brainevent.binary_jitumv

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brainevent.binary_jitumv#

brainevent.binary_jitumv = <NameScope(brainevent.binary_jitumv)>#

Event-driven matrix-vector product with a JIT uniform connectivity matrix.

Computes the product of a just-in-time generated sparse matrix with uniformly distributed weights and a binary event vector. Only non-zero (event) entries in vector contribute to the output, making this operation efficient for spike-based neural network simulations.

The sparse matrix A of shape (m, n) is never materialized. Each entry A[i, j] is drawn from Uniform(w_low, w_high) with probability prob, seeded by seed.

Parameters:

  • w_low (Array | ndarray | Quantity | Number) – Lower bound of the uniform weight distribution. Scalar value, optionally with physical units (brainunit.Quantity).

  • w_high (Array | ndarray | Quantity | Number) – Upper bound of the uniform weight distribution. Must have the same dimension (units) as w_low.

  • prob (float) – Connection probability in [0, 1]. Determines the fraction of non-zero entries in each row/column of the connectivity matrix.

  • vector (Array | ndarray | Quantity | Number) – Input event vector. Can be boolean or floating-point; non-zero entries are treated as active events. Length must match the appropriate matrix dimension (n if transpose=False, m if transpose=True).

  • seed (int | None) – Random seed for reproducible connectivity patterns. If None, a random seed is generated at compile time.

  • shape (Tuple[int, int]) – Shape (m, n) of the logical connectivity matrix.

  • transpose (bool) – If True, compute A.T @ vector instead of A @ vector. Default is False.

  • corder (bool) – Memory layout order for the connectivity generation. True for C-order (row-major), False for Fortran-order (column-major). Default is True.

  • backend (str | None) – Computation backend. One of 'numba' or 'pallas'. If None, the default backend is used.

Returns:

Result vector of length m (if transpose=False) or n (if transpose=True). Carries the product of units from the weight and the vector if either has physical units.

Return type:

Array | ndarray | Quantity | Number

See also

binary_jitumm

Event-driven matrix-matrix variant.

jitumv

Float (non-event) matrix-vector variant.

Notes

The connectivity matrix A of shape (m, n) follows the model:

A[i, j] = U[i, j] * B[i, j]

where U[i, j] ~ Uniform(w_low, w_high) and B[i, j] ~ Bernoulli(prob) are independent, both determined by seed.

The event-driven matrix-vector product computes:

result[i] = sum_{j : vector[j] is active} A[i, j]

where “active” means True for boolean arrays or > 0 for float arrays. Only positions where vector[j] is active contribute, making this efficient when the event vector is sparse. The full expansion is:

result[i] = sum_{j} U[i, j] * B[i, j] * 1_{vector[j] active}

When transpose=True, the operation becomes result = A^T @ vector.

Examples

>>> import jax.numpy as jnp
>>> from brainevent._jit_uniform.binary import binary_jitumv
>>> events = jnp.array([True, False, True, True, False])
>>> result = binary_jitumv(
...     0.1, 0.5, 0.2, events, seed=42,
...     shape=(3, 5), transpose=False, corder=True,
... )
>>> result.shape
(3,)
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