def __init__(self,
tau_m=20.0,
cm=1.0,
v_rest=-65.0,
v_reset=-65.0,
v_thresh=-50.0,
tau_syn_E=5.0,
tau_syn_I=5.0,
tau_refrac=0.1,
i_offset=0.0,
e_rev_E=0.0,
e_rev_I=-70.0,
du_th=0.5,
tau_th=20.0,
v=-65.0,
isyn_exc=0.0,
isyn_inh=0.0):
# pylint: disable=too-many-arguments, too-many-locals
neuron_model = NeuronModelLeakyIntegrateAndFire(
v, v_rest, tau_m, cm, i_offset, v_reset, tau_refrac)
synapse_type = SynapseTypeExponential(tau_syn_E, tau_syn_I, isyn_exc,
isyn_inh)
input_type = InputTypeConductance(e_rev_E, e_rev_I)
threshold_type = ThresholdTypeMaassStochastic(du_th, tau_th, v_thresh)
super(IFCondExpStoc, self).__init__(model_name="IF_cond_exp_stoc",
binary="IF_cond_exp_stoc.aplx",
neuron_model=neuron_model,
input_type=input_type,
synapse_type=synapse_type,
threshold_type=threshold_type)
def __init__(self,
a=0.02,
b=0.2,
c=-65.0,
d=2.0,
i_offset=0.0,
u=-14.0,
v=-70.0,
tau_syn_E=5.0,
tau_syn_I=5.0,
e_rev_E=0.0,
e_rev_I=-70.0,
isyn_exc=0.0,
isyn_inh=0.0):
# pylint: disable=too-many-arguments, too-many-locals
neuron_model = NeuronModelIzh(a, b, c, d, v, u, i_offset)
synapse_type = SynapseTypeExponential(tau_syn_E, tau_syn_I, isyn_exc,
isyn_inh)
input_type = InputTypeConductance(e_rev_E, e_rev_I)
threshold_type = ThresholdTypeStatic(_IZK_THRESHOLD)
super().__init__(model_name="IZK_cond_exp",
binary="IZK_cond_exp.aplx",
neuron_model=neuron_model,
input_type=input_type,
synapse_type=synapse_type,
threshold_type=threshold_type)
def __init__(self,
n_neurons,
spikes_per_second=AbstractPopulationVertex.
non_pynn_default_parameters['spikes_per_second'],
ring_buffer_sigma=AbstractPopulationVertex.
non_pynn_default_parameters['ring_buffer_sigma'],
incoming_spike_buffer_size=AbstractPopulationVertex.
non_pynn_default_parameters['incoming_spike_buffer_size'],
constraints=AbstractPopulationVertex.
non_pynn_default_parameters['constraints'],
label=AbstractPopulationVertex.
non_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_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'],
du_th=default_parameters['du_th'],
tau_th=default_parameters['tau_th'],
v_init=initialize_parameters['v_init'],
isyn_exc=default_parameters['isyn_exc'],
isyn_inh=default_parameters['isyn_inh']):
# pylint: disable=too-many-arguments, too-many-locals
neuron_model = NeuronModelLeakyIntegrateAndFire(
n_neurons, v_init, v_rest, tau_m, cm, i_offset, v_reset,
tau_refrac)
synapse_type = SynapseTypeExponential(n_neurons,
tau_syn_E,
tau_syn_I,
initial_input_exc=isyn_exc,
initial_input_inh=isyn_inh)
input_type = InputTypeConductance(n_neurons, e_rev_E, e_rev_I)
threshold_type = ThresholdTypeMaassStochastic(n_neurons, du_th, tau_th,
v_thresh)
super(IFCondExpStoc, self).__init__(
n_neurons=n_neurons,
binary="IF_cond_exp_stoc.aplx",
label=label,
max_atoms_per_core=IFCondExpStoc._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_cond_exp_stoc",
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=_apv_defs['spikes_per_second'],
ring_buffer_sigma=_apv_defs['ring_buffer_sigma'],
incoming_spike_buffer_size=_apv_defs['incoming_spike_buffer_size'],
constraints=_apv_defs['constraints'],
label=_apv_defs['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=initialize_parameters['u_init'],
v_init=initialize_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'],
isyn_exc=default_parameters['isyn_exc'],
isyn_inh=default_parameters['isyn_inh']):
# pylint: disable=too-many-arguments, too-many-locals
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 = InputTypeConductance(n_neurons, e_rev_E, e_rev_I)
threshold_type = ThresholdTypeStatic(n_neurons, _IZK_THRESHOLD)
super(IzkCondExpBase, self).__init__(
n_neurons=n_neurons,
binary="IZK_cond_exp.aplx",
label=label,
max_atoms_per_core=IzkCondExpBase._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_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,
spikes_per_second=AbstractPopulationVertex.
non_pynn_default_parameters['spikes_per_second'],
ring_buffer_sigma=AbstractPopulationVertex.
non_pynn_default_parameters['ring_buffer_sigma'],
incoming_spike_buffer_size=AbstractPopulationVertex.
non_pynn_default_parameters['incoming_spike_buffer_size'],
constraints=AbstractPopulationVertex.
non_pynn_default_parameters['constraints'],
label=AbstractPopulationVertex.non_pynn_default_parameters['label'],
# 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'],
isyn_exc=default_parameters['isyn_exc'],
isyn_inh=default_parameters['isyn_inh'],
# Add input type parameters for conductance
e_rev_E=default_parameters['e_rev_E'],
e_rev_I=default_parameters['e_rev_I'],
# TODO: Optionally, you can add initial values for the state
# variables; this is not technically done in PyNN
v_init=initialize_parameters['v_init']):
# 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,
isyn_exc, isyn_inh)
# TODO: create your input type model class (change if required)
# create your input type model
input_type = InputTypeConductance(n_neurons, e_rev_E, e_rev_I)
# 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=(
MyModelCondExpBase._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="MyModelCondExpBase",
# TODO: Set this to the matching binary name
binary="my_model_cond_exp.aplx")