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)