This pull request introduces new functionality to handle distance-dependent connection probabilities in the SPManager class. The following methods have been added or modified:
New Features
gather_global_positions_and_ids()
Collects and verifies global neuron positions and IDs.
Ensures that neurons are spatially distributed, raising an error if positions are undefined while distance dependency is enabled.
gaussian_kernel()
Computes a probability based on the Gaussian distance kernel between two neuron positions.
build_probability_list()
Constructs a probability list for all neuron pairs based on their spatial positions.
Supports caching for efficient repeated lookups.
get_neuron_pair_index()
Maps two neuron IDs to a unique index for accessing pair-specific probabilities in the probability_list.
global_shuffle_spatial()
Implements distance-dependent shuffling for pairing neurons based on spatial proximity and probability.
roulette_wheel_selection()
Performs a weighted random selection of post-synaptic neurons based on precomputed probabilities.
Modified Methods
enable_structural_plasticity()
Integrates calls to gather_global_positions_and_ids() and build_problist() when distance dependency is enabled.
Distance-Dependent Features
Enable Distance Dependency
Distance dependency can be enabled by setting structural_plasticity_gaussian_kernel_sigma to a positive value. This parameter controls the width of the Gaussian kernel used for connection probabilities. Higher values result in reduced sensitivity to distance, making distant neurons more likely to connect.
Caching Connection Probabilities
If structural_plasticity_cache_probabilities is set to true, connection probabilities will be precomputed and cached. This can improve performance, particularly in long-running simulations.
Testing
The following tests have been added to validate the new functionality:
C++ Tests:
test_gaussian_kernel
Tests Gaussian kernel computations for various distances and sigma values.
test_get_neuron_pair_index
Confirms correct mapping of neuron pairs to unique indices.
Description
This pull request introduces new functionality to handle distance-dependent connection probabilities in the
SPManagerclass. The following methods have been added or modified:New Features
gather_global_positions_and_ids()Collects and verifies global neuron positions and IDs.
Ensures that neurons are spatially distributed, raising an error if positions are undefined while distance dependency is enabled.
gaussian_kernel()Computes a probability based on the Gaussian distance kernel between two neuron positions.
build_probability_list()Constructs a probability list for all neuron pairs based on their spatial positions.
Supports caching for efficient repeated lookups.
get_neuron_pair_index()Maps two neuron IDs to a unique index for accessing pair-specific probabilities in the
probability_list.global_shuffle_spatial()Implements distance-dependent shuffling for pairing neurons based on spatial proximity and probability.
roulette_wheel_selection()Performs a weighted random selection of post-synaptic neurons based on precomputed probabilities.
Modified Methods
enable_structural_plasticity()Integrates calls to
gather_global_positions_and_ids()andbuild_problist()when distance dependency is enabled.Distance-Dependent Features
Enable Distance Dependency
Distance dependency can be enabled by setting
structural_plasticity_gaussian_kernel_sigmato a positive value. This parameter controls the width of the Gaussian kernel used for connection probabilities. Higher values result in reduced sensitivity to distance, making distant neurons more likely to connect.Caching Connection Probabilities
If
structural_plasticity_cache_probabilitiesis set totrue, connection probabilities will be precomputed and cached. This can improve performance, particularly in long-running simulations.Testing
The following tests have been added to validate the new functionality:
C++ Tests:
test_gaussian_kernelTests Gaussian kernel computations for various distances and sigma values.
test_get_neuron_pair_indexConfirms correct mapping of neuron pairs to unique indices.
test_global_shuffle_spatialValidates spatially aware shuffling logic.
Ensures connections favor spatial proximity and prevent self-connections.
Python Tests:
test_distance_dependent_without_positions()Ensures an error is raised when distance dependency is enabled, but positions are not provided.
test_gaussian_kernel()Tests probability computation using Gaussian distance dependency.
test_probability_list_building()Verifies the correct initialization and population of the probability list.
test_roulette_wheel_selection()Confirms that weighted random selection behaves as expected.
test_structural_plasticity_with_positions()Ensures structural plasticity works correctly when neuron positions are defined.
test_synapse_creation_distance_dependent()Validates distance-dependent synapse formation using position data.
Related Issue:
Incorporate Distance-Dependent Connection Probability in Structural Plasticity Module #3209