def addSegmentSynapse(self, layerDict, dist2sec, distKeys, synList,
synType):
for layer in layerDict:
for dist in distKeys:
sec_choice = dist2sec[dist]
for seg_choice in layerDict[layer][sec_choice]:
nmda_flag = 0
if self.synvars['type'] == "E2-NMDA2":
syn = h.Exp2Syn(sec_choice(seg_choice))
nmda = h.Exp2NMDA_Wang(sec_choice(seg_choice))
nmda_flag = 1
if self.synvars['type'] == "Log_E2Syn":
syn = h.Log_E2Syn(sec_choice(seg_choice))
if self.synvars['type'] == "Add_E2Syn":
syn = h.Add_E2Syn(sec_choice(seg_choice))
if self.synvars['type'] == "Mult_E2Syn":
syn = h.Mult_E2Syn(sec_choice(seg_choice))
if self.synvars['type'] == "E2":
syn = h.Exp2Syn(sec_choice(seg_choice))
if self.synvars['type'] == "E2_Prob":
syn = h.E2_Prob(sec_choice(seg_choice))
syn.P = self.synvars['P']
if self.synvars['type'] == "E2_STP_Prob":
syn = h.E2_STP_Prob(sec_choice(seg_choice))
if self.synvars['type'] == "STDPE2":
syn = h.STDPE2(sec_choice(seg_choice))
if self.synvars['type'] == "STDPE2_Clo":
syn = h.STDPE2_Clo(sec_choice(seg_choice))
if self.synvars['type'] == "STDPE2_STP":
syn = h.STDPE2_STP(sec_choice(seg_choice))
if self.synvars['type'] == "STDPE2_Prob":
syn = h.STDPE2_Prob(sec_choice(seg_choice))
syn.P = self.synvars['P']
#initializes different variables depending on synapse
if (self.synvars['type'] == "STDPE2_STP") | (
self.synvars['type'] == "E2_STP_Prob"):
syn.F1 = self.synvars['F1']
if (self.synvars['type'] == "STDPE2_Clo") | (
self.synvars['type'] == "STDPE2_STP") | (
self.synvars['type'] == "STDPE2") | (
self.synvars['type'] == "STDPE2_Prob"):
syn.wmax = self.synvars['wmax']
syn.wmin = self.synvars['wmin']
syn.thresh = self.synvars['thresh']
if (self.synvars['type'] == "E2_Prob") | (
self.synvars['type'] == "E2_STP_Prob") | (
self.synvars['type'] == "STDPE2_STP") | (
self.synvars['type'] == "STDPE2_Prob"):
h.use_mcell_ran4(1)
syn.seed = self.ranGen.randint(1, 4.295e9)
syn.tau1 = 0.5
syn.tau2 = 0.6
syn.e = 0
if nmda_flag == 1:
if synType == 'NMDA':
synList[layer].append(nmda)
else:
synList[layer].append(syn)
else:
synList[layer].append(syn)
def createSyn(synvars,sec_choice,seg_choice):
if synvars['type'] == "E3_NMDA":
syn = h.E3_NMDA(sec_choice(seg_choice))
if synvars['type'] == "E2":
syn = h.Exp2Syn(sec_choice(seg_choice))
if synvars['type'] == "E2_Prob":
syn = h.E2_Prob(sec_choice(seg_choice))
syn.P = synvars['P']
if synvars['type'] == "E2_STP_Prob":
syn = h.E2_STP_Prob(sec_choice(seg_choice))
if synvars['type'] == "STDPE2":
syn = h.STDPE2(sec_choice(seg_choice))
if synvars['type'] == "STDPE2_Clo":
syn = h.STDPE2_Clo(sec_choice(seg_choice))
if synvars['type'] == "STDPE2_STP" :
syn = h.STDPE2_STP(sec_choice(seg_choice))
if synvars['type'] == "STDPE2_Prob":
syn = h.STDPE2_Prob(sec_choice(seg_choice))
syn.P = synvars['P']
#initializes different variables depending on synapse
if (synvars['type'] == "STDPE2_STP")|(synvars['type'] == "E2_STP_Prob"):
syn.F1 = synvars['F1']
if (synvars['type'] == "STDPE2_Clo" )|( synvars['type'] == "STDPE2_STP")|( synvars['type'] == "STDPE2")| (synvars['type'] == "STDPE2_Prob"):
syn.wmax = synvars['wmax']
syn.wmin = synvars['wmin']
syn.thresh = synvars['thresh']
if (synvars['type'] == "E2_Prob" )|( synvars['type'] == "E2_STP_Prob")|(synvars['type'] == "STDPE2_STP") | (synvars['type'] == "STDPE2_Prob"):
h.use_mcell_ran4(1)
syn.seed = self.ranGen.randint(1,4.295e9)
syn.tau1 = 0.5
syn.tau2 = 0.6
syn.e = 0
return syn
def addSegmentSynapse(self, layerDict, list, synList, synType, in_param):
s = 0
for layer in sorted(layerDict.keys()):
c = 0
for sec_choice in list:
dict = layerDict[layer]
for seg_choice in dict[sec_choice]:
nmda_flag = 0
if self.synvars['type'] == "E2-NMDA2":
syn = h.Exp2Syn(sec_choice(seg_choice))
nmda = h.Exp2NMDA_Wang(sec_choice(seg_choice))
nmda_flag = 1
if self.synvars['type'] == "E2":
if layer == in_param["syn_layer"] and c == in_param[
"syn_id"]:
print layer, sec_choice, seg_choice
c = c + 1
syn = h.Exp2Syn(sec_choice(seg_choice))
if self.synvars['type'] == "E2_Prob":
syn = h.E2_Prob(sec_choice(seg_choice))
syn.P = self.synvars['P']
if self.synvars['type'] == "E2_STP_Prob":
syn = h.E2_STP_Prob(sec_choice(seg_choice))
if self.synvars['type'] == "STDPE2":
syn = h.STDPE2(sec_choice(seg_choice))
if self.synvars['type'] == "STDPE2_Clo":
syn = h.STDPE2_Clo(sec_choice(seg_choice))
if self.synvars['type'] == "STDPE2_STP":
syn = h.STDPE2_STP(sec_choice(seg_choice))
if self.synvars['type'] == "STDPE2_Prob":
syn = h.STDPE2_Prob(sec_choice(seg_choice))
syn.P = self.synvars['P']
#initializes different variables depending on synapse
if (self.synvars['type'] == "STDPE2_STP") | (
self.synvars['type'] == "E2_STP_Prob"):
syn.F1 = self.synvars['F1']
if (self.synvars['type'] == "STDPE2_Clo") | (
self.synvars['type'] == "STDPE2_STP") | (
self.synvars['type'] == "STDPE2") | (
self.synvars['type'] == "STDPE2_Prob"):
syn.wmax = self.synvars['wmax']
syn.wmin = self.synvars['wmin']
syn.thresh = self.synvars['thresh']
if (self.synvars['type'] == "E2_Prob") | (
self.synvars['type'] == "E2_STP_Prob") | (
self.synvars['type'] == "STDPE2_STP") | (
self.synvars['type'] == "STDPE2_Prob"):
h.use_mcell_ran4(1)
syn.seed = self.ranGen.randint(1, 4.295e9)
syn.tau1 = 0.5
syn.tau2 = 0.6
syn.e = 0
if nmda_flag == 1:
if synType == 'NMDA':
synList[layer].append(nmda)
else:
synList[layer].append(syn)
else:
synList[layer].append(syn)
s = s + c
print "total_synapses = ", s
def addSynapses(self):
self.pre_list = []
# E0
syn_ = h.MyExp2Syn(self.lm_thick1(0.5))
self.pre_list.append(syn_) # AMPA EC
syn_.tau1 = 0.5
syn_.tau2 = 3
syn_.e = 0
# E1
syn_ = h.MyExp2Syn(self.lm_thick2(0.5))
self.pre_list.append(syn_) # AMPA EC
syn_.tau1 = 0.5
syn_.tau2 = 3
syn_.e = 0
# E2
syn_ = h.MyExp2Syn(self.radTmed(0.5))
self.pre_list.append(syn_) # AMPA CA3 Shaffer collateral
syn_.tau1 = 0.5
syn_.tau2 = 3
syn_.e = 0
# E3
syn_ = h.NMDA(self.radTmed(0.5))
self.pre_list.append(syn_) # NMDA CA3 Shaffer collateral
syn_.tcon = 2.3
syn_.tcoff = 100
syn_.gNMDAmax = 1 # use connection weight to determine max cond
# E4
syn_ = h.MyExp2Syn(self.radTprox(0.5))
self.pre_list.append(syn_) # AMPA PC Recurrent collateral
syn_.tau1 = 0.5
syn_.tau2 = 3
syn_.e = 0
# I5
syn_ = h.MyExp2Syn(self.soma(0.5))
self.pre_list.append(syn_) # GABA-A basket cell
syn_.tau1 = 1
syn_.tau2 = 8
syn_.e = -75
# I6
syn_ = h.MyExp2Syn(self.axon(0.1))
self.pre_list.append(syn_) # GABA-A AA cell
syn_.tau1 = 1
syn_.tau2 = 8
syn_.e = -75
# I7
syn_ = h.MyExp2Syn(self.lm_thick1(0.5))
self.pre_list.append(syn_) # GABA-A OLM cell
syn_.tau1 = 1
syn_.tau2 = 8
syn_.e = -75
# I8
syn_ = h.MyExp2Syn(self.lm_thick2(0.5))
self.pre_list.append(syn_) # GABA-A OLM cell
syn_.tau1 = 1
syn_.tau2 = 8
syn_.e = -75
# I9
syn_ = h.MyExp2Syn(self.lm_thick1(0.5))
self.pre_list.append(syn_) # GABA-B OLM cell
syn_.tau1 = 35
syn_.tau2 = 100
syn_.e = -75
# I10
syn_ = h.MyExp2Syn(self.lm_thick2(0.5))
self.pre_list.append(syn_) # GABA-B OLM Cell
syn_.tau1 = 35
syn_.tau2 = 100
syn_.e = -75
# I11
syn_ = h.MyExp2Syn(self.radTmed(0.8))
self.pre_list.append(syn_) # GABA-A Bistratified
syn_.tau1 = 1
syn_.tau2 = 8
syn_.e = -75
# I12
syn_ = h.MyExp2Syn(self.radTmed(0.7))
self.pre_list.append(syn_) # GABA-A Bistratified
syn_.tau1 = 1
syn_.tau2 = 8
syn_.e = -75
# I13
syn_ = h.MyExp2Syn(self.radTmed(0.6))
self.pre_list.append(syn_) # GABA-A Bistratified
syn_.tau1 = 1
syn_.tau2 = 8
syn_.e = -75
# I14
syn_ = h.MyExp2Syn(self.radTmed(0.4))
self.pre_list.append(syn_) # GABA-A Bistratified
syn_.tau1 = 1
syn_.tau2 = 8
syn_.e = -75
# I15
syn_ = h.MyExp2Syn(self.radTmed(0.3))
self.pre_list.append(syn_) # GABA-A Bistratified
syn_.tau1 = 1
syn_.tau2 = 8
syn_.e = -75
# I16
syn_ = h.MyExp2Syn(self.radTmed(0.2))
self.pre_list.append(syn_) # GABA-A Bistratified
syn_.tau1 = 1
syn_.tau2 = 8
syn_.e = -75
# I17
syn_ = h.MyExp2Syn(self.radTmed(0.8))
self.pre_list.append(syn_) # GABA-B Bistratified
syn_.tau1 = 35
syn_.tau2 = 100
syn_.e = -75
# I18
syn_ = h.MyExp2Syn(self.radTmed(0.7))
self.pre_list.append(syn_) # GABA-B Bistratified
syn_.tau1 = 35
syn_.tau2 = 100
syn_.e = -75
# I19
syn_ = h.MyExp2Syn(self.radTmed(0.6))
self.pre_list.append(syn_) # GABA-B Bistratified
syn_.tau1 = 35
syn_.tau2 = 100
syn_.e = -75
# I20
syn_ = h.MyExp2Syn(self.radTmed(0.4))
self.pre_list.append(syn_) # GABA-B Bistratified
syn_.tau1 = 35
syn_.tau2 = 100
syn_.e = -75
# I21
syn_ = h.MyExp2Syn(self.radTmed(0.3))
self.pre_list.append(syn_) # GABA-B Bistratified
syn_.tau1 = 35
syn_.tau2 = 100
syn_.e = -75
# I22
syn_ = h.MyExp2Syn(self.radTmed(0.2))
self.pre_list.append(syn_) # GABA-B Bistratified
syn_.tau1 = 35
syn_.tau2 = 100
syn_.e = -75
# I23
syn_ = h.STDPE2(self.radTmed(0.5))
self.pre_list.append(syn_) # AMPA modifiable CA3 Schaffer collaterals
syn_.tau1 = 0.5
syn_.tau2 = 3
syn_.e = 0
def synapses(self):
syn_ = h.MyExp2Syn(self.lm_thick1(0.5))
syn_.tau1 = 0.5
syn_.tau2 = 3.0
syn_.e = 0.0
self.prelist.append(syn_)
syn_ = h.MyExp2Syn(self.lm_thick2(0.5))
syn_.tau1 = 0.5
syn_.tau2 = 3.0
syn_.e = 0.0
self.prelist.append(syn_)
syn_ = h.MyExp2Syn(self.radTmed(0.5))
syn_.tau1 = 0.5
syn_.tau2 = 3.0
syn_.e = 0.0
self.prelist.append(syn_)
syn_ = h.NMDA(self.radTmed(0.5))
syn_.tcon = 2.3
syn_.tcoff = 100.
syn_.gNMDAmax = 1.0
self.prelist.append(syn_)
syn_ = h.MyExp2Syn(self.radTprox(0.5))
syn_.tau1 = 0.5
syn_.tau2 = 3.0
syn_.e = 0.0
self.prelist.append(syn_)
syn_ = h.MyExp2Syn(self.soma(0.5))
syn_.tau1 = 1.0
syn_.tau2 = 8.0
syn_.e = -75
self.prelist.append(syn_)
syn_ = h.MyExp2Syn(self.axon(0.5))
syn_.tau1 = 1.0
syn_.tau2 = 8.0
syn_.e = -75.0
self.prelist.append(syn_)
syn_ = h.MyExp2Syn(self.lm_thick1(0.5))
syn_.tau1 = 1.0
syn_.tau2 = 8.0
syn_.e = -75
self.prelist.append(syn_)
syn_ = h.MyExp2Syn(self.lm_thick2(0.5))
syn_.tau1 = 1.0
syn_.tau2 = 8.0
syn_.e = -75
self.prelist.append(syn_)
syn_ = h.MyExp2Syn(self.lm_thick1(0.5))
syn_.tau1 = 35
syn_.tau2 = 100
syn_.e = -75
self.prelist.append(syn_)
syn_ = h.MyExp2Syn(self.lm_thick2(0.5))
syn_.tau1 = 35
syn_.tau2 = 100
syn_.e = -75
self.prelist.append(syn_)
for l in np.arange(0.2, 0.9, 0.1):
if l == 0.5:
continue
syn_ = h.MyExp2Syn(self.radTmed(l))
syn_.tau1 = 1.0
syn_.tau2 = 8.0
syn_.e = -75
self.prelist.append(syn_)
for l in np.arange(0.2, 0.9, 0.1):
if l == 0.5:
continue
syn_ = h.MyExp2Syn(self.radTmed(l))
syn_.tau1 = 35
syn_.tau2 = 100
syn_.e = -75
self.prelist.append(syn_)
syn_ = h.STDPE2(self.radTmed(0.5))
syn_.tau1 = 0.5
syn_.tau2 = 3.0
syn_.e = 0.0
