Bits per Spike as a Betting Game: An Interpretable Unit for Held-Out Log-Likelihood in Neural Data Analysis

Abstract

Held-out log-likelihood is the standard currency for comparing statistical models of neural spike trains, and is often reported as bits per spike relative to a homogeneous Poisson baseline. The units of this metric are difficult to reason about: it is rarely obvious whether an improvement of, say, $0.34$ bits per spike is a large effect or a negligible one. This note develops an interpretation of held-out log-likelihood borrowed from game-theoretic statistics. If $L$ denotes a model's expected log-likelihood ratio to the homogeneous Poisson baseline, then we show that $-\log (\alpha )/L$ represents the number of heldout time bins of recording needed to reject the baseline at level $\alpha$ under a particular null hypothesis testing procedure based on a betting game. Thus, a simple re-scaling of the log-likelihood yields an intuitive metric of model performance in units of recording time which answers "how much held out data would I need, on average, to disprove the null." We illustrate the construction on head-direction cells recorded in mouse anterior thalamus, where a generalized linear model reaches significance in roughly $120$ ms of held-out data for a strongly tuned cell and roughly $11$ s for a moderately tuned cell.

arXiv comment13 pages, 4 figures. Companion blog post: https://neurostatsblog.github.io/2026/05/27/model-comparison-by-betting/