def run_model(path, params):
soma_params = {
"V0": 0.0,
"C": 3.0,
"Iextmean": -1.0,
"Iextvarience": 0.5,
"ENa": 120.0,
"EK": -15.0,
"El": 0.0,
"ECa": 140.0,
"CCa": 0.05,
"sfica": 0.13,
"sbetaca": 0.075,
"gbarNa": 30.0,
"gbarK_DR": 17.0,
"gbarK_AHP": 0.8,
"gbarK_C ": 15.0,
"gl": 0.1,
"gbarCa": 6.0,
}
dendrite_params = {
"V0": 0.0,
"C": 3.0,
"Iextmean": -1.0,
"Iextvarience": 0.5,
"ENa": 120.0,
"EK": -15.0,
"El": 0.0,
"ECa": 140.0,
"CCa": 0.05,
"sfica": 0.13,
"sbetaca": 0.075,
"gbarNa": 0.0,
"gbarK_DR": 0.0,
"gbarK_AHP": 0.8,
"gbarK_C ": 5.0,
"gl": 0.1,
"gbarCa": 5.0,
}
connection_params = {
"compartment1": "soma",
"compartment2": "dendrite",
"p": 0.5,
"g": 1.5,
}
basket_fs_neuron = {
"V0": -65.0,
"Iextmean": -0.5,
"Iextvarience": 0.5,
"ENa": 50.0,
"EK": -90.0,
"El": -65.0,
"gbarNa": 55.0,
"gbarK": 8.0,
"gl": 0.1,
"fi": 10,
}
olm_params = {
"V0": -70,
"gl": 0.05,
"El": -70,
"gbarNa": 10.7,
"ENa": 90,
"gbarK": 31.9,
"EK": -100,
"gbarKa": 16.5,
"gbarH": 0.05,
"EH": -32.9,
"Iextmean": 0.0,
"Iextvarience": 0.5,
}
CosSpikeGeneratorParams = {
"freq": 5.0, # frequency in Hz
"phase": 0.0, # phase in rad
"latency": 10.0, # in ms
"probability": 0.01,
"threshold": 0.3,
}
PoisonSpikeGenerator = {
"latency": 10.0, # in ms
"probability": 0.00001,
}
simple_ext_synapse_params = {
"Erev": 60.0,
"gbarS": 0.005,
"tau": 0.5, # 0.2, # 1.3–2
"w": 20.0,
"delay": 0,
}
simple_inh_synapse_params = {
"Erev": -15.0,
"gbarS": 0.005,
"tau": 0.2, # 4.2-7.2 ms
"w": 100.0,
"delay": 0,
}
ext_synapse_params = {
"Erev": 60.0,
"gbarS": 0.005,
"alpha_s": 1.1,
"beta_s": 0.19,
"K": 5.0,
"teta": 2.0,
"w": 10.0,
"delay": 0,
}
inh_synapse_params = {
"Erev": -15.0,
"gbarS": 0.005,
"alpha_s": 14.0,
"beta_s": 0.07,
"K": 2.0,
"teta": 0.0,
"w": 10.0,
"delay": 0,
}
neurons = []
synapses = []
Np = 400 # number of pyramide neurons
Nb = 50 # number of basket cells
Nolm = 50 # 50 # number of olm cells
Npv1 = params["n_pv1"] # number of septum spike generators
Npv2 = params["n_pv2"]
Nec = params["n_ec"] # number of EC inputs
Ncs = params["n_cs"] # 100 # number of Shaffer collateral iunput
Ns = 600 # number synapses between pyramide cells
Nbasket2pyr = 10 # 100 4 # number synapses from basket interneuron to one pyramide neuron
Nolm2pyr = 10 # 4 # number synapses from basket interneuron to one pyramide neuron
Nspyr2basket = 10 # number synapses from one pyramide to interneurons
Nspyr2OLM = 20
# number synapses from septal generators to hippocampal interneurons
Nspv12bas = params["Nspv12bas"]
Nspv22olm = params["Nspv22olm"]
NsEc2pyr = params["NsEc2pyr"]
NsCs2pyr = params["NsCs2pyr"]
NsCs2bas = params["NsCs2bas"]
indexes = {
"pyr": [],
"bas": [],
"olm": [],
"pv1": [],
"pv2": [],
"mec": [],
"cs": [],
}
for idx in range(Np):
soma = {"soma": soma_params.copy()}
soma["soma"]["V0"] = 0.5 * np.random.randn()
soma["soma"]["Iextmean"] += 0.5 * np.random.randn()
dendrite = {"dendrite": dendrite_params.copy()}
dendrite["dendrite"]["V0"] = soma["soma"]["V0"]
dendrite["dendrite"]["Iextmean"] += 0.5 * np.random.randn()
connection = connection_params.copy()
neuron = {
"type": "pyramide",
"compartments": [soma, dendrite],
"connections": [connection]
}
indexes["pyr"].append(len(neurons))
neurons.append(neuron)
for idx in range(Nb):
neuron = {"type": "FS_Neuron", "compartments": basket_fs_neuron.copy()}
neuron["compartments"]["V0"] += 10 * np.random.rand()
neuron["compartments"]["Iextmean"] += 0.5 * np.random.randn()
indexes["bas"].append(len(neurons))
neurons.append(neuron)
for idx in range(Nolm):
neuron = {"type": "olm_cell", "compartments": olm_params.copy()}
neuron["compartments"]["V0"] += 10 * np.random.rand()
neuron["compartments"]["Iextmean"] += 0.5 * np.random.randn()
indexes["olm"].append(len(neurons))
neurons.append(neuron)
for idx in range(Npv1):
if (params["pv1_type"] == "rhythm"):
neuron = {
"type": "CosSpikeGenerator",
"compartments": CosSpikeGeneratorParams.copy()
}
# neuron["compartments"]["threshold"] = 0.6
neuron["compartments"]["phase"] = params[
"ms_pv1_pv2_phase_shift"] + params["ms_cs_pp_phase_shift"]
elif (params["pv1_type"] == "random"):
neuron = {
"type": "PoisonSpikeGenerator",
"compartments": PoisonSpikeGenerator.copy()
}
else:
raise ValueError("undefined type of ms pv1 neuron")
indexes["pv1"].append(len(neurons))
neurons.append(neuron)
for idx in range(Npv2):
if (params["pv2_type"] == "rhythm"):
neuron = {
"type": "CosSpikeGenerator",
"compartments": CosSpikeGeneratorParams.copy()
}
neuron["compartments"]["phase"] = params["ms_cs_pp_phase_shift"]
elif (params["pv2_type"] == "random"):
neuron = {
"type": "PoisonSpikeGenerator",
"compartments": PoisonSpikeGenerator.copy()
}
else:
raise ValueError("undefined type of ms pv2 neuron")
indexes["pv2"].append(len(neurons))
neurons.append(neuron)
for idx in range(Nec):
if (params["pp_type"] == "rhythm"):
neuron = {
"type": "CosSpikeGenerator",
"compartments": CosSpikeGeneratorParams.copy()
}
neuron["compartments"]["threshold"] = 0.3
neuron["compartments"]["phase"] = -2.79 # !!!!!!!!
elif (params["pp_type"] == "random"):
neuron = {
"type": "PoisonSpikeGenerator",
"compartments": PoisonSpikeGenerator.copy()
}
else:
raise ValueError("undefined type of ec neuron")
indexes["mec"].append(len(neurons))
neurons.append(neuron)
for idx in range(Ncs):
if (params["cs_type"] == "rhythm"):
neuron = {
"type": "CosSpikeGenerator",
"compartments": CosSpikeGeneratorParams.copy()
}
neuron["compartments"]["threshold"] = 0.3
neuron["compartments"]["phase"] = 0 # !!!!!!!!
elif (params["cs_type"] == "random"):
neuron = {
"type": "PoisonSpikeGenerator",
"compartments": PoisonSpikeGenerator.copy()
}
else:
raise ValueError("undefined type of ec neuron")
indexes["cs"].append(len(neurons))
neurons.append(neuron)
for idx in range(Ns):
#synapse beteween pyramides
pre_ind = np.random.choice(indexes["pyr"])
post_ind = np.random.choice(indexes["pyr"])
if (pre_ind == post_ind):
post_ind = np.random.randint(0, Np)
synapse = {
"type": "ComplexSynapse", # "SimpleSynapseWithDelay",
"pre_ind": pre_ind,
"post_ind": post_ind,
"pre_compartment_name": "soma",
"post_compartment_name": "soma",
"params": ext_synapse_params.copy(),
}
synapse["params"]["delay"] = np.random.randint(20, 50)
synapses.append(synapse)
for _ in range(Nbasket2pyr):
for idx in indexes["pyr"]:
# synapses from basket cells to pyramides
pre_ind = np.random.choice(indexes["bas"])
synapse = {
"type": "ComplexSynapse", # "SimpleSynapseWithDelay",
"pre_ind": pre_ind,
"post_ind": idx,
"pre_compartment_name": "soma",
"post_compartment_name": "soma",
"params": inh_synapse_params.copy(),
}
synapse["params"]["delay"] = np.random.randint(20, 50)
synapses.append(synapse)
for _ in range(Nolm2pyr):
for idx in indexes["pyr"]:
# synapses from OLM cells to pyramides
pre_ind = np.random.choice(indexes["olm"])
synapse = {
"type": "ComplexSynapse", # "SimpleSynapseWithDelay",
"pre_ind": pre_ind,
"post_ind": idx,
"pre_compartment_name": "soma",
"post_compartment_name": "dendrite",
"params": inh_synapse_params.copy(),
}
synapse["params"]["delay"] = np.random.randint(20, 50)
synapses.append(synapse)
for _ in range(Nspyr2OLM):
for idx in indexes["pyr"]:
pre_ind = idx
# set synapse from pyramide to OLM neuron
post_ind = np.random.choice(indexes["olm"])
synapse = {
"type": "ComplexSynapse", # "SimpleSynapseWithDelay",
"pre_ind": pre_ind,
"post_ind": post_ind,
"pre_compartment_name": "soma",
"post_compartment_name": "soma",
"params": ext_synapse_params.copy(),
}
synapse["params"]["delay"] = np.random.randint(20, 50)
synapses.append(synapse)
for _ in range(Nspyr2basket):
for idx in indexes["pyr"]:
# set synapse from pyramide to basket neuron
pre_ind = idx
post_ind = np.random.choice(indexes["bas"])
synapse = {
"type": "ComplexSynapse", # "SimpleSynapseWithDelay",
"pre_ind": pre_ind,
"post_ind": post_ind,
"pre_compartment_name": "soma",
"post_compartment_name": "soma",
"params": ext_synapse_params.copy(),
}
synapse["params"]["delay"] = np.random.randint(20, 50)
synapses.append(synapse)
for idx in range(Nspv12bas):
# set synapses from pv1 to basket
pre_ind = np.random.choice(indexes["pv1"])
post_ind = np.random.choice(indexes["bas"])
synapse = {
"type": "SimpleSynapseWithDelay",
"pre_ind": pre_ind,
"post_ind": post_ind,
"pre_compartment_name": "soma",
"post_compartment_name": "soma",
"params": simple_inh_synapse_params.copy(),
}
# synapse["params"]["Erev"] = -75
# synapse["params"]["w"] = 100
synapse["params"]["delay"] = np.random.randint(20, 50)
synapses.append(synapse)
for idx in range(Nspv22olm):
# set synapses from pv1 to olm
pre_ind = np.random.choice(indexes["pv2"])
post_ind = np.random.choice(indexes["olm"])
synapse = {
"type": "SimpleSynapseWithDelay",
"pre_ind": pre_ind,
"post_ind": post_ind,
"pre_compartment_name": "soma",
"post_compartment_name": "soma",
"params": simple_inh_synapse_params.copy()
}
# synapse["params"]["Erev"] = -75
# synapse["params"]["w"] = 100
synapse["params"]["delay"] = np.random.randint(20, 50)
synapses.append(synapse)
for idx in range(NsCs2pyr):
# synapses from shaffer colletarel to pyramodes
pre_ind = np.random.choice(indexes["cs"])
post_ind = np.random.choice(indexes["pyr"])
synapse = {
"type": "SimpleSynapseWithDelay",
"pre_ind": pre_ind,
"post_ind": post_ind,
"pre_compartment_name": "soma",
"post_compartment_name": "soma",
"params": simple_ext_synapse_params.copy()
}
# synapse["params"]["w"] = 20
synapse["params"]["delay"] = np.random.randint(20, 50)
synapses.append(synapse)
for idx in range(NsCs2bas):
# synapses from shaffer colletarel to basket
pre_ind = np.random.choice(indexes["cs"])
post_ind = np.random.choice(indexes["bas"])
synapse = {
"type": "SimpleSynapseWithDelay",
"pre_ind": pre_ind,
"post_ind": post_ind,
"pre_compartment_name": "soma",
"post_compartment_name": "soma",
"params": simple_ext_synapse_params.copy()
}
#synapse["params"]["w"] = 100
synapse["params"]["delay"] = np.random.randint(20, 50)
synapses.append(synapse)
for idx in range(NsEc2pyr):
# synapses from perforant pathway to pyramodes
pre_ind = np.random.choice(indexes["mec"])
post_ind = np.random.choice(indexes["pyr"])
synapse = {
"type": "SimpleSynapseWithDelay",
"pre_ind": pre_ind,
"post_ind": post_ind,
"pre_compartment_name": "soma",
"post_compartment_name": "dendrite",
"params": simple_ext_synapse_params.copy()
}
#synapse["params"]["w"] = 100
synapse["params"]["delay"] = np.random.randint(20, 50)
synapses.append(synapse)
"""
for syn in synapses:
print (neurons[syn["pre_ind"]]["type"] + " -> " + neurons[syn["post_ind"]]["type"])
print ( str(syn["pre_ind"]) + " -> " + str(syn["post_ind"]) )
return
"""
dt = 0.1
duration = 500
net = lib.Network(neurons, synapses)
for _ in range(4):
net.integrate(dt, duration)
net.save_results(path + "_all_results")
print("Что-то посчиталось!!!")
return indexes
def run_model(path, experiment):
cluster_pacemaker = {
"V0": -50.0,
"Iextmean": 1.2,
"Iextvarience": 0.5,
"ENa": 50.0,
"EK": -90.0,
"El": -50.0,
"Eh": -40.0,
"gbarNa": 55.0,
"gbarK": 8.0,
"gbarKS": 12.0,
"gbarH": 1.0,
"gl": 0.1,
"fi": 5,
}
fs_neuron = {
"V0": -65.0,
"Iextmean": 0.5,
"Iextvarience": 0.5,
"ENa": 50.0,
"EK": -90.0,
"El": -65.0,
"gbarNa": 55.0,
"gbarK": 8.0,
"gl": 0.1,
"fi": 10,
}
ext_synapse_params = {
"Erev" : 60.0,
"gbarS": 0.005,
"alpha_s" : 1.1,
"beta_s": 0.19,
"K": 5.0,
"teta": 2.0,
"w" : 1.0,
"delay" : 0,
}
inh_synapse_params = {
"Erev" : -15.0,
"gbarS": 0.005,
"alpha_s" : 14.0,
"beta_s": 0.07,
"K": 2.0,
"teta": 0.0,
"w" : 1.0,
"delay" : 0,
}
soma_params = {
"V0": 0.0,
"C" : 3.0,
"Iextmean": -0.5,
"Iextvarience": 0.2,
"ENa": 120.0,
"EK": -15.0,
"El": 0.0,
"ECa": 140.0,
"CCa": 0.05,
"sfica": 0.13,
"sbetaca": 0.075,
"gbarNa": 30.0,
"gbarK_DR": 17.0,
"gbarK_AHP": 0.8,
"gbarK_C " : 15.0,
"gl": 0.1,
"gbarCa": 6.0,
}
dendrite_params = {
"V0": 0.0,
"C" : 3.0,
"Iextmean": -0.5,
"Iextvarience": 0.2,
"ENa": 120.0,
"EK": -15.0,
"El": 0.0,
"ECa": 140.0,
"CCa": 0.05,
"sfica": 0.13,
"sbetaca": 0.075,
"gbarNa": 0.0,
"gbarK_DR": 0.0,
"gbarK_AHP": 0.8,
"gbarK_C " : 5.0,
"gl": 0.1,
"gbarCa": 5.0,
}
connection_params = {
"compartment1": "soma",
"compartment2": "dendrite",
"p": 0.5,
"g": 1.5,
}
basket_fs_neuron = {
"V0": -65.0,
"Iextmean": 0.2,
"Iextvarience": 0.2,
"ENa": 50.0,
"EK": -90.0,
"El": -65.0,
"gbarNa": 55.0,
"gbarK": 8.0,
"gl": 0.1,
"fi": 10,
}
olm_params = {
"V0" : -70,
"gl" : 0.05,
"El" : -70,
"gbarNa" : 10.7,
"ENa" : 90,
"gbarK" : 31.9,
"EK" : -100,
"gbarKa" : 16.5,
"gbarH" : 0.05,
"EH" : -32.9,
"Iextmean" : 0.2,
"Iextvarience": 0.2,
}
########## set model of septum ################
# number of neurons
Nglu = 40
NgabaCR = 40
NgabaPV1 = 40
NgabaPV2 = 40
NgabaPacPV1 = 10
NgabaPacPV2 = 10
NNseptum = Nglu + NgabaCR + NgabaPV1 + NgabaPV2 + NgabaPacPV1 + NgabaPacPV2
Sparsness = 0.5 # sparsness in septal network
neurons = []
for idx in range(Nglu):
neuron = {
"type" : "FS_Neuron",
"compartments" : fs_neuron.copy()
}
neuron["compartments"]["V0"] += 10 * np.random.rand()
neuron["compartments"]["Iextmean"] = 0.5 # 0.5*np.random.randn()
neurons.append(neuron)
for idx in range(NgabaCR):
neuron = {
"type" : "FS_Neuron",
"compartments" : fs_neuron.copy()
}
neuron["compartments"]["V0"] += 10 * np.random.rand()
neuron["compartments"]["Iextmean"] = 0 # 0.1*np.random.randn()
neurons.append(neuron)
for idx in range(NgabaPV1):
neuron = {
"type" : "FS_Neuron",
"compartments" : fs_neuron.copy()
}
neuron["compartments"]["V0"] += 10 * np.random.rand()
neuron["compartments"]["Iextmean"] = 0.1 #0.5*np.random.randn()
neurons.append(neuron)
for idx in range(NgabaPacPV1):
neuron = {
"type" : "ClusterNeuron",
"compartments" : cluster_pacemaker.copy()
}
neuron["compartments"]["V0"] += 10 * np.random.rand()
neuron["compartments"]["Iextmean"] = 0.1 # 0.5*np.random.randn()
neurons.append(neuron)
for idx in range(NgabaPV2):
neuron = {
"type" : "FS_Neuron",
"compartments" : fs_neuron.copy()
}
neuron["compartments"]["V0"] += 10 * np.random.rand()
neuron["compartments"]["Iextmean"] = 0.7 #0.5*np.random.randn()
neurons.append(neuron)
for idx in range(NgabaPacPV2):
neuron = {
"type" : "ClusterNeuron",
"compartments" : cluster_pacemaker.copy()
}
neuron["compartments"]["V0"] += 10 * np.random.rand()
neuron["compartments"]["Iextmean"] = 0.7 #0.5*np.random.randn()
neurons.append(neuron)
synapses = []
# synapse from Glu to Glu neurons
for idx1 in range(Nglu):
for idx2 in range(Nglu):
if (Sparsness < np.random.rand() or idx1 == idx2):
continue
pre_ind = idx1
post_ind = idx2
synapse = {
"type" : "ComplexSynapse",
"pre_ind": pre_ind,
"post_ind": post_ind,
"pre_compartment_name": "soma",
"post_compartment_name" : "soma",
"params": ext_synapse_params.copy(),
}
synapse["params"]["w"] = 1
synapses.append(synapse)
# synapses from Glu naurons to GABA(CR) neurons
for idx1 in range(Nglu):
for idx2 in range(Nglu, Nglu+NgabaCR):
if (Sparsness < np.random.rand() or idx1 == idx2):
continue
pre_ind = idx1
post_ind = idx2
synapse = {
"type" : "ComplexSynapse",
"pre_ind": pre_ind,
"post_ind": post_ind,
"pre_compartment_name": "soma",
"post_compartment_name" : "soma",
"params": ext_synapse_params.copy(),
}
synapse["params"]["w"] = 0.2
synapses.append(synapse)
# synapses from GABA(CR) neurons to Glu naurons
for idx1 in range(Nglu, Nglu+NgabaCR):
for idx2 in range(Nglu):
if (idx1 == idx2):
continue
pre_ind = idx1
post_ind = idx2
synapse = {
"type" : "ComplexSynapse",
"pre_ind": pre_ind,
"post_ind": post_ind,
"pre_compartment_name": "soma",
"post_compartment_name" : "soma",
"params": inh_synapse_params.copy(),
}
synapse["params"]["w"] = 1.5
synapses.append(synapse)
# synapses from Glu neurons to PV1 and pac-PV1 naurons
for idx1 in range(0, Nglu):
for idx2 in range(Nglu+NgabaCR, Nglu+NgabaCR+NgabaPV1+NgabaPacPV1):
if (Sparsness < np.random.rand() or idx1 == idx2):
continue
pre_ind = idx1
post_ind = idx2
synapse = {
"type" : "ComplexSynapse",
"pre_ind": pre_ind,
"post_ind": post_ind,
"pre_compartment_name": "soma",
"post_compartment_name" : "soma",
"params": ext_synapse_params.copy(),
}
synapse["params"]["w"] = 1
synapses.append(synapse)
# synapses from PV1 and pac-PV1 naurons to PV2 and pac-PV2
for idx1 in range(Nglu+NgabaCR, Nglu+NgabaCR+NgabaPV1+NgabaPacPV1):
for idx2 in range(Nglu+NgabaCR+NgabaPV1+NgabaPacPV1, Nglu+NgabaCR+NgabaPV1+NgabaPacPV1+NgabaPV2+NgabaPacPV2):
if (Sparsness < np.random.rand() or idx1 == idx2):
continue
pre_ind = idx1
post_ind = idx2
synapse = {
"type" : "ComplexSynapse",
"pre_ind": pre_ind,
"post_ind": post_ind,
"pre_compartment_name": "soma",
"post_compartment_name" : "soma",
"params": inh_synapse_params.copy(),
}
synapse["params"]["w"] = 0.5
synapses.append(synapse)
# synapses from PV2 and pac-PV2 naurons to PV1 and pac-PV1
for pre_ind in range(Nglu+NgabaCR+NgabaPV1+NgabaPacPV1, Nglu+NgabaCR+NgabaPV1+NgabaPacPV1+NgabaPV2+NgabaPacPV2):
for post_ind in range(Nglu+NgabaCR, Nglu+NgabaCR+NgabaPV1+NgabaPacPV1):
if (Sparsness < np.random.rand() or idx1 == idx2):
continue
synapse = {
"type" : "ComplexSynapse",
"pre_ind": pre_ind,
"post_ind": post_ind,
"pre_compartment_name": "soma",
"post_compartment_name" : "soma",
"params": inh_synapse_params.copy(),
}
synapse["params"]["w"] = 0.1
synapses.append(synapse)
for s in synapses:
s["params"]["w"] *= 2
###### set model of hippocampus ####################
# number of neurons
Np = 400 # number of pyramide neurons
Nb = 50 # number of basket cells
Nolm = 50 # number of olm cells
# Number synapses
Ns = 400 # number synapses between pyramide cells
Nint2pyr = 50 # 4 # number synapses from one interneuron to one pyramide neuron
# NSG = 20 # number of spike generators
Ns2in = 200 # number synapses from septal generators to hippocampal interneurons
NsOLM2PV = 400 # number synapses from OLM cell to MSDB PV1 cells
for idx in range(Np):
soma = {"soma" : soma_params.copy()}
soma["soma"]["V0"] = 0.5 * np.random.randn()
soma["soma"]["Iextmean"] += 0.5*np.random.randn()
dendrite = {"dendrite" : dendrite_params.copy()}
dendrite["dendrite"]["V0"] = soma["soma"]["V0"]
dendrite["dendrite"]["Iextmean"] += 0.5*np.random.randn()
connection = connection_params.copy()
neuron = {
"type" : "pyramide",
"compartments" : [soma, dendrite],
"connections" : [connection]
}
neurons.append(neuron)
for idx in range(Nb):
neuron = {
"type" : "FS_Neuron",
"compartments" : basket_fs_neuron.copy()
}
neuron["compartments"]["V0"] += 10 * np.random.rand()
neuron["compartments"]["Iextmean"] += 0.5*np.random.randn()
neurons.append(neuron)
for idx in range(Nolm):
neuron = {
"type" : "olm_cell",
"compartments" : olm_params.copy()
}
neuron["compartments"]["V0"] += 10 * np.random.rand()
neuron["compartments"]["Iextmean"] += 0.5*np.random.randn()
neurons.append(neuron)
# set synapses intro hippocampal model
for idx in range(Ns):
pre_ind = np.random.randint(0, Np) + NNseptum
post_ind = np.random.randint(0, Np) + NNseptum
if (pre_ind == post_ind):
post_ind = np.random.randint(0, Np) + NNseptum
synapse = {
"type" : "ComplexSynapse",
"pre_ind": pre_ind,
"post_ind": post_ind,
"pre_compartment_name": "soma",
"post_compartment_name" : "soma",
"params" : ext_synapse_params.copy(),
}
synapse["params"]["delay"] = np.random.randint(20, 50)
synapses.append(synapse)
for _ in range(Nint2pyr):
for idx in range(Np):
pre_ind = np.random.randint(Np, Np + Nb) + NNseptum
synapse = {
"type" : "ComplexSynapse",
"pre_ind": pre_ind,
"post_ind": idx + NNseptum,
"pre_compartment_name": "soma",
"post_compartment_name" : "soma",
"params": inh_synapse_params.copy(),
}
synapse["params"]["delay"] = np.random.randint(20, 50)
synapses.append(synapse)
for _ in range(Nint2pyr):
for idx in range(Np):
pre_ind = np.random.randint(Np + Nb, Np + Nb + Nolm) + NNseptum
synapse = {
"type" : "ComplexSynapse",
"pre_ind": pre_ind,
"post_ind": idx + NNseptum,
"pre_compartment_name": "soma",
"post_compartment_name" : "dendrite",
"params": inh_synapse_params.copy(),
}
synapse["params"]["delay"] = np.random.randint(20, 50)
synapses.append(synapse)
for idx in range(Np):
pre_ind = idx + NNseptum
# set synapse from pyramide to OLM neuron
post_ind = np.random.randint(Np + Nb, Np + Nb + Nolm) + NNseptum
synapse = {
"type" : "ComplexSynapse",
"pre_ind": pre_ind,
"post_ind": post_ind,
"pre_compartment_name": "soma",
"post_compartment_name" : "soma",
"params": ext_synapse_params.copy(),
}
synapse["params"]["delay"] = np.random.randint(20, 50)
synapses.append(synapse)
# set synapse from pyramide to basket neuron
post_ind = np.random.randint(Np, Np + Nb) + NNseptum
synapse = {
"type" : "ComplexSynapse",
"pre_ind": pre_ind,
"post_ind": post_ind,
"pre_compartment_name": "soma",
"post_compartment_name" : "soma",
"params": ext_synapse_params.copy(),
}
synapse["params"]["delay"] = np.random.randint(20, 50)
synapses.append(synapse)
# set synapses from MSDB PV-neurons to hippocampal interneurons
# from PV1-neurons to basketcells
for idx in range(Ns2in):
pre_ind = np.random.randint(Nglu+NgabaCR, Nglu+NgabaCR+NgabaPV1+NgabaPacPV1)
post_ind = np.random.randint(Np, Np + Nb) + NNseptum
synapse = {
"type" : "ComplexSynapse",
"pre_ind": pre_ind,
"post_ind": post_ind,
"pre_compartment_name": "soma",
"post_compartment_name" : "soma",
"params": inh_synapse_params.copy(),
}
# synapse["params"]["Erev"] = -75
synapse["params"]["w"] = 50
synapse["params"]["delay"] = np.random.randint(20, 50)
synapses.append(synapse)
# from PV2-neurons to OLM neurons
for idx in range(Ns2in):
pre_ind = np.random.randint(Nglu+NgabaCR+NgabaPV1+NgabaPacPV1, Nglu+NgabaCR+NgabaPV1+NgabaPacPV1+NgabaPV2+NgabaPacPV2)
post_ind = np.random.randint(Np + Nb, Np + Nb + Nolm) + NNseptum
synapse = {
"type" : "ComplexSynapse",
"pre_ind": pre_ind,
"post_ind": post_ind,
"pre_compartment_name": "soma",
"post_compartment_name" : "soma",
"params": inh_synapse_params.copy()
}
# synapse["params"]["Erev"] = -75
synapse["params"]["w"] = 50
synapse["params"]["delay"] = np.random.randint(20, 50)
synapses.append(synapse)
# set hippocampal synapses to MSBD PV-neurons
# from OLM cells to PV1-neurons
if (experiment == "OLM2PV1" or experiment == "OLM2PV1andPV2"):
for idx in range(NsOLM2PV):
post_ind = np.random.randint(Nglu+NgabaCR, Nglu+NgabaCR+NgabaPV1+NgabaPacPV1)
pre_ind = np.random.randint(Np + Nb, Np + Nb + Nolm) + NNseptum
synapse = {
"type" : "ComplexSynapse",
"pre_ind": pre_ind,
"post_ind": post_ind,
"pre_compartment_name": "soma",
"post_compartment_name" : "soma",
"params": inh_synapse_params.copy(),
}
# synapse["params"]["Erev"] = -75
synapse["params"]["w"] = 50
synapse["params"]["delay"] = np.random.randint(20, 50)
synapses.append(synapse)
if (experiment == "OLM2PV2" or experiment == "OLM2PV1andPV2"):
for idx in range(NsOLM2PV):
post_ind = np.random.randint(Nglu+NgabaCR+NgabaPV1+NgabaPacPV1, Nglu+NgabaCR+NgabaPV1+NgabaPacPV1+NgabaPV2+NgabaPacPV2)
pre_ind = np.random.randint(Np + Nb, Np + Nb + Nolm) + NNseptum
synapse = {
"type" : "ComplexSynapse",
"pre_ind": pre_ind,
"post_ind": post_ind,
"pre_compartment_name": "soma",
"post_compartment_name" : "soma",
"params": inh_synapse_params.copy(),
}
# synapse["params"]["Erev"] = -75
synapse["params"]["w"] = 50
synapse["params"]["delay"] = np.random.randint(20, 50)
synapses.append(synapse)
duration = 500
dt = 0.1
sim = SimmulationParams()
net = lib.Network(neurons, synapses)
for _ in range(4):
curent_time = time.time()
net.integrate(dt, duration, sim.iext_function)
print ("Calculation time is %0.3f sec" % (time.time() - curent_time) )
net.save_results(path+"all_results")
"""
def run_model(sim, path):
soma_params = {
"V0": 0.0,
"C": 3.0,
"Iextmean": -1.0,
"Iextvarience": 0.5,
"ENa": 120.0,
"EK": -15.0,
"El": 0.0,
"ECa": 140.0,
"CCa": 0.05,
"sfica": 0.13,
"sbetaca": 0.075,
"gbarNa": 30.0,
"gbarK_DR": 17.0,
"gbarK_AHP": 0.8,
"gbarK_C ": 15.0,
"gl": 0.1,
"gbarCa": 6.0,
}
dendrite_params = {
"V0": 0.0,
"C": 3.0,
"Iextmean": -1.0,
"Iextvarience": 0.5,
"ENa": 120.0,
"EK": -15.0,
"El": 0.0,
"ECa": 140.0,
"CCa": 0.05,
"sfica": 0.13,
"sbetaca": 0.075,
"gbarNa": 0.0,
"gbarK_DR": 0.0,
"gbarK_AHP": 0.8,
"gbarK_C ": 5.0,
"gl": 0.1,
"gbarCa": 5.0,
}
connection_params = {
"compartment1": "soma",
"compartment2": "dendrite",
"p": 0.5,
"g": 1.5,
}
basket_fs_neuron = {
"V0": -65.0,
"Iextmean": -0.5,
"Iextvarience": 0.5,
"ENa": 50.0,
"EK": -90.0,
"El": -65.0,
"gbarNa": 55.0,
"gbarK": 8.0,
"gl": 0.1,
"fi": 10,
}
olm_params = {
"V0": -70,
"gl": 0.05,
"El": -70,
"gbarNa": 10.7,
"ENa": 90,
"gbarK": 31.9,
"EK": -100,
"gbarKa": 16.5,
"gbarH": 0.05,
"EH": -32.9,
"Iextmean": 0.0,
"Iextvarience": 0.5,
}
CosSpikeGeneratorParams = {
"freq": 5.0, # frequency in Hz
"phase": 0.0, # phase in rad
"latency": 10.0, # in ms
"probability": 0.01,
"threshold": 0.3,
}
PoisonSpikeGenerator = {
"latency": 10.0, # in ms
"probability": 0.00001,
}
simple_ext_synapse_params = {
"Erev": 60.0,
"gbarS": 0.005,
"tau": 0.5, # 0.2, # 1.3–2
"w": 20.0,
"delay": 0,
}
simple_inh_synapse_params = {
"Erev": -15.0,
"gbarS": 0.005,
"tau": 0.2, # 4.2-7.2 ms
"w": 100.0,
"delay": 0,
}
ext_synapse_params = {
"Erev": 60.0,
"gbarS": 0.005,
"alpha_s": 1.1,
"beta_s": 0.19,
"K": 5.0,
"teta": 2.0,
"w": 10.0,
"delay": 0,
}
inh_synapse_params = {
"Erev": -15.0,
"gbarS": 0.005,
"alpha_s": 14.0,
"beta_s": 0.07,
"K": 2.0,
"teta": 0.0,
"w": 10.0,
"delay": 0,
}
if (sim.get_mode() == "variate_frequency"):
CosSpikeGeneratorParams["freq"] = sim.p
neurons = []
synapses = []
Np = 400 # number of pyramide neurons
Nb = 50 # number of basket cells
Nolm = 50 # 50 # number of olm cells
NSG = 100 # 100 # number of septum spike generators
Nec = 100 # number of EC inputs
Ncs = 100 # 100 # number of Shaffer collateral iunput
Ns = 600 # number synapses between pyramide cells
Nbasket2pyr = 3 # 100 4 # number synapses from basket interneuron to one pyramide neuron
Nolm2pyr = 10 # 4 # number synapses from olm interneuron to one pyramide neuron
Nspyr2basket = 10 # number synapses from one pyramide to interneurons
Nspyr2OLM = 20
# number synapses from septal generators to hippocampal interneurons
Nspv12bas = 400
Nspv22olm = 400
NsEc2pyr = 150 # 40
NsCs2pyr = 150
NsCs2bas = 25
indexes = {
"pyr": [],
"bas": [],
"olm": [],
"pv1": [],
"pv2": [],
"mec": [],
"cs": [],
}
for idx in range(Np):
soma = {"soma": soma_params.copy()}
soma["soma"]["V0"] = 0.5 * np.random.randn()
soma["soma"]["Iextmean"] += 0.5 * np.random.randn()
dendrite = {"dendrite": dendrite_params.copy()}
dendrite["dendrite"]["V0"] = soma["soma"]["V0"]
dendrite["dendrite"]["Iextmean"] += 0.5 * np.random.randn()
connection = connection_params.copy()
neuron = {
"type": "pyramide",
"compartments": [soma, dendrite],
"connections": [connection]
}
indexes["pyr"].append(len(neurons))
neurons.append(neuron)
for idx in range(Nb):
neuron = {"type": "FS_Neuron", "compartments": basket_fs_neuron.copy()}
neuron["compartments"]["V0"] += 10 * np.random.rand()
neuron["compartments"]["Iextmean"] += 0.5 * np.random.randn()
indexes["bas"].append(len(neurons))
neurons.append(neuron)
for idx in range(Nolm):
neuron = {"type": "olm_cell", "compartments": olm_params.copy()}
neuron["compartments"]["V0"] += 10 * np.random.rand()
neuron["compartments"]["Iextmean"] += 0.5 * np.random.randn()
indexes["olm"].append(len(neurons))
neurons.append(neuron)
for idx in range(NSG):
"""
if (sim.get_mode() == "only_one_rhytm" and sim.p[1] == 0 and idx >= NSG//2):
neuron = {
"type" : "PoisonSpikeGenerator",
"compartments" : PoisonSpikeGenerator.copy()
}
indexes["pv2"].append(len(neurons))
neurons.append(neuron)
continue
if (sim.get_mode() == "only_one_rhytm" and sim.p[0] == 0 and idx < NSG//2):
neuron = {
"type" : "PoisonSpikeGenerator",
"compartments" : PoisonSpikeGenerator.copy()
}
indexes["pv1"].append(len(neurons))
neurons.append(neuron)
continue
"""
neuron = {
"type": "CosSpikeGenerator",
"compartments": CosSpikeGeneratorParams.copy()
}
neuron["compartments"]["phase"] = -2.15 + 1.5 # np.pi # 0.5 - 2.15
if (idx >= NSG // 2):
"""
if (sim.get_mode() == "different_phase_shift"):
neuron["compartments"]["phase"] = sim.p
indexes["pv1"].append(len(neurons))
else:
neuron["compartments"]["phase"] = 0.5 * np.pi + 2.15 # !!!!
indexes["pv2"].append(len(neurons))
"""
neuron["compartments"]["phase"] = 1.5 # 0.2 #np.pi + 2.15 # !!!!
indexes["pv2"].append(len(neurons))
else:
indexes["pv1"].append(len(neurons))
neurons.append(neuron)
for idx in range(Nec):
neuron = {
"type": "CosSpikeGenerator",
"compartments": CosSpikeGeneratorParams.copy()
}
neuron["compartments"]["threshold"] = 0.3
neuron["compartments"]["phase"] = -2.79 # !!!!!!!!
indexes["mec"].append(len(neurons))
neurons.append(neuron)
for idx in range(Ncs):
neuron = {
"type": "CosSpikeGenerator",
"compartments": CosSpikeGeneratorParams.copy()
}
neuron["compartments"]["threshold"] = 0.3
neuron["compartments"]["phase"] = 0 # !!!!!!!!
indexes["cs"].append(len(neurons))
neurons.append(neuron)
for idx in range(Ns):
#synapse beteween pyramides
pre_ind = np.random.choice(indexes["pyr"])
post_ind = np.random.choice(indexes["pyr"])
if (pre_ind == post_ind):
post_ind = np.random.randint(0, Np)
synapse = {
"type": "ComplexSynapse", # "SimpleSynapseWithDelay",
"pre_ind": pre_ind,
"post_ind": post_ind,
"pre_compartment_name": "soma",
"post_compartment_name": "soma",
"params": ext_synapse_params.copy(),
}
synapse["params"]["delay"] = np.random.randint(20, 50)
synapses.append(synapse)
for _ in range(Nbasket2pyr):
for idx in indexes["pyr"]:
# synapses from basket cells to pyramides
pre_ind = np.random.choice(indexes["bas"])
synapse = {
"type": "ComplexSynapse", # "SimpleSynapseWithDelay",
"pre_ind": pre_ind,
"post_ind": idx,
"pre_compartment_name": "soma",
"post_compartment_name": "soma",
"params": inh_synapse_params.copy(),
}
synapse["params"]["delay"] = np.random.randint(20, 50)
synapses.append(synapse)
for _ in range(Nolm2pyr):
for idx in indexes["pyr"]:
# synapses from OLM cells to pyramides
pre_ind = np.random.choice(indexes["olm"])
synapse = {
"type": "ComplexSynapse", # "SimpleSynapseWithDelay",
"pre_ind": pre_ind,
"post_ind": idx,
"pre_compartment_name": "soma",
"post_compartment_name": "dendrite",
"params": inh_synapse_params.copy(),
}
synapse["params"]["delay"] = np.random.randint(20, 50)
synapses.append(synapse)
for _ in range(Nspyr2OLM):
for idx in indexes["pyr"]:
pre_ind = idx
# set synapse from pyramide to OLM neuron
post_ind = np.random.choice(indexes["olm"])
synapse = {
"type": "ComplexSynapse", # "SimpleSynapseWithDelay",
"pre_ind": pre_ind,
"post_ind": post_ind,
"pre_compartment_name": "soma",
"post_compartment_name": "soma",
"params": ext_synapse_params.copy(),
}
synapse["params"]["delay"] = np.random.randint(20, 50)
synapses.append(synapse)
for _ in range(Nspyr2basket):
for idx in indexes["pyr"]:
# set synapse from pyramide to basket neuron
pre_ind = idx
post_ind = np.random.choice(indexes["bas"])
synapse = {
"type": "ComplexSynapse", # "SimpleSynapseWithDelay",
"pre_ind": pre_ind,
"post_ind": post_ind,
"pre_compartment_name": "soma",
"post_compartment_name": "soma",
"params": ext_synapse_params.copy(),
}
synapse["params"]["delay"] = np.random.randint(20, 50)
synapses.append(synapse)
for idx in range(Nspv12bas):
# set synapses from pv1 to basket
pre_ind = np.random.choice(indexes["pv1"])
post_ind = np.random.choice(indexes["bas"])
synapse = {
"type": "SimpleSynapseWithDelay",
"pre_ind": pre_ind,
"post_ind": post_ind,
"pre_compartment_name": "soma",
"post_compartment_name": "soma",
"params": simple_inh_synapse_params.copy(),
}
# synapse["params"]["Erev"] = -75
# synapse["params"]["w"] = 100
synapse["params"]["delay"] = np.random.randint(20, 50)
synapses.append(synapse)
for idx in range(Nspv22olm):
# set synapses from pv1 to olm
pre_ind = np.random.choice(indexes["pv2"])
post_ind = np.random.choice(indexes["olm"])
synapse = {
"type": "SimpleSynapseWithDelay",
"pre_ind": pre_ind,
"post_ind": post_ind,
"pre_compartment_name": "soma",
"post_compartment_name": "soma",
"params": simple_inh_synapse_params.copy()
}
# synapse["params"]["Erev"] = -75
# synapse["params"]["w"] = 100
synapse["params"]["delay"] = np.random.randint(20, 50)
synapses.append(synapse)
for idx in range(NsCs2pyr):
# synapses from shaffer colletarel to pyramodes
pre_ind = np.random.choice(indexes["cs"])
post_ind = np.random.choice(indexes["pyr"])
synapse = {
"type": "SimpleSynapseWithDelay",
"pre_ind": pre_ind,
"post_ind": post_ind,
"pre_compartment_name": "soma",
"post_compartment_name": "soma",
"params": simple_ext_synapse_params.copy()
}
# synapse["params"]["w"] = 20
synapse["params"]["delay"] = np.random.randint(20, 50)
synapses.append(synapse)
for idx in range(NsCs2bas):
# synapses from shaffer colletarel to basket
pre_ind = np.random.choice(indexes["cs"])
post_ind = np.random.choice(indexes["bas"])
synapse = {
"type": "SimpleSynapseWithDelay",
"pre_ind": pre_ind,
"post_ind": post_ind,
"pre_compartment_name": "soma",
"post_compartment_name": "soma",
"params": simple_ext_synapse_params.copy()
}
#synapse["params"]["w"] = 100
synapse["params"]["delay"] = np.random.randint(20, 50)
synapses.append(synapse)
for idx in range(NsEc2pyr):
# synapses from perforant pathway to pyramodes
pre_ind = np.random.choice(indexes["mec"])
post_ind = np.random.choice(indexes["pyr"])
synapse = {
"type": "SimpleSynapseWithDelay",
"pre_ind": pre_ind,
"post_ind": post_ind,
"pre_compartment_name": "soma",
"post_compartment_name": "dendrite",
"params": simple_ext_synapse_params.copy()
}
#synapse["params"]["w"] = 1
synapse["params"]["delay"] = np.random.randint(20, 50)
synapses.append(synapse)
"""
for syn in synapses:
print (neurons[syn["pre_ind"]]["type"] + " -> " + neurons[syn["post_ind"]]["type"])
print ( str(syn["pre_ind"]) + " -> " + str(syn["post_ind"]) )
return
"""
dt = 0.1
duration = 500
net = lib.Network(neurons, synapses)
for _ in range(4):
net.integrate(dt, duration)
net.save_results(path + "_all_results")
"""
V = net.getVhist()
VmeanSoma = 0
VmeanDendrite = 0
np.save(path + "V", V)
for v in V:
try:
VmeanSoma += v["soma"]
VmeanDendrite += v["dendrite"]
except KeyError:
continue
VmeanSoma /= Np
VmeanDendrite /= Np
t = np.linspace(duration-500, duration, VmeanSoma.size)
plt.figure()
plt.subplot(211)
plt.plot(t, VmeanSoma, "b")
plt.subplot(212)
plt.plot(t, VmeanDendrite, "r")
plt.savefig(path + "mean_V", dpi=300)
lfp = net.getLFP()
lfp = plib.butter_bandpass_filter(lfp, 1, 80, 1000/dt, 3)
np.save(path + "lfp", lfp)
currents = net.getfullLFP()
np.save(path + " currents", currents)
plt.figure()
plt.plot(t, lfp)
#plt.xlim(1000, 1500)
# lfp_half = lfp[t > duration/2]
plt.savefig(path + "lfp", dpi=300)
fft_y = np.abs(np.fft.rfft(lfp))/lfp.size
fft_x = np.fft.rfftfreq(lfp.size, 0.001*dt)
plt.figure()
plt.plot(fft_x[1:], fft_y[1:])
plt.xlim(2, 50)
theta_power = np.sum(fft_y[(fft_x>4)&(fft_x<12)])/np.sum(fft_y)
plt.savefig(path + "spectra_of_lfp", dpi=300)
firing = net.getFiring()
np.save(path + "firing", firing)
plt.figure()
cum_it = Np
sl = firing[1, :] <= cum_it
pyr_line, = plt.plot(firing[0, sl], firing[1, sl], '.b', label='Pyramide')
sl = (firing[1, :] > cum_it) & (firing[1, :] <= cum_it + Nb)
basket_line, = plt.plot(firing[0, sl], firing[1, sl], '.g', label='Basket')
cum_it += Nb
sl = (firing[1, :] > cum_it) & (firing[1, :] <= cum_it + Nolm)
olm_line, = plt.plot(firing[0, sl], firing[1, sl], '.m', label='OLM')
cum_it += Nolm
sl = (firing[1, :] > cum_it)
septal_line, = plt.plot(firing[0, sl], firing[1, sl], '.r', label='Septum')
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plt.savefig(path + "raster", dpi=300)
plt.show()
"""
print("Что-то посчиталось!!!")
return indexes
for idx in range(Ns2in):
pre_ind = np.random.randint(Np + Nb + Nolm + NSG//2, Np + Nb + Nolm + NSG)
post_ind = np.random.randint(Np + Nb, Np + Nb + Nolm)
synapse = {
"type" : "ComplexSynapse",
"pre_ind": pre_ind,
"post_ind": post_ind,
"pre_compartment_name": "soma",
"post_compartment_name" : "soma",
"params": inh_synapse_params.copy()
}
# synapse["params"]["Erev"] = -75
synapse["params"]["w"] = 100
synapse["params"]["delay"] = np.random.randint(20, 50)
synapses.append(synapse)
"""
for syn in synapses:
if (syn["params"]["Erev"] == 60):
syn["params"]["w"] *= 2
if (syn["params"]["Erev"] == -15):
syn["params"]["w"] /= 2
dt = 0.1
duration = 500
net = lib.Network(neurons, synapses)
for _ in range(4):
net.integrate(dt, duration, sim.iext_function)
net.save_results(path + "_all_results")
def run_model(path, params):
cluster_pacemaker = {
"V0": -50.0,
"Iextmean": 1.2,
"Iextvarience": 0.5,
"ENa": 50.0,
"EK": -90.0,
"El": -50.0,
"Eh": -40.0,
"gbarNa": 55.0,
"gbarK": 8.0,
"gbarKS": 12.0,
"gbarH": 1.0,
"gl": 0.1,
"fi": 5,
}
fs_neuron = {
"V0": -65.0,
"Iextmean": 0.5,
"Iextvarience": 0.5,
"ENa": 50.0,
"EK": -90.0,
"El": -65.0,
"gbarNa": 55.0,
"gbarK": 8.0,
"gl": 0.1,
"fi": 10,
}
ext_synapse_params = {
"Erev": 60.0,
"gbarS": 0.005,
"alpha_s": 1.1,
"beta_s": 0.19,
"K": 5.0,
"teta": 2.0,
"w": 1.0,
"delay": 0,
}
inh_synapse_params = {
"Erev": -15.0,
"gbarS": 0.005,
"alpha_s": 14.0,
"beta_s": 0.07,
"K": 2.0,
"teta": 0.0,
"w": 1.0,
"delay": 0,
}
HS_neuron_params = {
"V0": -63.0,
"Iextmean": 0.5,
"Iextvarience": 0.0,
"ENa": 55.0,
"EK": -90.0,
"El": -65.0,
"EH": -40.0,
"ECa": 120.0,
"Ca": 0.0,
"KD": 30.0,
"gbarNa": 35.0,
"gbarK": 9.0,
"gl": 0.1,
"gbarK_Ca": 10,
"gbarCa": 1.0,
"gbarH": 0.15,
"alpha_Ca": 0.002,
"tau_Ca": 80,
}
########## set model of septum ################
# number of neurons
Nglu = params["Nglu"]
NgabaCR = params["NgabaCR"]
NgabaPV1 = params["NgabaPV1"]
NgabaPV2 = params["NgabaPV2"]
NgabaPacPV1 = params["NgabaPacPV1"]
NgabaPacPV2 = params["NgabaPacPV2"]
NgabaHS = params["NgabaHS"]
NN = Nglu + NgabaCR + NgabaPV1 + NgabaPV2 + NgabaPacPV1 + NgabaPacPV2
Sparsness = params["sparsness"] # sparsness in septal network
indexes = {
"glu": [],
"pv1": [],
"pv2": [],
"pv1_pac": [],
"pv2_pac": [],
"cr": [],
"hs": [],
}
neurons = []
for idx in range(Nglu):
neuron = {"type": "FS_Neuron", "compartments": fs_neuron.copy()}
neuron["compartments"]["V0"] += 10 * np.random.rand()
neuron["compartments"]["Iextmean"] = 0.5 # 0.5*np.random.randn()
indexes["glu"].append(len(neurons))
neurons.append(neuron)
for idx in range(NgabaCR):
neuron = {"type": "FS_Neuron", "compartments": fs_neuron.copy()}
neuron["compartments"]["V0"] += 10 * np.random.rand()
neuron["compartments"]["Iextmean"] = 0 # 0.1*np.random.randn()
indexes["cr"].append(len(neurons))
neurons.append(neuron)
for idx in range(NgabaPV1):
neuron = {"type": "FS_Neuron", "compartments": fs_neuron.copy()}
neuron["compartments"]["V0"] += 10 * np.random.rand()
neuron["compartments"]["Iextmean"] = 0.1 #0.5*np.random.randn()
indexes["pv1"].append(len(neurons))
neurons.append(neuron)
for idx in range(NgabaPacPV1):
neuron = {
"type": "ClusterNeuron",
"compartments": cluster_pacemaker.copy()
}
neuron["compartments"]["V0"] += 10 * np.random.rand()
neuron["compartments"]["Iextmean"] = 0.1 # 0.5*np.random.randn()
indexes["pv1"].append(len(neurons))
neurons.append(neuron)
for idx in range(NgabaPV2):
neuron = {"type": "FS_Neuron", "compartments": fs_neuron.copy()}
neuron["compartments"]["V0"] += 10 * np.random.rand()
neuron["compartments"]["Iextmean"] = 0.7 #0.5*np.random.randn()
indexes["pv2"].append(len(neurons))
neurons.append(neuron)
for idx in range(NgabaPacPV2):
neuron = {
"type": "ClusterNeuron",
"compartments": cluster_pacemaker.copy()
}
neuron["compartments"]["V0"] += 10 * np.random.rand()
neuron["compartments"]["Iextmean"] = 0.7 #0.5*np.random.randn()
indexes["pv2"].append(len(neurons))
neurons.append(neuron)
for idx in range(NgabaHS):
neuron = {
"type": "HS_projective_neuron",
"compartments": HS_neuron_params.copy()
}
neuron["compartments"]["V0"] += 10 * np.random.rand()
# neuron["compartments"]["Iextmean"] = 0.7 #0.5*np.random.randn()
indexes["hs"].append(len(neurons))
neurons.append(neuron)
synapses = []
# synapse from Glu to Glu neurons
for idx1 in indexes["glu"]:
for idx2 in indexes["glu"]:
if (Sparsness < np.random.rand() or idx1 == idx2):
continue
pre_ind = idx1
post_ind = idx2
synapse = {
"type": "ComplexSynapse",
"pre_ind": pre_ind,
"post_ind": post_ind,
"pre_compartment_name": "soma",
"post_compartment_name": "soma",
"params": ext_synapse_params.copy(),
}
synapse["params"]["w"] = 1
synapses.append(synapse)
# synapses from Glu naurons to GABA(CR) neurons
for idx1 in indexes["glu"]:
for idx2 in indexes["cr"]:
if (Sparsness < np.random.rand() or idx1 == idx2):
continue
pre_ind = idx1
post_ind = idx2
synapse = {
"type": "ComplexSynapse",
"pre_ind": pre_ind,
"post_ind": post_ind,
"pre_compartment_name": "soma",
"post_compartment_name": "soma",
"params": ext_synapse_params.copy(),
}
synapse["params"]["w"] = 0.2
synapses.append(synapse)
# synapses from GABA(CR) neurons to Glu naurons
for idx1 in indexes["cr"]:
for idx2 in indexes["glu"]:
if (idx1 == idx2):
continue
pre_ind = idx1
post_ind = idx2
synapse = {
"type": "ComplexSynapse",
"pre_ind": pre_ind,
"post_ind": post_ind,
"pre_compartment_name": "soma",
"post_compartment_name": "soma",
"params": inh_synapse_params.copy(),
}
synapse["params"]["w"] = 1.5
synapses.append(synapse)
# synapses from Glu neurons to PV1 and pac-PV1 naurons
for idx1 in indexes["glu"]:
for idx2 in indexes["pv1"]:
if (Sparsness < np.random.rand() or idx1 == idx2):
continue
pre_ind = idx1
post_ind = idx2
synapse = {
"type": "ComplexSynapse",
"pre_ind": pre_ind,
"post_ind": post_ind,
"pre_compartment_name": "soma",
"post_compartment_name": "soma",
"params": ext_synapse_params.copy(),
}
synapse["params"]["w"] = 1
synapses.append(synapse)
# synapses from PV1 and pac-PV1 naurons to PV2 and pac-PV2
for idx1 in indexes["pv1"]:
for idx2 in indexes["pv2"]:
if (Sparsness < np.random.rand() or idx1 == idx2):
continue
pre_ind = idx1
post_ind = idx2
synapse = {
"type": "ComplexSynapse",
"pre_ind": pre_ind,
"post_ind": post_ind,
"pre_compartment_name": "soma",
"post_compartment_name": "soma",
"params": inh_synapse_params.copy(),
}
synapse["params"]["w"] = 0.5
synapses.append(synapse)
# synapses from PV2 and pac-PV2 naurons to PV1 and pac-PV1
for pre_ind in indexes["pv2"]:
for post_ind in indexes["pv1"]:
if (Sparsness < np.random.rand() or idx1 == idx2):
continue
synapse = {
"type": "ComplexSynapse",
"pre_ind": pre_ind,
"post_ind": post_ind,
"pre_compartment_name": "soma",
"post_compartment_name": "soma",
"params": inh_synapse_params.copy(),
}
synapse["params"]["w"] = 0.1
synapses.append(synapse)
for s in synapses:
s["params"]["w"] /= params["sparsness"]
duration = params["duration"]
dt = 0.1
net = lib.Network(neurons, synapses)
for _ in range(1):
curent_time = time.time()
net.integrate(dt, duration)
print("Calculation time is %0.3f sec" % (time.time() - curent_time))
net.save_results(path + "all_results")