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'],
# [default] Global model parameters
damping_factor=None, # required
damping_sum=None, # required
# [default] Model parameters
incoming_edges_count=None, # required
outgoing_edges_count=None, # required
# [none pynn] Initial values for the state variables
rank_init=None, # required
curr_rank_acc_init=none_pynn_default_parameters[
'curr_rank_acc_init'],
curr_rank_count_init=none_pynn_default_parameters[
'curr_rank_count_init'],
iter_state_init=none_pynn_default_parameters['iter_state_init']):
neuron_model = NeuronModelPageRank(
n_neurons,
damping_factor, damping_sum,
incoming_edges_count, outgoing_edges_count,
rank_init, curr_rank_acc_init, curr_rank_count_init, iter_state_init
)
input_type = InputTypeCurrent() # Used as a NoOp
synapse_type = SynapseTypeNoOp()
threshold_type = ThresholdTypeNoOp()
# instantiate sPyNNaker 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=PageRankBase._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,
model_name="PageRank", # name shown in reports
binary="page_rank.aplx" # C binary, defined in neuron/builds/<name>
)
def create_vertex(self, n_neurons, label, constraints, spikes_per_second,
ring_buffer_sigma, incoming_spike_buffer_size):
max_atoms = self.get_max_atoms_per_core()
return AbstractPopulationVertex(n_neurons, label, constraints,
max_atoms, spikes_per_second,
ring_buffer_sigma,
incoming_spike_buffer_size,
self.__model, self)
def __init__(self, n_neurons, neuron_model):
AbstractPopulationVertex.__init__(
self,
n_neurons=n_neurons,
binary=None,
label="Mock",
max_atoms_per_core=None,
spikes_per_second=self.
non_pynn_default_parameters['spikes_per_second'],
ring_buffer_sigma=self.
non_pynn_default_parameters['ring_buffer_sigma'],
incoming_spike_buffer_size=self.
non_pynn_default_parameters['incoming_spike_buffer_size'],
model_name="Mock",
neuron_model=neuron_model,
input_type=None,
synapse_type=None,
threshold_type=None)
def create_vertex(self, n_neurons, label, constraints, spikes_per_second,
ring_buffer_sigma, incoming_spike_buffer_size,
drop_late_spikes):
# pylint: disable=arguments-differ
max_atoms = self.get_max_atoms_per_core()
return AbstractPopulationVertex(n_neurons, label, constraints,
max_atoms, spikes_per_second,
ring_buffer_sigma,
incoming_spike_buffer_size,
self.__model, self, drop_late_spikes)
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_I=default_parameters['tau_syn_I'],
tau_refrac=default_parameters['tau_refrac'],
i_offset=default_parameters['i_offset'],
v_init=none_pynn_default_parameters['v_init'],
isyn_exc=default_parameters['isyn_exc'],
isyn_inh=default_parameters['isyn_inh']):
neuron_model = NeuronModelLeakyIntegrateAndFire(
n_neurons, v_init, v_rest, tau_m, cm, i_offset,
v_reset, tau_refrac)
synapse_type = SynapseTypeExponentialSupervision(
n_neurons, tau_syn_E, tau_syn_I, isyn_exc, isyn_inh)
input_type = InputTypeCurrent()
threshold_type = ThresholdTypeStatic(n_neurons, v_thresh)
AbstractPopulationVertex.__init__(
self, n_neurons=n_neurons, binary="IF_curr_exp_supervision.aplx", label=label,
max_atoms_per_core=IFCurrExpSupervision._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_exp_supervision", 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'],
# 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'],
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)
# 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")
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'],
# 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=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'],
# Global model parameters
damping_factor=default_parameters['damping_factor'],
damping_sum=default_parameters['damping_sum'],
# Model parameters
incoming_edges_count=default_parameters['incoming_edges_count'],
outgoing_edges_count=default_parameters['outgoing_edges_count'],
# Threshold types parameters
# Initial values for the state variables; this is not technically done in PyNN
rank_init=none_pynn_default_parameters['rank_init'],
curr_rank_acc_init=none_pynn_default_parameters[
'curr_rank_acc_init'],
curr_rank_count_init=none_pynn_default_parameters[
'curr_rank_count_init'],
iter_state_init=none_pynn_default_parameters['iter_state_init']):
neuron_model = NeuronModelPageRank(n_neurons, damping_factor,
damping_sum, incoming_edges_count,
outgoing_edges_count, rank_init,
curr_rank_acc_init,
curr_rank_count_init,
iter_state_init)
input_type = InputTypeCurrent()
synapse_type = SynapseTypeNoOp()
threshold_type = ThresholdTypeNoOp()
# 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=PageRankBase._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=None,
model_name="PageRank", # name shown in reports
binary="page_rank.aplx") # c src binary name
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'],
# threshold types parameters
threshold_value=default_parameters['threshold_value'],
prob_fire=default_parameters['prob_fire'],
seed=default_parameters['seed'],
# 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'],
# 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,
isyn_exc, isyn_inh)
# create input type model
input_type = InputTypeCurrent()
# create threshold type model
threshold_type = MyThresholdType(n_neurons, threshold_value, prob_fire,
seed)
# 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=(
MyModelCurrExpMyThresholdBase._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="MyModelCurrExpMyThreshold",
# the matching binary name
binary="my_model_curr_exp_my_threshold.aplx")
