ConnectionResult

class braintools.conn.ConnectionResult(pre_indices, post_indices, pre_size, post_size, pre_positions=None, post_positions=None, pre_compartments=None, post_compartments=None, weights=None, delays=None, model_type='point', metadata=None)

Universal container for connectivity results across all neuron model types.

This class provides a unified interface for representing connections while maintaining type-specific information and capabilities.

Parameters:

• pre_indices (ndarray) – Presynaptic element indices (neurons, populations, or compartments).

• post_indices (ndarray) – Postsynaptic element indices (neurons, populations, or compartments).

• pre_size (int | Sequence[int] | None) – Size of the presynaptic population (number of elements or shape).

• post_size (int | Sequence[int] | None) – Size of the postsynaptic population (number of elements or shape).

• weights (ndarray | Quantity | None) – Connection weights with appropriate units for the model type.

• delays (ndarray | Quantity | None) – Connection delays with time units (e.g., ms).

• model_type (str) – Type of neuron model (‘point’, ‘population_rate’, ‘multi_compartment’).

• pre_positions (ndarray | None) – Positions of presynaptic elements (for distance calculations).

• post_positions (ndarray | None) – Positions of postsynaptic elements (for distance calculations).

• pre_compartments (ndarray | None) – Presynaptic compartment indices (for multi-compartment models).

• post_compartments (ndarray | None) – Postsynaptic compartment indices (for multi-compartment models).

• metadata (Dict[str, Any] | None) – Model-specific metadata and parameters.

delay2csr()

Convert delays to sparse connectivity matrix in CSR format.

delay2matrix()

Convert delays to dense connectivity matrix.

get_distances()

Calculate distances between connected elements.

Return type:

Quantity | None

property n_connections: int

Number of connections.

property positions: Tuple[ndarray, ndarray] | None

Backward compatibility property for positions.

property shape: Tuple[int, int]

Shape of the connectivity (pre_size, post_size).

weight2csr()

Convert to sparse connectivity matrix in CSR format.

weight2dense()

Convert to dense connectivity matrix.