spynnaker.pyNN.models.neuron.neuron_models package

Module contents

class spynnaker.pyNN.models.neuron.neuron_models.NeuronModel(structs: List[Struct], units: Dict[str, str])

Bases: AbstractStandardNeuronComponent

A component of a neuron that is the model of the model.

Parameters:

• structs – The structures of the component

• units – The units to use for each parameter

class spynnaker.pyNN.models.neuron.neuron_models.NeuronModelIFTrunc(v_init: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]] | None, tau_m: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]], cm: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]], i_offset: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]], v_reset: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]], tau_refrac: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]])

Bases: NeuronModel

Integrate and Fire without leak, and with truncation to V_reset should the membrane voltage ever go below it.

Parameters:

• v_init – \(V_{init}\)

• tau_m – \(\tau_{m}\)

• cm – \(C_m\)

• i_offset – \(I_{offset}\)

• v_reset – \(V_{reset}\)

• tau_refrac – \(\tau_{refrac}\)

add_parameters(parameters: RangeDictionary[float]) → None

Add the initial values of the parameters to the parameter holder.

Parameters:

parameters – A holder of the parameters

add_state_variables(state_variables: RangeDictionary[float]) → None

Add the initial values of the state variables to the state variables holder.

Parameters:

state_variables – A holder of the state variables

property cm: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]]

Settable model parameter: \(C_m\)

property i_offset: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]]

Settable model parameter: \(I_{offset}\)

property tau_m: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]]

Settable model parameter: \(\tau_{m}\)

property tau_refrac: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]]

Settable model parameter: \(\tau_{refrac}\)

property v_init: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]]

Settable model parameter: \(V_{init}\)

property v_reset: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]]

Settable model parameter: \(V_{reset}\)

class spynnaker.pyNN.models.neuron.neuron_models.NeuronModelIzh(a: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]], b: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]], c: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]], d: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]], v_init: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]], u_init: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]], i_offset: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]])

Bases: NeuronModel

Model of neuron due to Eugene M. Izhikevich et al.

Parameters:

• a – \(a\)

• b – \(b\)

• c – \(c\)

• d – \(d\)

• v_init – \(v_{init}\)

• u_init – \(u_{init}\)

• i_offset – \(I_{offset}\)

property a: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]]

Settable model parameter: \(a\)

add_parameters(parameters: RangeDictionary[float]) → None

Add the initial values of the parameters to the parameter holder.

Parameters:

parameters – A holder of the parameters

add_state_variables(state_variables: RangeDictionary[float]) → None

Add the initial values of the state variables to the state variables holder.

Parameters:

state_variables – A holder of the state variables

property b: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]]

Settable model parameter: \(b\)

property c: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]]

Settable model parameter: \(c\)

property d: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]]

Settable model parameter: \(d\)

property i_offset: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]]

Settable model parameter: \(I_{offset}\)

property u_init: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]]

Settable model parameter: \(u_{init}\)

property v_init: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]]

Settable model parameter: \(v_{init}\)

class spynnaker.pyNN.models.neuron.neuron_models.NeuronModelLeakyIntegrateAndFire(v_init: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]] | None, v_rest: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]], tau_m: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]], cm: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]], i_offset: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]], v_reset: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]], tau_refrac: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]])

Bases: NeuronModel

Classic leaky integrate and fire neuron model.

Parameters:

• v_init – \(V_{init}\)

• v_rest – \(V_{rest}\)

• tau_m – \(\tau_{m}\)

• cm – \(C_m\)

• i_offset – \(I_{offset}\)

• v_reset – \(V_{reset}\)

• tau_refrac – \(\tau_{refrac}\)

add_parameters(parameters: RangeDictionary[float]) → None

Add the initial values of the parameters to the parameter holder.

Parameters:

parameters – A holder of the parameters

add_state_variables(state_variables: RangeDictionary[float]) → None

Add the initial values of the state variables to the state variables holder.

Parameters:

state_variables – A holder of the state variables

property cm: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]]

Settable model parameter: \(C_m\)

property i_offset: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]]

Settable model parameter: \(I_{offset}\)

property tau_m: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]]

Settable model parameter: \(\tau_{m}\)

property tau_refrac: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]]

Settable model parameter: \(\tau_{refrac}\)

property v_init: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]]

Settable model parameter: \(V_{init}\)

property v_reset: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]]

Settable model parameter: \(V_{reset}\)

property v_rest: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]]

Settable model parameter: \(V_{rest}\)