burst_synchrony_index

class braintools.metric.burst_synchrony_index(spike_matrix, burst_threshold=3, max_isi=100.0, dt=None)

Calculate burst synchrony index based on co-occurring burst events.

This measure identifies burst events in each spike train and quantifies the synchronization of these bursts across the population.

A burst is defined as a sequence of at least burst_threshold spikes with inter-spike intervals ≤ max_isi.

Parameters:

• spike_matrix (Array | ndarray | bool | number | bool | int | float | complex | Quantity) – Spike matrix with shape (n_time_steps, n_neurons).

• burst_threshold (int) – Minimum number of spikes required to constitute a burst.

• max_isi (float) – Maximum inter-spike interval within a burst. Must use the same time unit as dt.

• dt (float) – Time step between successive samples. If None, uses brainstate default.

Returns:

Burst synchrony index between 0 (no burst synchrony) and 1 (perfect burst synchrony).

Return type:

float

Notes

This function runs on host (concrete) arrays (Python loops, len), so it is not jit/vmap/grad-compatible.

Examples

>>> import jax.numpy as jnp
>>> import braintools
>>> spikes = jnp.zeros((1000, 10))
>>> for start in [100, 300, 600]:
...     for i in range(10):
...         spikes = spikes.at[start:start + 5, i].set(1)
>>> sync_idx = braintools.metric.burst_synchrony_index(spikes, max_isi=5.0, dt=1.0)
>>> bool(0.0 <= sync_idx <= 1.0)
True