spynnaker.pyNN.models.neuron.implementations package

Module contents

class spynnaker.pyNN.models.neuron.implementations.AbstractNeuronImpl

Bases: object

An abstraction of a whole neuron model including all parts.

abstractmethod add_parameters(parameters: RangeDictionary) → None[source]

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

Parameters:

parameters – A holder of the parameters

abstractmethod add_state_variables(state_variables: RangeDictionary) → None[source]

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

Parameters:

state_variables – A holder of the state variables

abstract property binary_name: str

The name of the binary executable of this implementation.

abstractmethod get_global_weight_scale() → float[source]
Returns:

The weight scaling required by this model.

abstractmethod get_n_synapse_types() → int[source]
Returns:

The number of synapse types supported by the model.

abstractmethod get_recordable_data_types() → Mapping[str, DataType][source]

Get the data type of the variables that can be recorded.

Returns:

dictionary of name of variable to data type of variable

abstractmethod get_recordable_units(variable: str) → str[source]

Get the units of the given variable that can be recorded.

Parameters:

variable – The name of the variable

Returns:

The unit or this variable. For example ‘mV’ or ‘uS’. Will be an empty string for things like spikes and probability

abstractmethod get_recordable_variable_index(variable: str) → int[source]
Parameters:

variable – The name of the variable

Returns:

The index of the variable in the list of variables that can be recorded.

abstractmethod get_recordable_variables() → Sequence[str][source]
Returns:

The names of the variables that can be recorded in this model.

abstractmethod get_synapse_id_by_target(target: str) → int | None[source]
Parameters:

target – The name of the synapse

Returns:

The ID of a synapse given the name.

abstractmethod get_synapse_targets() → Sequence[str][source]
Returns:

The target names of the synapse type.

abstractmethod get_units(variable: str) → str[source]
Parameters:

variable – The name of the variable

Returns:

The units of the given variable.

abstract property is_conductance_based: bool

Whether the model uses conductance.

abstractmethod is_recordable(variable: str) → bool[source]

Determine if the given variable can be recorded.

Parameters:

variable – The name of the variable

Returns:

True if the variable is recorded, False otherwise

abstract property model_name: str

The name of the model.

abstract property structs: Sequence[Struct]

A list of structures used by the implementation.

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

Bases: object

Represents a component of a standard neural model.

Parameters:

  • structs – The structures of the component

  • units – The units to use for each parameter

abstractmethod add_parameters(parameters: RangeDictionary[float]) → None[source]

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

Parameters:

parameters – A holder of the parameters

abstractmethod add_state_variables(state_variables: RangeDictionary[float]) → None[source]

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

Parameters:

state_variables – A holder of the state variables

get_units(variable: str) → str[source]
Parameters:

variable – The name of the variable

Returns:

The units of the given variable.

has_variable(variable: str) → bool[source]

Determine if this component has a variable by the given name.

Parameters:

variable – The name of the variable

Returns:

True if the variable exists, False otherwise

property structs: List[Struct]

The structures of the component. If there are multiple structures, the order is how they will appear in memory; where there are structures that repeat per neuron the repeats will appear adjacent e.g. for non-repeating structure g, followed by repeating structures s1 and s2 with 3 neurons the layout will be: [g, s1, s1, s1, s2, s2, s2].

class spynnaker.pyNN.models.neuron.implementations.NeuronImplStandard(model_name: str, binary: str, neuron_model: NeuronModel, input_type: AbstractInputType, synapse_type: AbstractSynapseType, threshold_type: AbstractThresholdType, additional_input_type: AbstractAdditionalInput | None = None)

Bases: AbstractNeuronImpl

The standard componentised neuron implementation.

Parameters:

  • model_name

  • binary

  • neuron_model

  • input_type

  • synapse_type

  • threshold_type

  • additional_input_type

add_parameters(parameters: RangeDictionary) → None[source]

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

Parameters:

parameters – A holder of the parameters

add_state_variables(state_variables: RangeDictionary) → None[source]

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

Parameters:

state_variables – A holder of the state variables

property binary_name: str

The name of the binary executable of this implementation.

get_global_weight_scale() → float[source]
Returns:

The weight scaling required by this model.

get_n_synapse_types() → int[source]
Returns:

The number of synapse types supported by the model.

get_recordable_data_types() → Mapping[str, DataType][source]

Get the data type of the variables that can be recorded.

Returns:

dictionary of name of variable to data type of variable

get_recordable_units(variable: str) → str[source]

Get the units of the given variable that can be recorded.

Parameters:

variable – The name of the variable

Returns:

The unit or this variable. For example ‘mV’ or ‘uS’. Will be an empty string for things like spikes and probability

get_recordable_variable_index(variable: str) → int[source]
Parameters:

variable – The name of the variable

Returns:

The index of the variable in the list of variables that can be recorded.

get_recordable_variables() → Sequence[str][source]
Returns:

The names of the variables that can be recorded in this model.

get_synapse_id_by_target(target: str) → int | None[source]
Parameters:

target – The name of the synapse

Returns:

The ID of a synapse given the name.

get_synapse_targets() → Sequence[str][source]
Returns:

The target names of the synapse type.

get_units(variable: str) → str[source]
Parameters:

variable – The name of the variable

Returns:

The units of the given variable.

property is_conductance_based: bool

Whether the model uses conductance.

is_recordable(variable: str) → bool[source]

Determine if the given variable can be recorded.

Parameters:

variable – The name of the variable

Returns:

True if the variable is recorded, False otherwise

property model_name: str

The name of the model.

property n_steps_per_timestep: int

Get the last set n steps per timestep

property structs: Sequence[Struct]

A list of structures used by the implementation.

class spynnaker.pyNN.models.neuron.implementations.NeuronImplStocExp(tau: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]], bias: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]], refract_init: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]], seed: int | None)

Bases: AbstractNeuronImpl

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

Parameters:

  • tau – \(\tau_m\)

  • bias

  • refract_init

  • seed – Seed for the random distribution

add_parameters(parameters: RangeDictionary) → None[source]

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

Parameters:

parameters – A holder of the parameters

add_state_variables(state_variables: RangeDictionary) → None[source]

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

Parameters:

state_variables – A holder of the state variables

property binary_name: str

The name of the binary executable of this implementation.

get_global_weight_scale() → float[source]
Returns:

The weight scaling required by this model.

get_n_synapse_types() → int[source]
Returns:

The number of synapse types supported by the model.

get_recordable_data_types() → Mapping[str, DataType][source]

Get the data type of the variables that can be recorded.

Returns:

dictionary of name of variable to data type of variable

get_recordable_units(variable: str) → str[source]

Get the units of the given variable that can be recorded.

Parameters:

variable – The name of the variable

Returns:

The unit or this variable. For example ‘mV’ or ‘uS’. Will be an empty string for things like spikes and probability

get_recordable_variable_index(variable: str) → int[source]
Parameters:

variable – The name of the variable

Returns:

The index of the variable in the list of variables that can be recorded.

get_recordable_variables() → Sequence[str][source]
Returns:

The names of the variables that can be recorded in this model.

get_synapse_id_by_target(target: str) → int | None[source]
Parameters:

target – The name of the synapse

Returns:

The ID of a synapse given the name.

get_synapse_targets() → Sequence[str][source]
Returns:

The target names of the synapse type.

get_units(variable: str) → str[source]
Parameters:

variable – The name of the variable

Returns:

The units of the given variable.

property is_conductance_based: bool

Whether the model uses conductance.

is_recordable(variable: str) → bool[source]

Determine if the given variable can be recorded.

Parameters:

variable – The name of the variable

Returns:

True if the variable is recorded, False otherwise

property model_name: str

The name of the model.

property structs: Sequence[Struct]

A list of structures used by the implementation.

class spynnaker.pyNN.models.neuron.implementations.NeuronImplStocExpStable(v_init: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]], v_reset: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]], tau: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]], tau_refrac: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]], bias: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]], refract_init: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]], seed: int | None)

Bases: AbstractNeuronImpl

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

Parameters:

  • v_init – \(V_{init}\)

  • v_reset – \(V_{thresh}\)

  • tau

  • tau_refrac

  • bias

  • refract_init

  • seed – Seed for the random distribution

add_parameters(parameters: RangeDictionary) → None[source]

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

Parameters:

parameters – A holder of the parameters

add_state_variables(state_variables: RangeDictionary) → None[source]

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

Parameters:

state_variables – A holder of the state variables

property binary_name: str

The name of the binary executable of this implementation.

get_global_weight_scale() → float[source]
Returns:

The weight scaling required by this model.

get_n_synapse_types() → int[source]
Returns:

The number of synapse types supported by the model.

get_recordable_data_types() → Mapping[str, DataType][source]

Get the data type of the variables that can be recorded.

Returns:

dictionary of name of variable to data type of variable

get_recordable_units(variable: str) → str[source]

Get the units of the given variable that can be recorded.

Parameters:

variable – The name of the variable

Returns:

The unit or this variable. For example ‘mV’ or ‘uS’. Will be an empty string for things like spikes and probability

get_recordable_variable_index(variable: str) → int[source]
Parameters:

variable – The name of the variable

Returns:

The index of the variable in the list of variables that can be recorded.

get_recordable_variables() → Sequence[str][source]
Returns:

The names of the variables that can be recorded in this model.

get_synapse_id_by_target(target: str) → int | None[source]
Parameters:

target – The name of the synapse

Returns:

The ID of a synapse given the name.

get_synapse_targets() → Sequence[str][source]
Returns:

The target names of the synapse type.

get_units(variable: str) → str[source]
Parameters:

variable – The name of the variable

Returns:

The units of the given variable.

property is_conductance_based: bool

Whether the model uses conductance.

is_recordable(variable: str) → bool[source]

Determine if the given variable can be recorded.

Parameters:

variable – The name of the variable

Returns:

True if the variable is recorded, False otherwise

property model_name: str

The name of the model.

property structs: Sequence[Struct]

A list of structures used by the implementation.

class spynnaker.pyNN.models.neuron.implementations.NeuronImplStocSigma(tau_refrac: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]], alpha: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]], bias: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]], refract_init: float | Iterable[float] | RandomDistribution | ndarray[tuple[Any, ...], dtype[floating]], seed: int | None)

Bases: AbstractNeuronImpl

Stochastic model with sigma threshold and instantaneous synapses.

Parameters:

  • tau_refrac

  • alpha

  • bias

  • refract_init

  • seed – Seed for the random distribution

add_parameters(parameters: RangeDictionary) → None[source]

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

Parameters:

parameters – A holder of the parameters

add_state_variables(state_variables: RangeDictionary) → None[source]

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

Parameters:

state_variables – A holder of the state variables

property binary_name: str

The name of the binary executable of this implementation.

get_global_weight_scale() → float[source]
Returns:

The weight scaling required by this model.

get_n_synapse_types() → int[source]
Returns:

The number of synapse types supported by the model.

get_recordable_data_types() → Mapping[str, DataType][source]

Get the data type of the variables that can be recorded.

Returns:

dictionary of name of variable to data type of variable

get_recordable_units(variable: str) → str[source]

Get the units of the given variable that can be recorded.

Parameters:

variable – The name of the variable

Returns:

The unit or this variable. For example ‘mV’ or ‘uS’. Will be an empty string for things like spikes and probability

get_recordable_variable_index(variable: str) → int[source]
Parameters:

variable – The name of the variable

Returns:

The index of the variable in the list of variables that can be recorded.

get_recordable_variables() → Sequence[str][source]
Returns:

The names of the variables that can be recorded in this model.

get_synapse_id_by_target(target: str) → int | None[source]
Parameters:

target – The name of the synapse

Returns:

The ID of a synapse given the name.

get_synapse_targets() → Sequence[str][source]
Returns:

The target names of the synapse type.

get_units(variable: str) → str[source]
Parameters:

variable – The name of the variable

Returns:

The units of the given variable.

property is_conductance_based: bool

Whether the model uses conductance.

is_recordable(variable: str) → bool[source]

Determine if the given variable can be recorded.

Parameters:

variable – The name of the variable

Returns:

True if the variable is recorded, False otherwise

property model_name: str

The name of the model.

property structs: Sequence[Struct]

A list of structures used by the implementation.