class Synapses(Enum):
GLUTAMATERGIC = SynapseModel(
'Glutamatergic',
nest_model=SynapseModels.STATIC_SYNAPSE.value,
params={'delay': Constants.SYNAPTIC_DELAY_EX.value})
GABAERGIC = SynapseModel(
'GABAergic',
nest_model=SynapseModels.STATIC_SYNAPSE.value,
params={'delay': Constants.SYNAPTIC_DELAY_EX.value})
class MotorCortexColumns(AbstractColumns):
"""
Implementation of the AbstractColumns class with overrided methods
"""
_logger = api_kernel.log.getLogger("MotorCortexColumns")
_glu_syn_params = {
'delay': [1, 2.5], # Synaptic delay
'alpha': 1.0, # Coeficient for inhibitory STDP time (alpha * lambda)
'lambda': 0.01, # Time interval for STDP
'Wmax': 10, # Maximum possible weight
'mu_minus': 0.01, # STDP depression step
'mu_plus': 0.01 # STDP potential step
}
_Glutamatergic = SynapseModel("Glutamatergic_columns",
nest_model=STDP_SYNAPSE,
params=_glu_syn_params)
def __init__(self, width, height):
"""
Args:
width (int): size (width) of the column structure
height (int): size (height) of the column structure
"""
self._columns = {}
self._width_x = width
self._height_y = height
self._column_mapping = np.arange(height * width).reshape(
(height, width))
# Create cortex columns (by layers)
for column in range(width * height):
self._logger.info("column {} of {}".format(column + 1,
width * height))
self._columns[column] = MotorCortexLayers(column)
# Connect columns
self._setConnectomes()
def _setConnectomes(self):
# Setup synapse model
for column_index in self.getColumnsIndexes():
for neighbor_index in self._getColumnNeighbors(column_index):
self.columns(column_index).layers(L2).nuclei(Glu).connect(
self.columns(neighbor_index).layers(L2).nuclei(Glu),
synapse=self._Glutamatergic,
weight=0.5,
conn_prob=0.093)
class MotorCortexLayers(AbstractLayers):
"""
Class implementation of the Motor cortex layers class
"""
# Common variable for objects
_nrn_parameters = {
't_ref': [2.5, 4.0], # Refractory period
'V_m': -70.0, #
'E_L': -70.0, #
'E_K': -77.0, #
'g_L': 30.0, #
'g_Na': 12000.0, #
'g_K': 3600.0, #
'C_m': 134.0, # Capacity of membrane (pF)
'tau_syn_ex': 0.2, # Time of excitatory action (ms)
'tau_syn_in': 2.0 # Time of inhibitory action (ms)
}
_glu_syn_params = {
'delay': [1, 2.5], # Synaptic delay
'alpha': 1.0, # Coeficient for inhibitory STDP time (alpha * lambda)
'lambda': 0.01, # Time interval for STDP
'Wmax': 10, # Maximum possible weight
'mu_minus': 0.01, # STDP depression step
'mu_plus': 0.01 # STDP potential step
}
_gaba_syn_params = {
'delay': [1, 2.5], # Synaptic delay
'alpha': 1.0, # Coeficient for inhibitory STDP time (alpha * lambda)
'lambda': 0.01, # Time interval for STDP
'Wmax': -10.0, # Maximum possible weight
'mu_minus': 0.01, # STDP depression step
'mu_plus': 0.01 # STDP potential step
}
# Setup synapse models
_Glutamatergic = SynapseModel("Glutamatergic_layers",
nest_model=STDP_SYNAPSE,
params=_glu_syn_params)
_GABAergic = SynapseModel("GABAergic_layers",
nest_model=STDP_SYNAPSE,
params=_gaba_syn_params)
def __init__(self, column_index):
# Create Nucleus objects and put into the dict of the layers
self._dict_layers = {
L2: Nucleus("Column {} Layer 2".format(column_index)),
L3: Nucleus("Column {} Layer 3".format(column_index)),
L4: Nucleus("Column {} Layer 4".format(column_index)),
L5A: Nucleus("Column {} Layer 5A".format(column_index)),
L5B: Nucleus("Column {} Layer 5B".format(column_index)),
L6: Nucleus("Column {} Layer 6".format(column_index))
}
# Add sub-nucleui and create neurons without sqaling (because of flag 'forColumns'=True)
self.layers(L2).addSubNucleus(Glu,
number=546,
params=self._nrn_parameters,
forColumns=True)
self.layers(L2).addSubNucleus(GABA,
number=107,
params=self._nrn_parameters,
forColumns=True)
self.layers(L3).addSubNucleus(Glu,
number=1145,
params=self._nrn_parameters,
forColumns=True)
self.layers(L3).addSubNucleus(GABA,
number=123,
params=self._nrn_parameters,
forColumns=True)
self.layers(L4).addSubNucleus(Glu,
number=1656,
params=self._nrn_parameters,
forColumns=True)
self.layers(L4).addSubNucleus(GABA,
number=140,
params=self._nrn_parameters,
forColumns=True)
self.layers(L5A).addSubNucleus(Glu,
number=454,
params=self._nrn_parameters,
forColumns=True)
self.layers(L5A).addSubNucleus(GABA,
number=90,
params=self._nrn_parameters,
forColumns=True)
self.layers(L5B).addSubNucleus(Glu,
number=641,
params=self._nrn_parameters,
forColumns=True)
self.layers(L5B).addSubNucleus(GABA,
number=131,
params=self._nrn_parameters,
forColumns=True)
self.layers(L6).addSubNucleus(Glu,
number=1288,
params=self._nrn_parameters,
forColumns=True)
self.layers(L6).addSubNucleus(GABA,
number=127,
params=self._nrn_parameters,
forColumns=True)
# Invoke method to create synapses
self.setConnectomes()
def setConnectomes(self):
"""
Set connectomes between layers
"""
# Connection probability
L2_to_L2 = 0.093
L2_to_L5A = 0.043
L3_to_L2 = 0.055
L3_to_L3 = 0.187
L3_to_L5B = 0.018
L4_to_L2 = 0.009
L4_to_L3 = 0.024
L4_to_L4 = 0.243
L4_to_L5A = 0.007
L4_to_L5B = 0.007
L5A_to_L3 = 0.057
L5A_to_L5A = 0.191
L5B_to_L2 = 0.083
L5B_to_L5B = 0.072
L5B_to_L6 = 0.020
L6_to_L4 = 0.032
L6_to_L5A = 0.032
L6_to_L6 = 0.028
# Setup connectomes
self.layers(L2).nuclei(Glu).connect(self.layers(L2).nuclei(GABA),
synapse=self._Glutamatergic,
weight=0.7) #, conn_prob=L2_to_L2)
self.layers(L2).nuclei(Glu).connect(
self.layers(L5A).nuclei(Glu),
synapse=self._Glutamatergic,
weight=1.0) #, conn_prob=L2_to_L5A)
self.layers(L2).nuclei(GABA).connect(self.layers(L2).nuclei(Glu),
synapse=self._GABAergic,
weight=-1.5)
self.layers(L3).nuclei(Glu).connect(self.layers(L2).nuclei(Glu),
synapse=self._Glutamatergic,
weight=1.0) #, conn_prob=L3_to_L2)
self.layers(L3).nuclei(Glu).connect(self.layers(L3).nuclei(GABA),
synapse=self._Glutamatergic,
weight=0.5) #, conn_prob=L3_to_L3)
self.layers(L3).nuclei(Glu).connect(
self.layers(L5B).nuclei(Glu),
synapse=self._Glutamatergic,
weight=1.0) #, conn_prob=L3_to_L5B)
self.layers(L3).nuclei(GABA).connect(self.layers(L3).nuclei(Glu),
synapse=self._GABAergic,
weight=-1.5)
self.layers(L4).nuclei(Glu).connect(self.layers(L2).nuclei(Glu),
synapse=self._Glutamatergic,
weight=0.6) #, conn_prob=L4_to_L2)
self.layers(L4).nuclei(Glu).connect(self.layers(L3).nuclei(Glu),
synapse=self._Glutamatergic,
weight=0.6) #, conn_prob=L4_to_L3)
self.layers(L4).nuclei(Glu).connect(self.layers(L4).nuclei(Glu),
synapse=self._Glutamatergic,
weight=0.7) #, conn_prob=L4_to_L4)
self.layers(L4).nuclei(Glu).connect(self.layers(L4).nuclei(GABA),
synapse=self._Glutamatergic,
weight=0.5) #, conn_prob=L4_to_L4)
self.layers(L4).nuclei(Glu).connect(
self.layers(L5A).nuclei(Glu),
synapse=self._Glutamatergic,
weight=0.6) #, conn_prob=L4_to_L5A)
self.layers(L4).nuclei(Glu).connect(
self.layers(L5B).nuclei(Glu),
synapse=self._Glutamatergic,
weight=0.6) #, conn_prob=L4_to_L5B)
self.layers(L4).nuclei(GABA).connect(self.layers(L4).nuclei(Glu),
synapse=self._GABAergic,
weight=-1.5)
self.layers(L5A).nuclei(Glu).connect(
self.layers(L3).nuclei(Glu),
synapse=self._Glutamatergic,
weight=1.0) #, conn_prob=L5A_to_L3)
self.layers(L5A).nuclei(Glu).connect(
self.layers(L5A).nuclei(Glu),
synapse=self._Glutamatergic,
weight=0.7) #, conn_prob=L5A_to_L5A)
self.layers(L5A).nuclei(Glu).connect(
self.layers(L5A).nuclei(GABA),
synapse=self._Glutamatergic,
weight=0.5) #, conn_prob=L5A_to_L5A)
self.layers(L5A).nuclei(GABA).connect(self.layers(L5A).nuclei(Glu),
synapse=self._GABAergic,
weight=-1.5)
self.layers(L5B).nuclei(Glu).connect(
self.layers(L2).nuclei(Glu),
synapse=self._Glutamatergic,
weight=1.0) #, conn_prob=L5B_to_L2)
self.layers(L5B).nuclei(Glu).connect(
self.layers(L5B).nuclei(GABA),
synapse=self._Glutamatergic,
weight=0.5) #, conn_prob=L5B_to_L5B)
self.layers(L5B).nuclei(Glu).connect(
self.layers(L6).nuclei(Glu),
synapse=self._Glutamatergic,
weight=1.0) #, conn_prob=L5B_to_L6)
self.layers(L5B).nuclei(GABA).connect(self.layers(L5B).nuclei(Glu),
synapse=self._GABAergic,
weight=-1.5)
self.layers(L6).nuclei(Glu).connect(self.layers(L4).nuclei(Glu),
synapse=self._Glutamatergic,
weight=0.7) #, conn_prob=L6_to_L4)
self.layers(L6).nuclei(Glu).connect(
self.layers(L5A).nuclei(Glu),
synapse=self._Glutamatergic,
weight=1.0) #, conn_prob=L6_to_L5A)
self.layers(L6).nuclei(Glu).connect(self.layers(L6).nuclei(GABA),
synapse=self._Glutamatergic,
weight=0.5) #, conn_prob=L6_to_L6)
self.layers(L6).nuclei(GABA).connect(self.layers(L4).nuclei(Glu),
synapse=self._GABAergic,
weight=-1.5)
self.layers(L6).nuclei(GABA).connect(self.layers(L6).nuclei(Glu),
synapse=self._GABAergic,
weight=-1.5)
from neucogar.namespaces import *
from neucogar.api_kernel import CreateNetwork
from neucogar.SynapseModel import SynapseModel
from brain_parts import *
# Init synapses
Glutamatergic = SynapseModel("Glutamatergic", nest_model=STDP_SYNAPSE, params=stdp_glu_params)
GABAergic = SynapseModel("GABAergic", nest_model=STDP_SYNAPSE, params=stdp_gaba_params)
Dopaminergic_ex = SynapseModel("Dopaminergic_ex", nest_model=STDP_DOPA_SYNAPSE, params=stdp_dopa_ex_params)
Dopaminergic_in = SynapseModel("Dopaminergic_in", nest_model=STDP_DOPA_SYNAPSE, params=stdp_dopa_in_params)
# Set connectomes
motor_cortex.nuclei(Glu_0).connect(striatum.nuclei(GABA_D1), synapse=Glutamatergic, weight=0.1)
motor_cortex.nuclei(Glu_0).connect(striatum.nuclei(GABA_D2), synapse=Glutamatergic, weight=0.1)
motor_cortex.nuclei(Glu_0).connect(striatum.nuclei(GABA_D2), synapse=Glutamatergic, weight=0.08)
motor_cortex.nuclei(Glu_0).connect(stn.nuclei(Glu), synapse=Glutamatergic, weight=1)
motor_cortex.nuclei(Glu_0).connect(thalamus.nuclei(Glu), synapse=Glutamatergic, weight=0.08)
thalamus.nuclei(Glu).connect(motor_cortex.nuclei(Glu_1), synapse=Glutamatergic, weight=1)
striatum.nuclei(GABA_D1).connect(snr.nuclei(GABA), synapse=GABAergic, weight=-0.01)
striatum.nuclei(GABA_D1).connect(gpi.nuclei(GABA), synapse=GABAergic, weight=-0.01)
striatum.nuclei(GABA_D2).connect(gpe.nuclei(GABA), synapse=GABAergic, weight=-0.01)
gpe.nuclei(GABA).connect(stn.nuclei(Glu), synapse=GABAergic, weight=-0.01)
stn.nuclei(Glu).connect(snr.nuclei(GABA), synapse=Glutamatergic, weight=1)
stn.nuclei(Glu).connect(gpi.nuclei(GABA), synapse=Glutamatergic, weight=1)
stn.nuclei(Glu).connect(gpe.nuclei(GABA), synapse=Glutamatergic, weight=0.3)
from neucogar.namespaces import *
from neucogar.api_kernel import CreateNetwork
from neucogar.SynapseModel import SynapseModel
from .brain_parts import *
from dopamine.connectomes import *
# Init synapses
Serotoninergic_ex = SynapseModel("Serotoninergic_ex",
nest_model=STDP_SERO_SYNAPSE,
params=stdp_sero_ex_params)
Serotoninergic_in = SynapseModel("Serotoninergic_in",
nest_model=STDP_SERO_SYNAPSE,
params=stdp_sero_in_params)
# Glu 0 - VA/VL
# GLu1 - midline nuclei
#pfc.nuclei(Glu).connect(drn.nuclei(HT5), synapse=Glutamatergic, weight=10)
#pfc.nuclei(GABA).connect(pfc.nuclei(Glu), synapse=GABAergic, weight=-10)
###thalamus.nuclei(Glu_1).connect(pfc.columns(0).layers(L4).nuclei(Glu), synapse=Glutamatergic, weight=5)
#vta.nuclei(DA).connect(pfc.columns(0).layers(L4).nuclei(Glu), synapse=Dopaminergic_ex, weight=5)
#vta.nuclei(DA).connect(drn.nuclei(HT5), synapse=Dopaminergic_ex, weight=10)
#snc.nuclei(DA).connect(drn.nuclei(HT5), synapse=Dopaminergic_ex, weight=10)
# !!! locus_coeruleus.nuclei(NA).connect(drn.nuclei(HT5), synapse=NA , weight=1)
locus_coeruleus.nuclei(NA).connect(vta.nuclei(DA), synapse=NA_ex,
'Wmax': 300, # Maximum possible weight
'mu_minus': 0.005, # STDP depression step
'mu_plus': 0.005 # STDP potential step
}
stdp_gaba = {
'delay': 1.25, # Synaptic delay
'alpha': 1.0, # Coeficient for inhibitory STDP time (alpha * lambda)
'lambda': 0.0075, # Time interval for STDP
'Wmax': -300.0, # Maximum possible weight
'mu_minus': 0.005, # STDP depression step
'mu_plus': 0.005 # STDP potential step
}
# synapses
Glutamatergic = SynapseModel("Glutamatergic",
nest_model=NEST_NAMESPACE.STDP_SYNAPSE,
params=stdp_glu)
GABAergic = SynapseModel("GABAergic",
nest_model=NEST_NAMESPACE.STDP_SYNAPSE,
params=stdp_gaba)
weight_Glu = 185
weight_GABA = -70
def connect(neuron_network):
# source is FLEX_MOTOR
neuron_network.connect(source=Layer1NeuronGroupNames.FLEX_MOTOR,
target=Layer1NeuronGroupNames.FLEX_RENSHAW,
synapse=Glutamatergic,
weight=weight_Glu)