Bits per Spike as a Betting Game: An Interpretable Unit for Held-Out Log-Likelihood in Neural Data Analysis
Year: 2026
Type: preprint
Abstract: under a particular null hypothesis testing procedure based on a betting game. Thus, a simple re-scaling of the heldout log-likelihood yields an intuitive metric of model performance in units of recording time which answers "how much heldout 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.
Source: PubMed
Author: Williams, Alex H.
Institutions: Flatiron Institute, New York University
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
Funder: National Institutes of Health
OpenAlex ID: W7172314442