_params['Erev_ampa']) # par.x(16)
if syn_params.get('AlphaTmax_nmda'):
lsyn.AlphaTmax_nmda = float(syn_params['AlphaTmax_nmda']) # par.x(25)
if syn_params.get('Beta_nmda'):
lsyn.Beta_nmda = float(syn_params['Beta_nmda']) # par.x(26)
if syn_params.get('Cdur_nmda'):
lsyn.Cdur_nmda = float(syn_params['Cdur_nmda']) # par.x(27)
if syn_params.get('gbar_nmda'):
lsyn.gbar_nmda = float(syn_params['gbar_nmda']) # par.x(28)
if syn_params.get('Erev_nmda'):
lsyn.Erev_nmda = float(syn_params['Erev_nmda']) # par.x(16)
#global max_exc
if syn_params.get('initW'):
#lsyn.initW = float(syn_params['initW']) * random.uniform(0.5,1.0) # par.x(0) * rC.uniform(0.5,1.0)//rand.normal(0.5,1.5) //`rand.repick()
#lsyn.initW = float(min(lognormal(2.5*0.18181829517744805, 0.13993260156705545), 0.8) * scale)
lsyn.initW = 0.5#float(min(lognormal(0.495, 0.09), 0.8) * scale)
#lsyn.initW = 5
# if (lsyn.initW > max_exc):
# max_exc = lsyn.initW
#lsyn.initW = float(lognormal(mean_weight, 0.13993260156705545) * scale)
#lsyn.initW = float(min(lognormal(mean_weight, 0.22), 1.20) * scale)
#lsyn.initW = 0.18181829517744805 * 10
#print(lsyn.initW)
if syn_params.get('Wmax'):
lsyn.Wmax = 8#float(syn_params['Wmax']) * lsyn.initW # par.x(1) * lsyn.initW
if syn_params.get('Wmin'):
lsyn.Wmin = float(syn_params['Wmin']) * lsyn.initW # par.x(2) * lsyn.initW
#delay = float(syn_params['initW']) # par.x(3) + delayDistance
#lcon = new NetCon(&v(0.5), lsyn, 0, delay, 1)
if syn_params.get('lambda1'):
lsyn.lambda1 = float(syn_params['lambda1']) # par.x(6)
if syn_params.get('lambda2'):
lsyn.lambda2 = float(syn_params['lambda2']) # par.x(7)
if syn_params.get('threshold1'):
lsyn.threshold1 = float(syn_params['threshold1']) # par.x(8)
if syn_params.get('threshold2'):
lsyn.threshold2 = float(syn_params['threshold2']) # par.x(9)
if syn_params.get('tauD1'):
lsyn.tauD1 = float(syn_params['tauD1']) # par.x(10)
if syn_params.get('d1'):
lsyn.d1 = float(syn_params['d1']) # par.x(11)
if syn_params.get('tauD2'):
lsyn.tauD2 = float(syn_params['tauD2']) # par.x(12)
if syn_params.get('d2'):
lsyn.d2 = float(syn_params['d2']) # par.x(13)
if syn_params.get('tauF'):
lsyn.tauF = float(syn_params['tauF']) # par.x(14)
if syn_params.get('f'):
lsyn.f = float(syn_params['f']) # par.x(15)
if syn_params.get('bACH'):
lsyn.bACH = float(syn_params['bACH']) # par.x(17)
if syn_params.get('aDA'):
lsyn.aDA = float(syn_params['aDA']) # par.x(18)
if syn_params.get('bDA'):
lsyn.bDA = float(syn_params['bDA']) # par.x(19)
if syn_params.get('wACH'):
lsyn.wACH = float(syn_params['wACH']) # par.x(20)
return lsyn
def Pyr2Pyr(syn_params, sec_x, sec_id):
"""Create a pyr2pyr 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.pyr2pyr(sec_x, sec=sec_id)
#Assigns random generator of release probability.
r = h.Random()
r.MCellRan4()
r.uniform(0, 1)
lsyn.setRandObjRef(r)
#A list of random generators is kept so that they are not automatically garbaged.
generators.append(r)
lsyn.P_0 = np.clip(np.random.normal(0.53, 0.22), 0,
1) #Release probability
if syn_params.get('AlphaTmax_ampa'):
lsyn.AlphaTmax_ampa = float(syn_params['AlphaTmax_ampa']) # par.x(21)
if syn_params.get('Beta_ampa'):
lsyn.Beta_ampa = float(syn_params['Beta_ampa']) # par.x(22)
if syn_params.get('Cdur_ampa'):
lsyn.Cdur_ampa = float(syn_params['Cdur_ampa']) # par.x(23)
if syn_params.get('gbar_ampa'):
lsyn.gbar_ampa = float(syn_params['gbar_ampa']) # par.x(24)
if syn_params.get('Erev_ampa'):
lsyn.Erev_ampa = float(syn_params['Erev_ampa']) # par.x(16)
if syn_params.get('AlphaTmax_nmda'):
lsyn.AlphaTmax_nmda = float(syn_params['AlphaTmax_nmda']) # par.x(25)
if syn_params.get('Beta_nmda'):
lsyn.Beta_nmda = float(syn_params['Beta_nmda']) # par.x(26)
if syn_params.get('Cdur_nmda'):
lsyn.Cdur_nmda = float(syn_params['Cdur_nmda']) # par.x(27)
if syn_params.get('gbar_nmda'):
lsyn.gbar_nmda = float(syn_params['gbar_nmda']) # par.x(28)
if syn_params.get('Erev_nmda'):
lsyn.Erev_nmda = float(syn_params['Erev_nmda']) # par.x(16)
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('Wmax'):
lsyn.Wmax = float(
syn_params['Wmax']) * lsyn.initW # par.x(1) * lsyn.initW
if syn_params.get('Wmin'):
lsyn.Wmin = float(
syn_params['Wmin']) * lsyn.initW # par.x(2) * lsyn.initW
#delay = float(syn_params['initW']) # par.x(3) + delayDistance
#lcon = new NetCon(&v(0.5), lsyn, 0, delay, 1)
if syn_params.get('lambda1'):
lsyn.lambda1 = float(syn_params['lambda1']) # par.x(6)
if syn_params.get('lambda2'):
lsyn.lambda2 = float(syn_params['lambda2']) # par.x(7)
if syn_params.get('threshold1'):
lsyn.threshold1 = float(syn_params['threshold1']) # par.x(8)
if syn_params.get('threshold2'):
lsyn.threshold2 = float(syn_params['threshold2']) # par.x(9)
if syn_params.get('tauD1'):
lsyn.tauD1 = float(syn_params['tauD1']) # par.x(10)
if syn_params.get('d1'):
lsyn.d1 = float(syn_params['d1']) # par.x(11)
if syn_params.get('tauD2'):
lsyn.tauD2 = float(syn_params['tauD2']) # par.x(12)
if syn_params.get('d2'):
lsyn.d2 = float(syn_params['d2']) # par.x(13)
if syn_params.get('tauF'):
lsyn.tauF = float(syn_params['tauF']) # par.x(14)
if syn_params.get('f'):
lsyn.f = float(syn_params['f']) # par.x(15)
if syn_params.get('bACH'):
lsyn.bACH = float(syn_params['bACH']) # par.x(17)
if syn_params.get('aDA'):
lsyn.aDA = float(syn_params['aDA']) # par.x(18)
if syn_params.get('bDA'):
lsyn.bDA = float(syn_params['bDA']) # par.x(19)
if syn_params.get('wACH'):
lsyn.wACH = float(syn_params['wACH']) # par.x(20)
return lsyn
def Pyr2Pyr(syn_params, sec_x, sec_id):
"""Create a pyr2pyr 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.pyr2pyr(sec_x, sec=sec_id)
#Assigns random generator of release probability.
r = h.Random()
r.MCellRan4()
r.uniform(0, 1)
lsyn.setRandObjRef(r)
generators.append(r)
lsyn.P_0 = np.max(np.random.normal(0.53, 0.22), 0) #Release probability
if syn_params.get('AlphaTmax_ampa'):
lsyn.AlphaTmax_ampa = float(syn_params['AlphaTmax_ampa']) # par.x(21)
if syn_params.get('Beta_ampa'):
lsyn.Beta_ampa = float(syn_params['Beta_ampa']) # par.x(22)
if syn_params.get('Cdur_ampa'):
lsyn.Cdur_ampa = float(syn_params['Cdur_ampa']) # par.x(23)
if syn_params.get('gbar_ampa'):
lsyn.gbar_ampa = float(syn_params['gbar_ampa']) # par.x(24)
if syn_params.get('Erev_ampa'):
lsyn.Erev_ampa = float(syn_params['Erev_ampa']) # par.x(16)
if syn_params.get('AlphaTmax_nmda'):
lsyn.AlphaTmax_nmda = float(syn_params['AlphaTmax_nmda']) # par.x(25)
if syn_params.get('Beta_nmda'):
lsyn.Beta_nmda = float(syn_params['Beta_nmda']) # par.x(26)
if syn_params.get('Cdur_nmda'):
lsyn.Cdur_nmda = float(syn_params['Cdur_nmda']) # par.x(27)
if syn_params.get('gbar_nmda'):
lsyn.gbar_nmda = float(syn_params['gbar_nmda']) # par.x(28)
if syn_params.get('Erev_nmda'):
lsyn.Erev_nmda = float(syn_params['Erev_nmda']) # par.x(16)
if syn_params.get('initW'):
#lsyn.initW = float(syn_params['initW']) * 0.0000001#random.uniform(0.5,1.0) # par.x(0) * rC.uniform(0.5,1.0)//rand.normal(0.5,1.5) //`rand.repick()
#lsyn.initW = float(pyrWeight)
#lsyn.initW = float(np.random.uniform(pyrWeight_m - pyrWeight_s, pyrWeight_m + pyrWeight_s))
#import pdb; pdb.set_trace()
#import pdb; pdb.set_trace()
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(min(lognormal(pyrWeight_m, pyrWeight_s), 15))
#base = float(max(0.001, min(np.random.normal(pyrWeight_m, pyrWeight_s), 15)))
#base = np.random.lognormal(pyrWeight_m, np.sqrt(pyrWeight_s), 1)
####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 )
#############
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)
if sec_type == "dend":
#lsyn.initW = base * (0.9278403931213186 * ( 1.0022024845737223 ** dist ))
lsyn.initW = base * dend(dist)
elif sec_type == "apic":
if dist < 750:
#lsyn.initW = base * (0.9131511669645764 * ( 1.0019436631560847 ** dist))
lsyn.initW = base * close_apic(dist)
else:
lsyn.initW = base * far_apic(dist)
#lsyn.initW = base * (0.16857988107990907 * ( 1.0039628707324273 ** dist))
# if sec_id >= 60:
# lsyn.initW = base * (0.59768734 * (1.00326839 ** dist))
# else:
# lsyn.initW = base * (0.62153507 * (1.00248601 ** dist))
#lsyn.initW = min(float(lsyn.initW), 1000)
lsyn.initW = min(float(lsyn.initW), 50)
#lsyn.initW = np.random.uniform(0.02, 2)
#lsyn.initW = pyrWeight_m
#lsyn.initW = float(min(lognormal(pyrWeight_m, pyrWeight_s), 8))
if syn_params.get('Wmax'):
lsyn.Wmax = float(
syn_params['Wmax']) * lsyn.initW # par.x(1) * lsyn.initW
if syn_params.get('Wmin'):
lsyn.Wmin = float(
syn_params['Wmin']) * lsyn.initW # par.x(2) * lsyn.initW
#delay = float(syn_params['initW']) # par.x(3) + delayDistance
#lcon = new NetCon(&v(0.5), lsyn, 0, delay, 1)
if syn_params.get('lambda1'):
lsyn.lambda1 = float(syn_params['lambda1']) # par.x(6)
if syn_params.get('lambda2'):
lsyn.lambda2 = float(syn_params['lambda2']) # par.x(7)
if syn_params.get('threshold1'):
lsyn.threshold1 = float(syn_params['threshold1']) # par.x(8)
if syn_params.get('threshold2'):
lsyn.threshold2 = float(syn_params['threshold2']) # par.x(9)
if syn_params.get('tauD1'):
lsyn.tauD1 = float(syn_params['tauD1']) # par.x(10)
if syn_params.get('d1'):
lsyn.d1 = float(syn_params['d1']) # par.x(11)
if syn_params.get('tauD2'):
lsyn.tauD2 = float(syn_params['tauD2']) # par.x(12)
if syn_params.get('d2'):
lsyn.d2 = float(syn_params['d2']) # par.x(13)
if syn_params.get('tauF'):
lsyn.tauF = float(syn_params['tauF']) # par.x(14)
if syn_params.get('f'):
lsyn.f = float(syn_params['f']) # par.x(15)
if syn_params.get('bACH'):
lsyn.bACH = float(syn_params['bACH']) # par.x(17)
if syn_params.get('aDA'):
lsyn.aDA = float(syn_params['aDA']) # par.x(18)
if syn_params.get('bDA'):
lsyn.bDA = float(syn_params['bDA']) # par.x(19)
if syn_params.get('wACH'):
lsyn.wACH = float(syn_params['wACH']) # par.x(20)
return lsyn