spynnaker.pyNN.models.neuron.builds package

Module contents

These are particular configurations of neuron model components. Each of them is either supported or explicitly not supported.

Others are possible but might require an unknown amount of work to get running on SpiNNaker.

class spynnaker.pyNN.models.neuron.builds.EIFConductanceAlphaPopulation(**kwargs: Any)

Bases: AbstractProvidesDefaults

Exponential integrate and fire neuron with spike triggered and sub-threshold adaptation currents (isfa, ista reps.)

Warning

Not currently supported by the tool chain.

class spynnaker.pyNN.models.neuron.builds.HHCondExp(**kwargs: Any)

Bases: AbstractProvidesDefaults

Single-compartment Hodgkin-Huxley model with exponentially decaying current input.

Warning

Not currently supported by the tool chain.

class spynnaker.pyNN.models.neuron.builds.IFCondAlpha(**kwargs: Any)

Bases: AbstractProvidesDefaults

Leaky integrate and fire neuron with an alpha-shaped current input.

Warning

Not currently supported by the tool chain.

class spynnaker.pyNN.models.neuron.builds.IFCondExpBase(**kwargs: Any)

Bases: AbstractPyNNNeuronModelStandard

Leaky integrate and fire neuron with an exponentially decaying conductance input.

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 – \(E^{rev}_i\)

  • v – \(V_{init}\)

  • isyn_exc – \(I^{syn}_e\)

  • isyn_inh – \(I^{syn}_i\)

  • model_name – Name of the model.

  • binary – Name of the implementation executable.

  • neuron_model – The model of the neuron body

  • input_type – The model of synaptic input types

  • synapse_type – The model of the synapses’ dynamics

  • threshold_type – The model of the firing threshold

  • additional_input_type – The model (if any) of additional environmental inputs

class spynnaker.pyNN.models.neuron.builds.IFCondExpStoc(**kwargs: Any)

Bases: 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:

  • model_name – Name of the model.

  • binary – Name of the implementation executable.

  • neuron_model – The model of the neuron body

  • input_type – The model of synaptic input types

  • synapse_type – The model of the synapses’ dynamics

  • threshold_type – The model of the firing threshold

  • additional_input_type – The model (if any) of additional environmental inputs

class spynnaker.pyNN.models.neuron.builds.IFCurrAlpha(**kwargs: Any)

Bases: AbstractPyNNNeuronModelStandard

Leaky integrate and fire neuron with an alpha-shaped current-based input.

Parameters:

  • model_name – Name of the model.

  • binary – Name of the implementation executable.

  • neuron_model – The model of the neuron body

  • input_type – The model of synaptic input types

  • synapse_type – The model of the synapses’ dynamics

  • threshold_type – The model of the firing threshold

  • additional_input_type – The model (if any) of additional environmental inputs

class spynnaker.pyNN.models.neuron.builds.IFCurrDelta(**kwargs: Any)

Bases: AbstractPyNNNeuronModelStandard

Leaky integrate and fire neuron with an instantaneous current input.

Parameters:

  • model_name – Name of the model.

  • binary – Name of the implementation executable.

  • neuron_model – The model of the neuron body

  • input_type – The model of synaptic input types

  • synapse_type – The model of the synapses’ dynamics

  • threshold_type – The model of the firing threshold

  • additional_input_type – The model (if any) of additional environmental inputs

class spynnaker.pyNN.models.neuron.builds.IFCurrDeltaCa2Adaptive(**kwargs: Any)

Bases: AbstractPyNNNeuronModelStandard

Leaky integrate and fire neuron with an instantaneous current input.

Parameters:

  • model_name – Name of the model.

  • binary – Name of the implementation executable.

  • neuron_model – The model of the neuron body

  • input_type – The model of synaptic input types

  • synapse_type – The model of the synapses’ dynamics

  • threshold_type – The model of the firing threshold

  • additional_input_type – The model (if any) of additional environmental inputs

class spynnaker.pyNN.models.neuron.builds.IFCurrDeltaFixedProb(**kwargs: Any)

Bases: AbstractPyNNNeuronModelStandard

Leaky integrate and fire neuron with an instantaneous current input, and fixed probability of spiking once a threshold is reached.

Parameters:

  • model_name – Name of the model.

  • binary – Name of the implementation executable.

  • neuron_model – The model of the neuron body

  • input_type – The model of synaptic input types

  • synapse_type – The model of the synapses’ dynamics

  • threshold_type – The model of the firing threshold

  • additional_input_type – The model (if any) of additional environmental inputs

class spynnaker.pyNN.models.neuron.builds.IFCurrDualExpBase(**kwargs: Any)

Bases: AbstractPyNNNeuronModelStandard

Leaky integrate and fire neuron with two exponentially decaying excitatory current inputs, and one exponentially decaying inhibitory current input.

Parameters:

  • model_name – Name of the model.

  • binary – Name of the implementation executable.

  • neuron_model – The model of the neuron body

  • input_type – The model of synaptic input types

  • synapse_type – The model of the synapses’ dynamics

  • threshold_type – The model of the firing threshold

  • additional_input_type – The model (if any) of additional environmental inputs

class spynnaker.pyNN.models.neuron.builds.IFCurrExpBase(**kwargs: Any)

Bases: AbstractPyNNNeuronModelStandard

Leaky integrate and fire neuron with an exponentially decaying current input.

Parameters:

  • model_name – Name of the model.

  • binary – Name of the implementation executable.

  • neuron_model – The model of the neuron body

  • input_type – The model of synaptic input types

  • synapse_type – The model of the synapses’ dynamics

  • threshold_type – The model of the firing threshold

  • additional_input_type – The model (if any) of additional environmental inputs

class spynnaker.pyNN.models.neuron.builds.IFCurrExpCa2Adaptive(**kwargs: Any)

Bases: 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:

  • model_name – Name of the model.

  • binary – Name of the implementation executable.

  • neuron_model – The model of the neuron body

  • input_type – The model of synaptic input types

  • synapse_type – The model of the synapses’ dynamics

  • threshold_type – The model of the firing threshold

  • additional_input_type – The model (if any) of additional environmental inputs

class spynnaker.pyNN.models.neuron.builds.IFCurrExpSEMDBase(**kwargs: Any)

Bases: AbstractPyNNNeuronModelStandard

Leaky integrate and fire neuron with an exponentially decaying current input, where the excitatory input depends upon the inhibitory input (see https://www.cit-ec.de/en/nbs/spiking-insect-vision)

Parameters:

  • model_name – Name of the model.

  • binary – Name of the implementation executable.

  • neuron_model – The model of the neuron body

  • input_type – The model of synaptic input types

  • synapse_type – The model of the synapses’ dynamics

  • threshold_type – The model of the firing threshold

  • additional_input_type – The model (if any) of additional environmental inputs

class spynnaker.pyNN.models.neuron.builds.IFFacetsConductancePopulation(**kwargs: Any)

Bases: AbstractProvidesDefaults

Leaky integrate and fire neuron with conductance-based synapses and fixed threshold as it is resembled by the FACETS Hardware Stage 1.

Warning

Not currently supported by the tool chain.

class spynnaker.pyNN.models.neuron.builds.IFTruncDelta(**kwargs: Any)

Bases: AbstractPyNNNeuronModelStandard

Non-leaky Integrate and fire neuron with an instantaneous current input, and truncation of membrane voltage so that it never goes below V_reset.

Parameters:

  • model_name – Name of the model.

  • binary – Name of the implementation executable.

  • neuron_model – The model of the neuron body

  • input_type – The model of synaptic input types

  • synapse_type – The model of the synapses’ dynamics

  • threshold_type – The model of the firing threshold

  • additional_input_type – The model (if any) of additional environmental inputs

class spynnaker.pyNN.models.neuron.builds.IzkCondDualExpBase(**kwargs: Any)

Bases: AbstractPyNNNeuronModelStandard

Izhikevich neuron model with conductance inputs and dual synapse.

Parameters:

  • model_name – Name of the model.

  • binary – Name of the implementation executable.

  • neuron_model – The model of the neuron body

  • input_type – The model of synaptic input types

  • synapse_type – The model of the synapses’ dynamics

  • threshold_type – The model of the firing threshold

  • additional_input_type – The model (if any) of additional environmental inputs

class spynnaker.pyNN.models.neuron.builds.IzkCondExpBase(**kwargs: Any)

Bases: AbstractPyNNNeuronModelStandard

Izhikevich neuron model with conductance inputs.

Parameters:

  • model_name – Name of the model.

  • binary – Name of the implementation executable.

  • neuron_model – The model of the neuron body

  • input_type – The model of synaptic input types

  • synapse_type – The model of the synapses’ dynamics

  • threshold_type – The model of the firing threshold

  • additional_input_type – The model (if any) of additional environmental inputs

class spynnaker.pyNN.models.neuron.builds.IzkCurrExpBase(**kwargs: Any)

Bases: AbstractPyNNNeuronModelStandard

Izhikevich neuron model with current inputs.

Parameters:

  • model_name – Name of the model.

  • binary – Name of the implementation executable.

  • neuron_model – The model of the neuron body

  • input_type – The model of synaptic input types

  • synapse_type – The model of the synapses’ dynamics

  • threshold_type – The model of the firing threshold

  • additional_input_type – The model (if any) of additional environmental inputs

class spynnaker.pyNN.models.neuron.builds.StocExp(**kwargs: Any)

Bases: AbstractPyNNNeuronModel

Stochastic neuron model exponential threshold and instantaneous synapses, and voltage which is reset each time step.

Parameters:

model – The model implementation

class spynnaker.pyNN.models.neuron.builds.StocExpStable(**kwargs: Any)

Bases: AbstractPyNNNeuronModel

Stochastic neuron model with exponential threshold and instantaneous synapses, and voltage stays unless changed by input.

Parameters:

model – The model implementation

class spynnaker.pyNN.models.neuron.builds.StocSigma(**kwargs: Any)

Bases: AbstractPyNNNeuronModel

Stochastic model with sigma threshold and instantaneous synapses.

Parameters:

model – The model implementation