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

arXiv 2211.09295 ↗First posted 17 Nov 2022stat.ML cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 5 of its 43 references that are in the map. First posted on arXiv 17 Nov 2022.

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References · 5

  1. 2018Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shiftcited by 240
  2. 2013Partial Distance Correlation with Methods for Dissimilaritiescited by 20
  3. 2019hyppo: A Multivariate Hypothesis Testing Python Packagecited by 8
  4. 2019The Chi-Square Test of Distance Correlationcited by 8
  5. 2016Better-Than-Chance Classification for Signal Detectioncited by 7

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