def __init__(
self, n_neurons, machine_time_step, timescale_factor,
spikes_per_second=None, ring_buffer_sigma=None, constraints=None,
label=None,
tau_m=default_parameters['tau_m'], cm=default_parameters['cm'],
v_rest=default_parameters['v_rest'],
v_reset=default_parameters['v_reset'],
v_thresh=default_parameters['v_thresh'],
tau_syn_E=default_parameters['tau_syn_E'],
tau_syn_E2=default_parameters['tau_syn_E2'],
tau_syn_I=default_parameters['tau_syn_I'],
tau_refrac=default_parameters['tau_refrac'],
i_offset=default_parameters['i_offset'], v_init=None):
neuron_model = NeuronModelLeakyIntegrateAndFire(
n_neurons, machine_time_step, v_init, v_rest, tau_m, cm, i_offset,
v_reset, tau_refrac)
synapse_type = SynapseTypeDualExponential(
n_neurons, machine_time_step, tau_syn_E, tau_syn_E2, tau_syn_I)
input_type = InputTypeCurrent()
threshold_type = ThresholdTypeStatic(n_neurons, v_thresh)
AbstractPopulationVertex.__init__(
self, n_neurons=n_neurons, binary="IF_curr_exp_dual.aplx",
label=label,
max_atoms_per_core=IFCurrDualExp._model_based_max_atoms_per_core,
machine_time_step=machine_time_step,
timescale_factor=timescale_factor,
spikes_per_second=spikes_per_second,
ring_buffer_sigma=ring_buffer_sigma,
model_name="IF_curr_dual_exp", neuron_model=neuron_model,
input_type=input_type, synapse_type=synapse_type,
threshold_type=threshold_type, constraints=constraints)
def __init__(
self, n_neurons, machine_time_step, timescale_factor,
spikes_per_second=None, ring_buffer_sigma=None, constraints=None,
label=None,
a=default_parameters['a'], b=default_parameters['b'],
c=default_parameters['c'], d=default_parameters['d'],
i_offset=default_parameters['i_offset'],
u_init=default_parameters['u_init'],
v_init=default_parameters['v_init'],
tau_syn_E=default_parameters['tau_syn_E'],
tau_syn_I=default_parameters['tau_syn_I']):
neuron_model = NeuronModelIzh(
n_neurons, machine_time_step, a, b, c, d, v_init, u_init, i_offset)
synapse_type = SynapseTypeExponential(
n_neurons, machine_time_step, tau_syn_E, tau_syn_I)
input_type = InputTypeCurrent()
threshold_type = ThresholdTypeStatic(n_neurons, _IZK_THRESHOLD)
AbstractPopulationVertex.__init__(
self, n_neurons=n_neurons, binary="IZK_curr_exp.aplx", label=label,
max_atoms_per_core=IzkCurrExp._model_based_max_atoms_per_core,
machine_time_step=machine_time_step,
timescale_factor=timescale_factor,
spikes_per_second=spikes_per_second,
ring_buffer_sigma=ring_buffer_sigma,
model_name="IZK_curr_exp", neuron_model=neuron_model,
input_type=input_type, synapse_type=synapse_type,
threshold_type=threshold_type, constraints=constraints)
def __init__(
self, n_neurons, machine_time_step, timescale_factor,
spikes_per_second=None, ring_buffer_sigma=None,
incoming_spike_buffer_size=None, constraints=None, label=None,
a=default_parameters['a'], b=default_parameters['b'],
c=default_parameters['c'], d=default_parameters['d'],
i_offset=default_parameters['i_offset'],
u_init=default_parameters['u_init'],
v_init=default_parameters['v_init'],
tau_syn_E=default_parameters['tau_syn_E'],
tau_syn_I=default_parameters['tau_syn_I'],
e_rev_E=default_parameters['e_rev_E'],
e_rev_I=default_parameters['e_rev_I']):
neuron_model = NeuronModelIzh(
n_neurons, machine_time_step, a, b, c, d, v_init, u_init, i_offset)
synapse_type = SynapseTypeExponential(
n_neurons, machine_time_step, tau_syn_E, tau_syn_I)
input_type = InputTypeConductance(n_neurons, e_rev_E, e_rev_I)
threshold_type = ThresholdTypeStatic(n_neurons, _IZK_THRESHOLD)
AbstractPopulationVertex.__init__(
self, n_neurons=n_neurons, binary="IZK_cond_exp.aplx", label=label,
max_atoms_per_core=IzkCondExp._model_based_max_atoms_per_core,
machine_time_step=machine_time_step,
timescale_factor=timescale_factor,
spikes_per_second=spikes_per_second,
ring_buffer_sigma=ring_buffer_sigma,
incoming_spike_buffer_size=incoming_spike_buffer_size,
model_name="IZK_cond_exp", neuron_model=neuron_model,
input_type=input_type, synapse_type=synapse_type,
threshold_type=threshold_type, constraints=constraints)
def __init__(
self, n_neurons, machine_time_step, timescale_factor,
spikes_per_second=None, ring_buffer_sigma=None,
incoming_spike_buffer_size=None, constraints=None, label=None,
tau_m=default_parameters['tau_m'], cm=default_parameters['cm'],
v_rest=default_parameters['v_rest'],
v_reset=default_parameters['v_reset'],
v_thresh=default_parameters['v_thresh'],
tau_syn_E=default_parameters['tau_syn_E'],
tau_syn_I=default_parameters['tau_syn_I'],
tau_refrac=default_parameters['tau_refrac'],
i_offset=default_parameters['i_offset'],
e_rev_E=default_parameters['e_rev_E'],
e_rev_I=default_parameters['e_rev_I'], v_init=None):
neuron_model = NeuronModelLeakyIntegrateAndFire(
n_neurons, machine_time_step, v_init, v_rest, tau_m, cm, i_offset,
v_reset, tau_refrac)
synapse_type = SynapseTypeExponential(
n_neurons, machine_time_step, tau_syn_E, tau_syn_I)
input_type = InputTypeConductance(n_neurons, e_rev_E, e_rev_I)
threshold_type = ThresholdTypeStatic(n_neurons, v_thresh)
AbstractPopulationVertex.__init__(
self, n_neurons=n_neurons, binary="IF_cond_exp.aplx", label=label,
max_atoms_per_core=IFCondExp._model_based_max_atoms_per_core,
machine_time_step=machine_time_step,
timescale_factor=timescale_factor,
spikes_per_second=spikes_per_second,
ring_buffer_sigma=ring_buffer_sigma,
incoming_spike_buffer_size=incoming_spike_buffer_size,
model_name="IF_cond_exp", neuron_model=neuron_model,
input_type=input_type, synapse_type=synapse_type,
threshold_type=threshold_type, constraints=constraints)
def __init__(self,
n_neurons,
machine_time_step,
timescale_factor,
spikes_per_second=None,
ring_buffer_sigma=None,
incoming_spike_buffer_size=None,
constraints=None,
label=None,
tau_m=default_parameters['tau_m'],
cm=default_parameters['cm'],
v_rest=default_parameters['v_rest'],
v_reset=default_parameters['v_reset'],
v_thresh=default_parameters['v_thresh'],
tau_syn_E=default_parameters['tau_syn_E'],
tau_syn_I=default_parameters['tau_syn_I'],
tau_refrac=default_parameters['tau_refrac'],
i_offset=default_parameters['i_offset'],
v_init=None):
neuron_model = NeuronModelLeakyIntegrateAndFire(
n_neurons, machine_time_step, v_init, v_rest, tau_m, cm, i_offset,
v_reset, tau_refrac)
synapse_type = ExpSupervision(n_neurons, machine_time_step, tau_syn_E,
tau_syn_I)
input_type = InputTypeCurrent()
threshold_type = ThresholdTypeStatic(n_neurons, v_thresh)
# create additional inputs
additional_input = None
# instantiate the sPyNNaker system by initialising
# the AbstractPopulationVertex
AbstractPopulationVertex.__init__(
# standard inputs, do not need to change.
self,
n_neurons=n_neurons,
label=label,
machine_time_step=machine_time_step,
timescale_factor=timescale_factor,
spikes_per_second=spikes_per_second,
ring_buffer_sigma=ring_buffer_sigma,
incoming_spike_buffer_size=incoming_spike_buffer_size,
max_atoms_per_core=(
IFCurrExpSupervision._model_based_max_atoms_per_core),
# the various model types
neuron_model=neuron_model,
input_type=input_type,
synapse_type=synapse_type,
threshold_type=threshold_type,
additional_input=additional_input,
# the model a name (shown in reports)
model_name="IFCurrExpSupervision",
# the matching binary name
binary="if_curr_exp_supervision.aplx")
def __init__(self,
n_neurons,
machine_time_step,
timescale_factor,
spikes_per_second=None,
ring_buffer_sigma=None,
incoming_spike_buffer_size=None,
constraints=None,
label=None,
tau_m=default_parameters['tau_m'],
cm=default_parameters['cm'],
v_rest=default_parameters['v_rest'],
v_reset=default_parameters['v_reset'],
v_thresh=default_parameters['v_thresh'],
tau_syn_E=default_parameters['tau_syn_E'],
tau_syn_I=default_parameters['tau_syn_I'],
tau_refrac=default_parameters['tau_refrac'],
i_offset=default_parameters['i_offset'],
tau_ca2=default_parameters["tau_ca2"],
i_ca2=default_parameters["i_ca2"],
i_alpha=default_parameters["i_alpha"],
v_init=None):
neuron_model = NeuronModelLeakyIntegrateAndFire(
n_neurons, machine_time_step, v_init, v_rest, tau_m, cm, i_offset,
v_reset, tau_refrac)
synapse_type = SynapseTypeExponential(n_neurons, machine_time_step,
tau_syn_E, tau_syn_I)
input_type = InputTypeCurrent()
threshold_type = ThresholdTypeStatic(n_neurons, v_thresh)
additional_input = AdditionalInputCa2Adaptive(n_neurons,
machine_time_step,
tau_ca2, i_ca2, i_alpha)
AbstractPopulationVertex.__init__(
self,
n_neurons=n_neurons,
binary="IF_curr_exp_ca2_adaptive.aplx",
label=label,
max_atoms_per_core=IFCurrExpCa2Adaptive.
_model_based_max_atoms_per_core,
machine_time_step=machine_time_step,
timescale_factor=timescale_factor,
spikes_per_second=spikes_per_second,
ring_buffer_sigma=ring_buffer_sigma,
incoming_spike_buffer_size=incoming_spike_buffer_size,
model_name="IF_curr_exp_ca2_adaptive",
neuron_model=neuron_model,
input_type=input_type,
synapse_type=synapse_type,
threshold_type=threshold_type,
additional_input=additional_input,
constraints=constraints)
def __init__(self,
n_neurons,
spikes_per_second=AbstractPopulationVertex.
none_pynn_default_parameters['spikes_per_second'],
ring_buffer_sigma=AbstractPopulationVertex.
none_pynn_default_parameters['ring_buffer_sigma'],
incoming_spike_buffer_size=AbstractPopulationVertex.
none_pynn_default_parameters['incoming_spike_buffer_size'],
constraints=AbstractPopulationVertex.
none_pynn_default_parameters['constraints'],
label=AbstractPopulationVertex.
none_pynn_default_parameters['label'],
tau_m=default_parameters['tau_m'],
cm=default_parameters['cm'],
v_rest=default_parameters['v_rest'],
v_reset=default_parameters['v_reset'],
v_thresh=default_parameters['v_thresh'],
tau_syn_E=default_parameters['tau_syn_E'],
tau_syn_E2=default_parameters['tau_syn_E2'],
tau_syn_I=default_parameters['tau_syn_I'],
tau_refrac=default_parameters['tau_refrac'],
i_offset=default_parameters['i_offset'],
v_init=None,
isyn_exc=default_parameters['isyn_exc'],
isyn_inh=default_parameters['isyn_inh'],
isyn2_exc=default_parameters['isyn2_exc']):
neuron_model = NeuronModelLeakyIntegrateAndFire(
n_neurons, v_init, v_rest, tau_m, cm, i_offset, v_reset,
tau_refrac)
synapse_type = SynapseTypeDualExponential(n_neurons, tau_syn_E,
tau_syn_E2, tau_syn_I,
isyn_exc, isyn2_exc,
isyn_inh)
input_type = InputTypeCurrent()
threshold_type = ThresholdTypeStatic(n_neurons, v_thresh)
AbstractPopulationVertex.__init__(
self,
n_neurons=n_neurons,
binary="IF_curr_exp_dual.aplx",
label=label,
max_atoms_per_core=IFCurrDualExpBase.
_model_based_max_atoms_per_core,
spikes_per_second=spikes_per_second,
ring_buffer_sigma=ring_buffer_sigma,
incoming_spike_buffer_size=incoming_spike_buffer_size,
model_name="IF_curr_dual_exp",
neuron_model=neuron_model,
input_type=input_type,
synapse_type=synapse_type,
threshold_type=threshold_type,
constraints=constraints)
def __init__(self, n_neurons, machine_time_step, timescale_factor,
spikes_per_second=None, ring_buffer_sigma=None,
incoming_spike_buffer_size=None,
constraints=None, label=None,
tau_m=default_parameters['tau_m'],
cm=default_parameters['cm'],
v_rest=default_parameters['v_rest'],
v_reset=default_parameters['v_reset'],
v_thresh=default_parameters['v_thresh'],
tau_syn_E=default_parameters['tau_syn_E'],
tau_syn_I=default_parameters['tau_syn_I'],
tau_refrac=default_parameters['tau_refrac'],
i_offset=default_parameters['i_offset'],
v_init=None):
neuron_model = NeuronModelLeakyIntegrateAndFire(
n_neurons, machine_time_step, v_init, v_rest, tau_m, cm, i_offset,
v_reset, tau_refrac)
synapse_type = ExpSupervision(
n_neurons, machine_time_step, tau_syn_E, tau_syn_I)
input_type = InputTypeCurrent()
threshold_type = ThresholdTypeStatic(n_neurons, v_thresh)
# create additional inputs
additional_input = None
# instantiate the sPyNNaker system by initialising
# the AbstractPopulationVertex
AbstractPopulationVertex.__init__(
# standard inputs, do not need to change.
self, n_neurons=n_neurons, label=label,
machine_time_step=machine_time_step,
timescale_factor=timescale_factor,
spikes_per_second=spikes_per_second,
ring_buffer_sigma=ring_buffer_sigma,
incoming_spike_buffer_size=incoming_spike_buffer_size,
max_atoms_per_core=(
IFCurrExpSupervision._model_based_max_atoms_per_core),
# the various model types
neuron_model=neuron_model, input_type=input_type,
synapse_type=synapse_type, threshold_type=threshold_type,
additional_input=additional_input,
# the model a name (shown in reports)
model_name="IFCurrExpSupervision",
# the matching binary name
binary="if_curr_exp_supervision.aplx")
def __init__(self,
n_neurons,
spikes_per_second=AbstractPopulationVertex.
none_pynn_default_parameters['spikes_per_second'],
ring_buffer_sigma=AbstractPopulationVertex.
none_pynn_default_parameters['ring_buffer_sigma'],
incoming_spike_buffer_size=AbstractPopulationVertex.
none_pynn_default_parameters['incoming_spike_buffer_size'],
constraints=AbstractPopulationVertex.
none_pynn_default_parameters['constraints'],
label=AbstractPopulationVertex.
none_pynn_default_parameters['label'],
a=default_parameters['a'],
b=default_parameters['b'],
c=default_parameters['c'],
d=default_parameters['d'],
i_offset=default_parameters['i_offset'],
u_init=default_parameters['u_init'],
v_init=default_parameters['v_init'],
tau_syn_E=default_parameters['tau_syn_E'],
tau_syn_I=default_parameters['tau_syn_I'],
isyn_exc=default_parameters['isyn_exc'],
isyn_inh=default_parameters['isyn_inh']):
neuron_model = NeuronModelIzh(n_neurons, a, b, c, d, v_init, u_init,
i_offset)
synapse_type = SynapseTypeExponential(n_neurons, tau_syn_E, tau_syn_I,
isyn_exc, isyn_inh)
input_type = InputTypeCurrent()
threshold_type = ThresholdTypeStatic(n_neurons, _IZK_THRESHOLD)
AbstractPopulationVertex.__init__(
self,
n_neurons=n_neurons,
binary="IZK_curr_exp.aplx",
label=label,
max_atoms_per_core=IzkCurrExpBase._model_based_max_atoms_per_core,
spikes_per_second=spikes_per_second,
ring_buffer_sigma=ring_buffer_sigma,
incoming_spike_buffer_size=incoming_spike_buffer_size,
model_name="IZK_curr_exp",
neuron_model=neuron_model,
input_type=input_type,
synapse_type=synapse_type,
threshold_type=threshold_type,
constraints=constraints)
def __init__(
self,
n_neurons,
spikes_per_second=None,
ring_buffer_sigma=None,
incoming_spike_buffer_size=None,
constraints=None,
label=None,
# neuron model parameters
my_parameter=default_parameters['my_parameter'],
i_offset=default_parameters['i_offset'],
# threshold types parameters
v_thresh=default_parameters['v_thresh'],
# synapse type parameters
my_ex_synapse_parameter=default_parameters[
'my_ex_synapse_parameter'],
my_in_synapse_parameter=default_parameters[
'my_in_synapse_parameter'],
# state variables
v_init=None):
# create neuron model class
neuron_model = MyNeuronModel(n_neurons, i_offset, my_parameter)
# create synapse type model
synapse_type = MySynapseType(n_neurons, my_ex_synapse_parameter,
my_in_synapse_parameter)
# create input type model
input_type = InputTypeCurrent()
# create threshold type model
threshold_type = ThresholdTypeStatic(n_neurons, v_thresh)
# create additional inputs
additional_input = None
# instantiate the sPyNNaker system by initialising
# the AbstractPopulationVertex
AbstractPopulationVertex.__init__(
# standard inputs, do not need to change.
self,
n_neurons=n_neurons,
label=label,
spikes_per_second=spikes_per_second,
ring_buffer_sigma=ring_buffer_sigma,
incoming_spike_buffer_size=incoming_spike_buffer_size,
max_atoms_per_core=(
MyModelCurrMySynapseType._model_based_max_atoms_per_core),
# the various model types
neuron_model=neuron_model,
input_type=input_type,
synapse_type=synapse_type,
threshold_type=threshold_type,
additional_input=additional_input,
# the model a name (shown in reports)
model_name="MyModelMySynapseType",
# the matching binary name
binary="my_model_curr_my_synapse_type.aplx")
def __init__(
self, n_neurons, spikes_per_second=None, ring_buffer_sigma=None,
incoming_spike_buffer_size=None, constraints=None, label=None,
# neuron model parameters
my_parameter=default_parameters['my_parameter'],
i_offset=default_parameters['i_offset'],
# threshold types parameters
v_thresh=default_parameters['v_thresh'],
# synapse type parameters
tau_syn_E=default_parameters['tau_syn_E'],
tau_syn_I=default_parameters['tau_syn_I'],
# additional input parameter
my_additional_input_parameter=(
default_parameters['my_additional_input_parameter']),
# state variables
v_init=None):
# create neuron model class
neuron_model = MyNeuronModel(
n_neurons, i_offset, my_parameter)
# create synapse type model
synapse_type = SynapseTypeExponential(
n_neurons, tau_syn_E, tau_syn_I)
# create input type model
input_type = InputTypeCurrent()
# create threshold type model
threshold_type = ThresholdTypeStatic(
n_neurons, v_thresh)
# create additional inputs
additional_input = MyAdditionalInput(
n_neurons, my_additional_input_parameter)
# instantiate the sPyNNaker system by initialising
# the AbstractPopulationVertex
AbstractPopulationVertex.__init__(
# standard inputs, do not need to change.
self, n_neurons=n_neurons, label=label,
spikes_per_second=spikes_per_second,
ring_buffer_sigma=ring_buffer_sigma,
incoming_spike_buffer_size=incoming_spike_buffer_size,
max_atoms_per_core=(
MyModelCurrExpMyAdditionalInput.
_model_based_max_atoms_per_core),
# the various model types
neuron_model=neuron_model, input_type=input_type,
synapse_type=synapse_type, threshold_type=threshold_type,
additional_input=additional_input,
# the model a name (shown in reports)
model_name="MyModelCurrExpMyAdditionalInput",
# the matching binary name
binary="my_model_curr_exp_my_additional_input.aplx")
def __init__(
self,
n_neurons,
spikes_per_second=None,
ring_buffer_sigma=None,
incoming_spike_buffer_size=None,
constraints=None,
label=None,
# TODO: neuron model parameters (add / remove as required)
# neuron model parameters
my_parameter=default_parameters['my_parameter'],
i_offset=default_parameters['i_offset'],
# TODO: threshold types parameters (add / remove as required)
# threshold types parameters
v_thresh=default_parameters['v_thresh'],
# TODO: synapse type parameters (add /remove as required)
# synapse type parameters
tau_syn_E=default_parameters['tau_syn_E'],
tau_syn_I=default_parameters['tau_syn_I'],
# TODO: Optionally, you can add initial values for the state
# variables; this is not technically done in PyNN
v_init=None):
# TODO: create your neuron model class (change if required)
# create your neuron model class
neuron_model = MyNeuronModel(n_neurons, i_offset, my_parameter)
# TODO: create your synapse type model class (change if required)
# create your synapse type model
synapse_type = SynapseTypeExponential(n_neurons, tau_syn_E, tau_syn_I)
# TODO: create your input type model class (change if required)
# create your input type model
input_type = InputTypeCurrent()
# TODO: create your threshold type model class (change if required)
# create your threshold type model
threshold_type = ThresholdTypeStatic(n_neurons, v_thresh)
# TODO: create your own additional inputs (change if required).
# create your own additional inputs
additional_input = None
# instantiate the sPyNNaker system by initialising
# the AbstractPopulationVertex
AbstractPopulationVertex.__init__(
# standard inputs, do not need to change.
self,
n_neurons=n_neurons,
label=label,
spikes_per_second=spikes_per_second,
ring_buffer_sigma=ring_buffer_sigma,
incoming_spike_buffer_size=incoming_spike_buffer_size,
# TODO: Ensure the correct class is used below
max_atoms_per_core=MyModelCurrExp._model_based_max_atoms_per_core,
# These are the various model types
neuron_model=neuron_model,
input_type=input_type,
synapse_type=synapse_type,
threshold_type=threshold_type,
additional_input=additional_input,
# TODO: Give the model a name (shown in reports)
model_name="MyModelCurrExp",
# TODO: Set this to the matching binary name
binary="my_model_curr_exp.aplx")
def __init__(
self,
n_neurons,
machine_time_step,
timescale_factor,
spikes_per_second=None,
ring_buffer_sigma=None,
incoming_spike_buffer_size=None,
constraints=None,
label=None,
# neuron model parameters
primary=default_parameters['primary'],
# threshold types parameters
v_thresh=default_parameters['v_thresh'],
# initial values for the state values
v_init=None,
receive_port=None,
receive_tag=None,
board_address=None):
# create your neuron model class
neuron_model = SpindleModel(n_neurons, machine_time_step, primary)
# create your synapse type model
synapse_type = FusimotorActivation(
n_neurons, machine_time_step,
MuscleSpindle.default_parameters['a_syn_D'],
MuscleSpindle.default_parameters['tau_syn_D'],
MuscleSpindle.default_parameters['a_syn_S'],
MuscleSpindle.default_parameters['tau_syn_S'])
# create your input type model
input_type = InputTypeCurrent()
# create your threshold type model
threshold_type = ThresholdTypeStatic(n_neurons, v_thresh)
# create your own additional inputs
additional_input = None
# instantiate the sPyNNaker system by initialising
# the AbstractPopulationVertex
AbstractPopulationVertex.__init__(
# standard inputs, do not need to change.
self,
n_neurons=n_neurons,
label=label,
machine_time_step=machine_time_step,
timescale_factor=timescale_factor,
spikes_per_second=spikes_per_second,
ring_buffer_sigma=ring_buffer_sigma,
incoming_spike_buffer_size=incoming_spike_buffer_size,
# max units per core
max_atoms_per_core=MuscleSpindle._model_based_max_atoms_per_core,
# These are the various model types
neuron_model=neuron_model,
input_type=input_type,
synapse_type=synapse_type,
threshold_type=threshold_type,
additional_input=additional_input,
# model name (shown in reports)
model_name="MuscleSpindle",
# matching binary name
binary="muscle_spindle.aplx")
ReceiveBuffersToHostBasicImpl.__init__(self)
self.add_constraint(
TagAllocatorRequireReverseIptagConstraint(
receive_port,
constants.SDP_PORTS.INPUT_BUFFERING_SDP_PORT.value,
board_address, receive_tag))
def __init__(
self, n_neurons, spikes_per_second=None, ring_buffer_sigma=None,
incoming_spike_buffer_size=None, constraints=None, label=None,
# TODO: neuron model parameters (add / remove as required)
# neuron model parameters
my_parameter=default_parameters['my_parameter'],
i_offset=default_parameters['i_offset'],
# TODO: threshold types parameters (add / remove as required)
# threshold types parameters
v_thresh=default_parameters['v_thresh'],
# TODO: synapse type parameters (add /remove as required)
# synapse type parameters
tau_syn_E=default_parameters['tau_syn_E'],
tau_syn_I=default_parameters['tau_syn_I'],
# TODO: Optionally, you can add initial values for the state
# variables; this is not technically done in PyNN
v_init=None):
# TODO: create your neuron model class (change if required)
# create your neuron model class
neuron_model = MyNeuronModel(
n_neurons, i_offset, my_parameter)
# TODO: create your synapse type model class (change if required)
# create your synapse type model
synapse_type = SynapseTypeExponential(
n_neurons, tau_syn_E, tau_syn_I)
# TODO: create your input type model class (change if required)
# create your input type model
input_type = InputTypeCurrent()
# TODO: create your threshold type model class (change if required)
# create your threshold type model
threshold_type = ThresholdTypeStatic(n_neurons, v_thresh)
# TODO: create your own additional inputs (change if required).
# create your own additional inputs
additional_input = None
# instantiate the sPyNNaker system by initialising
# the AbstractPopulationVertex
AbstractPopulationVertex.__init__(
# standard inputs, do not need to change.
self, n_neurons=n_neurons, label=label,
spikes_per_second=spikes_per_second,
ring_buffer_sigma=ring_buffer_sigma,
incoming_spike_buffer_size=incoming_spike_buffer_size,
# TODO: Ensure the correct class is used below
max_atoms_per_core=MyModelCurrExp._model_based_max_atoms_per_core,
# These are the various model types
neuron_model=neuron_model, input_type=input_type,
synapse_type=synapse_type, threshold_type=threshold_type,
additional_input=additional_input,
# TODO: Give the model a name (shown in reports)
model_name="MyModelCurrExp",
# TODO: Set this to the matching binary name
binary="my_model_curr_exp.aplx")
def set_no_machine_time_steps(self, new_no_machine_time_steps):
AbstractPopulationVertex.set_no_machine_time_steps(
self, new_no_machine_time_steps * 2)
