spynnaker.pyNN.models.neuron.structural_plasticity.synaptogenesis.formation package

Module contents

class spynnaker.pyNN.models.neuron.structural_plasticity.synaptogenesis.formation.AbstractFormation

Bases: AbstractHasParameterNames

A synaptic connection formation rule.

abstractmethod get_parameters_sdram_usage_in_bytes() → int[source]
Returns:

The amount of SDRAM used by the parameters of this rule.

abstract property vertex_executable_suffix: str

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

abstractmethod write_parameters(spec: DataSpecificationBase) → None[source]

Write the parameters of the rule to the spec.

Parameters:

spec

class spynnaker.pyNN.models.neuron.structural_plasticity.synaptogenesis.formation.DistanceDependentFormation(grid: _Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | complex | bytes | str | _NestedSequence[complex | bytes | str] = (16, 16), p_form_forward: float = 0.16, sigma_form_forward: float = 2.5, p_form_lateral: float = 1.0, sigma_form_lateral: float = 1.0)

Bases: AbstractFormation

Formation rule that depends on the physical distance between neurons.

Parameters:

  • grid – (x, y) dimensions of the grid of distance

  • p_form_forward – The peak probability of formation on feed-forward connections

  • sigma_form_forward – The spread of probability with distance of formation on feed-forward connections

  • p_form_lateral – The peak probability of formation on lateral connections

  • sigma_form_lateral – The spread of probability with distance of formation on lateral connections

distance(x0: _Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | complex | bytes | str | _NestedSequence[complex | bytes | str], x1: _Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | complex | bytes | str | _NestedSequence[complex | bytes | str], metric: str) → ndarray[tuple[Any, ...], dtype[floating]][source]

Compute the distance between points x0 and x1 place on the grid using periodic boundary conditions.

Parameters:

  • x0 – first point in space

  • x1 – second point in space

  • metric – distance metric, i.e. euclidian or manhattan or equidistant

Returns:

the distance

generate_distance_probability_array(probability: float, sigma: float) → ndarray[tuple[Any, ...], dtype[uint16]][source]

Generate the exponentially decaying probability LUTs.

Parameters:

  • probability – peak probability

  • sigma – spread

Returns:

distance-dependent probabilities

get_parameter_names() → Iterable[str][source]
Returns:

The parameter names available from the component.

get_parameters_sdram_usage_in_bytes() → int[source]
Returns:

The amount of SDRAM used by the parameters of this rule.

property vertex_executable_suffix: str

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

write_parameters(spec: DataSpecificationBase) → None[source]

Write the parameters of the rule to the spec.

Parameters:

spec