Testing for context-dependent changes in neural encoding in naturalistic experiments

TL;DR

This paper introduces a decoding-based method to detect how neural encoding varies with context in naturalistic, longitudinal neural recordings, effectively controlling for confounding factors and applied to mouse prefrontal cortex data.

Contribution

It presents a novel, agnostic decoding approach for identifying context-dependent neural encoding changes in complex, real-world experimental data.

Findings

Decoding location encoding from mouse prefrontal cortex is feasible.

Encoding changes with task engagement can be detected.

Method controls for confounding factors in neural data.

Abstract

We propose a decoding-based approach to detect context effects on neural codes in longitudinal neural recording data. The approach is agnostic to how information is encoded in neural activity, and can control for a variety of possible confounding factors present in the data. We demonstrate our approach by determining whether it is possible to decode location encoding from prefrontal cortex in the mouse and, further, testing whether the encoding changes due to task engagement.