spynnaker8.extra_models package

Module contents

spynnaker8.extra_models.IFCurDelta

alias of spynnaker.pyNN.models.neuron.builds.if_curr_delta.IFCurrDelta

class spynnaker8.extra_models.IFCurrExpCa2Adaptive(**kwargs)[source]

Bases: spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandard

Model from Liu, Y. H., & Wang, X. J. (2001). Spike-frequency adaptation of a generalized leaky integrate-and-fire model neuron. Journal of Computational Neuroscience, 10(1), 25-45. doi:10.1023/A:1008916026143

Parameters:
class spynnaker8.extra_models.IFCondExpStoc(**kwargs)[source]

Bases: spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandard

Leaky integrate and fire neuron with a stochastic threshold.

Habenschuss S, Jonke Z, Maass W. Stochastic computations in cortical microcircuit models. PLoS Computational Biology. 2013;9(11):e1003311. doi:10.1371/journal.pcbi.1003311

Parameters:
spynnaker8.extra_models.Izhikevich_cond

alias of spynnaker.pyNN.models.neuron.builds.izk_cond_exp_base.IzkCondExpBase

spynnaker8.extra_models.IF_curr_dual_exp

alias of spynnaker.pyNN.models.neuron.builds.if_curr_dual_exp_base.IFCurrDualExpBase

spynnaker8.extra_models.IF_curr_exp_sEMD

alias of spynnaker.pyNN.models.neuron.builds.if_curr_exp_semd_base.IFCurrExpSEMDBase

class spynnaker8.extra_models.WeightDependenceAdditiveTriplet(w_min=0.0, w_max=1.0, A3_plus=0.01, A3_minus=0.01)[source]

Bases: spynnaker.pyNN.models.neuron.plasticity.stdp.weight_dependence.abstract_has_a_plus_a_minus.AbstractHasAPlusAMinus, spynnaker.pyNN.models.neuron.plasticity.stdp.weight_dependence.abstract_weight_dependence.AbstractWeightDependence

An triplet-based additive weight dependence STDP rule.

Parameters:
  • w_min (float) – \(w^{min}\)
  • w_max (float) – \(w^{max}\)
  • A3_plus (float) – \(A_3^+\)
  • A3_minus (float) – \(A_3^-\)
A3_minus

\(A_3^-\)

Return type:float
A3_plus

\(A_3^+\)

Return type:float
default_parameters = {'A3_minus': 0.01, 'A3_plus': 0.01, 'w_max': 1.0, 'w_min': 0.0}
get_parameter_names()[source]

Returns the parameter names

Return type:iterable(str)
get_parameters_sdram_usage_in_bytes(n_synapse_types, n_weight_terms)[source]

Get the amount of SDRAM used by the parameters of this rule

Parameters:
  • n_synapse_types (int) –
  • n_weight_terms (int) –
Return type:

int

is_same_as(weight_dependence)[source]

Determine if this weight dependence is the same as another

Parameters:weight_dependence (AbstractWeightDependence) –
Return type:bool
vertex_executable_suffix

The suffix to be appended to the vertex executable for this rule

Return type:str
w_max

\(w^{max}\)

Return type:float
w_min

\(w^{min}\)

Return type:float
weight_maximum

The maximum weight that will ever be set in a synapse as a result of this rule

Return type:float
write_parameters(spec, weight_scales, n_weight_terms)[source]

Write the parameters of the rule to the spec

Parameters:
spynnaker8.extra_models.PfisterSpikeTriplet

alias of spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.timing_dependence_pfister_spike_triplet.TimingDependencePfisterSpikeTriplet

spynnaker8.extra_models.SpikeNearestPairRule

alias of spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.timing_dependence_spike_nearest_pair.TimingDependenceSpikeNearestPair

spynnaker8.extra_models.RecurrentRule

alias of spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.timing_dependence_recurrent.TimingDependenceRecurrent

spynnaker8.extra_models.Vogels2011Rule

alias of spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.timing_dependence_vogels_2011.TimingDependenceVogels2011

class spynnaker8.extra_models.SpikeSourcePoissonVariable(rates, starts, durations=None)[source]

Bases: spynnaker.pyNN.models.abstract_pynn_model.AbstractPyNNModel

create_vertex(n_neurons, label, constraints, seed, splitter)[source]

Create a vertex for a population of the model

Parameters:
  • n_neurons (int) – The number of neurons in the population
  • label (str) – The label to give to the vertex
  • constraints (list(AbstractConstraint) or None) – A list of constraints to give to the vertex, or None
Returns:

An application vertex for the population

Return type:

ApplicationVertex

default_population_parameters = {'seed': None, 'splitter': None}
classmethod get_max_atoms_per_core()[source]

Get the maximum number of atoms per core for this model

Return type:int
classmethod set_model_max_atoms_per_core(n_atoms=500)[source]

Set the maximum number of atoms per core for this model

Parameters:n_atoms (int or None) – The new maximum, or None for the largest possible