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

Module contents

class spynnaker.pyNN.models.neuron.structural_plasticity.synaptogenesis.formation.AbstractFormation[source]

Bases: object

A formation rule

get_parameter_names()[source]

Return the names of the parameters supported by this rule

Return type:iterable(str)
get_parameters_sdram_usage_in_bytes()[source]

Get the amount of SDRAM used by the parameters of this rule

Return type:int
vertex_executable_suffix

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

Return type:str
write_parameters(spec)[source]

Write the parameters of the rule to the spec

Parameters:spec (DataSpecificationGenerator) –
class spynnaker.pyNN.models.neuron.structural_plasticity.synaptogenesis.formation.DistanceDependentFormation(grid=(16, 16), p_form_forward=0.16, sigma_form_forward=2.5, p_form_lateral=1.0, sigma_form_lateral=1.0)[source]

Bases: spynnaker.pyNN.models.neuron.structural_plasticity.synaptogenesis.formation.abstract_formation.AbstractFormation

Formation rule that depends on the physical distance between neurons

Parameters:
  • grid (tuple(int,int) or list(int) or ndarray(int)) – (x, y) dimensions of the grid of distance
  • p_form_forward (float) – The peak probability of formation on feed-forward connections
  • sigma_form_forward (float) – The spread of probability with distance of formation on feed-forward connections
  • p_form_lateral (float) – The peak probability of formation on lateral connections
  • sigma_form_lateral (float) – The spread of probability with distance of formation on lateral connections
distance(x0, x1, metric)[source]

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

Parameters:
  • x0 (ndarray(int)) – first point in space
  • x1 (ndarray(int)) – second point in space
  • grid (ndarray(int)) – shape of grid
  • metric (str) – distance metric, i.e. euclidian or manhattan or equidistant
Returns:

the distance

Return type:

float

generate_distance_probability_array(probability, sigma)[source]

Generate the exponentially decaying probability LUTs.

Parameters:
  • probability (float) – peak probability
  • sigma (float) – spread
Returns:

distance-dependent probabilities

Return type:

ndarray(float)

get_parameter_names()[source]

Return the names of the parameters supported by this rule

Return type:iterable(str)
get_parameters_sdram_usage_in_bytes()[source]

Get the amount of SDRAM used by the parameters of this rule

Return type:int
vertex_executable_suffix

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

Return type:str
write_parameters(spec)[source]

Write the parameters of the rule to the spec

Parameters:spec (DataSpecificationGenerator) –