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")
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'],
v_rest=default_parameters['v_rest'],
decay=default_parameters['decay'],
# 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'],
isyn_exc=default_parameters['isyn_exc'],
isyn_inh=default_parameters['isyn_inh'],
# 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, v_rest, decay)
# create synapse type model
synapse_type = SynapseTypeExponential(
n_neurons, tau_syn_E, tau_syn_I, isyn_exc, isyn_inh)
# 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=(
MyModelCurrExpMyAdditionalInputBase.
_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=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'],
# neuron model parameters
my_parameter=default_parameters['my_parameter'],
i_offset=default_parameters['i_offset'],
v_rest=default_parameters['v_rest'],
decay=default_parameters['decay'],
# 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'],
my_exc_init=default_parameters['my_exc_init'],
my_inh_init=default_parameters['my_inh_init'],
# state variables
v_init=initialize_parameters['v_init']):
# create neuron model class
neuron_model = MyNeuronModel(n_neurons, i_offset, my_parameter, v_rest,
decay)
# create synapse type model
synapse_type = MySynapseType(n_neurons, my_ex_synapse_parameter,
my_in_synapse_parameter, my_exc_init,
my_inh_init)
# 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=(
MyModelCurrMySynapseTypeBase._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")