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

Submodules

spynnaker.pyNN.models.neuron.structural_plasticity.synaptogenesis.formation.abstract_formation module

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

vertex_executable_suffix

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

write_parameters(spec)[source]

Write the parameters of the rule to the spec

spynnaker.pyNN.models.neuron.structural_plasticity.synaptogenesis.formation.distance_dependent_formation module

class spynnaker.pyNN.models.neuron.structural_plasticity.synaptogenesis.formation.distance_dependent_formation.DistanceDependentFormation(grid=array([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 – (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, x1, metric)[source]

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

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

numpy.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

vertex_executable_suffix

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

write_parameters(spec)[source]

Write the parameters of the rule to the spec

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

vertex_executable_suffix

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

write_parameters(spec)[source]

Write the parameters of the rule to the spec

class spynnaker.pyNN.models.neuron.structural_plasticity.synaptogenesis.formation.DistanceDependentFormation(grid=array([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 – (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, x1, metric)[source]

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

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

numpy.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

vertex_executable_suffix

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

write_parameters(spec)[source]

Write the parameters of the rule to the spec