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:
  • tau_m (float) – \(\tau_m\)
  • cm (float) – \(C_m\)
  • v_rest (float) – \(V_{rest}\)
  • v_reset (float) – \(V_{reset}\)
  • v_thresh (float) – \(V_{thresh}\)
  • tau_syn_E (float) – \(\tau^{syn}_e\)
  • tau_syn_I (float) – \(\tau^{syn}_i\)
  • tau_refrac (float) – \(\tau_{refrac}\)
  • i_offset (float) – \(I_{offset}\)
  • tau_ca2 (float) – \(\tau_{\mathrm{Ca}^{+2}}\)
  • i_ca2 (float) – \(I_{\mathrm{Ca}^{+2}}\)
  • i_alpha (float) – \(\tau_\alpha\)
  • v (float) – \(V_{init}\)
  • isyn_exc (float) – \(I^{syn}_e\)
  • isyn_inh (float) – \(I^{syn}_i\)
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:
  • tau_m – \(\tau_m\)
  • cm – \(C_m\)
  • v_rest – \(V_{rest}\)
  • v_reset – \(V_{reset}\)
  • v_thresh – \(V_{thresh}\)
  • tau_syn_E – \(\tau^{syn}_e\)
  • tau_syn_I – \(\tau^{syn}_i\)
  • tau_refrac – \(\tau_{refrac}\)
  • i_offset – \(I_{offset}\)
  • e_rev_E – \(E^{rev}_e\)
  • e_rev_I – \(E^{rev}_i\)
  • du_th – \(du_{thresh}\)
  • tau_th – \(\tau_{thresh}\)
  • v – \(V_{init}\)
  • isyn_exc – \(I^{syn}_e\)
  • isyn_inh – \(I^{syn}_i\)
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.weight_dependence_additive_triplet.WeightDependenceAdditiveTriplet

Parameters:
  • w_min (float) – \(w_\mathrm{min}\)
  • w_max (float) – \(w_\mathrm{max}\)
  • A3_plus (float) – \(A_3^+\)
  • A3_minus (float) – \(A_3^-\)
spynnaker8.extra_models.PfisterSpikeTriplet

alias of spynnaker8.models.synapse_dynamics.timing_dependence.timing_dependence_pfister_spike_triplet.TimingDependencePfisterSpikeTriplet

spynnaker8.extra_models.SpikeNearestPairRule

alias of spynnaker8.models.synapse_dynamics.timing_dependence.timing_dependence_spike_nearest_pair.TimingDependenceSpikeNearestPair

spynnaker8.extra_models.RecurrentRule

alias of spynnaker8.models.synapse_dynamics.timing_dependence.timing_dependence_recurrent.TimingDependenceRecurrent

spynnaker8.extra_models.Vogels2011Rule

alias of spynnaker8.models.synapse_dynamics.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)[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}
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