Testing for context-dependent changes in neural encoding in naturalistic experiments
This addresses the challenge of analyzing neural data in complex, real-world settings for neuroscientists, but appears incremental as it builds on existing decoding methods.
The authors tackled the problem of detecting context-dependent changes in neural encoding in naturalistic experiments by proposing a decoding-based approach that is agnostic to encoding specifics and controls for confounds, demonstrating it by decoding location encoding in mouse prefrontal cortex and testing for changes due to task engagement.
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.