spike_time_tiling_coefficient

spike_time_tiling_coefficient#

class braintools.metric.spike_time_tiling_coefficient(spike_matrix, dt=None, tau=0.005)#

Calculate Spike Time Tiling Coefficient (STTC).

STTC measures synchrony between spike trains while controlling for firing rate differences. It’s based on the proportion of spikes that fall within a temporal window around spikes in the other train.

The STTC is computed as:

\[STTC = \frac{1}{2}\left(\frac{P_A - T_B}{1 - P_A T_B} + \frac{P_B - T_A}{1 - P_B T_A}\right)\]

where \(P_A\) is the proportion of spikes in train A that have a spike from train B within time \(\tau\), and \(T_A\) is the proportion of total time covered by windows around spikes in train A.

Parameters:

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

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

  • tau (float) – Half-width of the temporal window for coincidence detection. Must use the same time unit as dt (default assumes seconds).

Returns:

STTC matrix with shape (n_neurons, n_neurons). Diagonal elements are 1. Values range from -1 to 1, where 1 indicates perfect synchrony.

Return type:

jnp.ndarray

Notes

The time-coverage terms \(T_A\), \(T_B\) use the union of the per-spike \([\,s - \tau,\ s + \tau\,]\) windows, so overlapping windows are not double-counted (the defining quantity in Cutts & Eglen, 2014). Each STTC term is guarded independently against a zero denominator.

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

Examples

>>> import jax.numpy as jnp
>>> import braintools
>>> spikes = jnp.zeros((1000, 3))
>>> sync_times = [100, 300, 500, 700]
>>> for t in sync_times:
...     spikes = spikes.at[t:t + 3, :].set(1)
>>> sttc = braintools.metric.spike_time_tiling_coefficient(spikes, dt=1.0, tau=2.0)
>>> sttc.shape
(3, 3)

References