Choose a connectivity format

brainevent offers several ways to represent the connectivity a spike vector multiplies against. This guide helps you pick the right one. For the reasoning behind the formats, see Sparse format trade-offs.

Decision guide

Format	Use when	Avoid when
Dense (jax/numpy array)	The matrix is small or genuinely dense (>~25 % non-zero), or you need arbitrary per-entry weights with the simplest possible code.	The matrix is large and sparse — you waste memory and compute on zeros.
CSR / CSC	You have an explicit, fixed sparse matrix and want fast row- (CSR) or column- (CSC) oriented event-driven products.	Connectivity is generated randomly and the full matrix would not fit in memory.
JITC (JITCScalarR, JITCNormalR, JITCUniformR, …)	Connectivity is random with a fixed probability, and you want to never materialise the matrix — it is regenerated on the fly from a seed.	You need to inspect, mutate, or learn individual weights.
Fixed fan-in/out (FixedPreNumConn, FixedPostNumConn)	Each neuron has a fixed number of connections (biologically common), and you want that structure encoded directly.	Connection counts vary per neuron, or you need an explicit weight matrix.

Rule of thumb

• Explicit and reusable → CSR/CSC.

• Random and huge → JITC* (memory cost is independent of density; see Use JIT connectivity for large networks).

• Fixed degree per neuron → FixedPreNumConn / FixedPostNumConn.

Every format multiplies the same way against a BinaryArray, so you can swap formats without changing the surrounding code:

import brainevent
import jax.numpy as jnp

spikes = brainevent.BinaryArray(jnp.array([1, 0, 1, 0, 1], dtype=jnp.float32))

for conn in (csr, jitc, fixed):       # any format
    out = spikes @ conn               # identical call site

Row vs column variants (R / C)

JITC and CSR/CSC come in row- and column-oriented variants. Choose based on which dimension your spike vector indexes — the variant that keeps the contraction along contiguous memory is faster. When in doubt, benchmark both with benchmark_function().

See also

Tutorial 2: Sparse Data Structures - CSR and CSC and Tutorial 3: JIT Connection Matrices - Just-In-Time Generated Random Connections walk through each format interactively.