Rate-distortion theory of neural coding and its implications for working memory
- Harvard University, United States
Abstract
Rate-distortion theory provides a powerful framework for understanding the nature of human memory by formalizing the relationship between information rate (the average number of bits per stimulus transmitted across the memory channel) and distortion (the cost of memory errors). Here we show how this abstract computational-level framework can be realized by a model of neural population coding. The model reproduces key regularities of visual working memory, including some that were not previously explained by population coding models. We verify a novel prediction of the model by reanalyzing recordings of monkey prefrontal neurons during an oculomotor delayed response task.
Data availability
The current manuscript is a computational study, so no data have been generated for this manuscript. Source code can be found at https://github.com/amvjakob/wm-rate-distortion. The previously published datasets are available upon request from the corresponding authors of the published papers, Souza and Oberauer (2015), Daniel Bliss at al. (2017), Panichello et al. (2019). A minimally processed dataset from Barbosa et al. (2020) is available online ((https://github.com/comptelab/interplayPFC), with the raw data available upon request from the corresponding author of the published paper (raw monkey data available upon request to Christos Constantinidis cconstan@wakehealth.edu, and raw EEG data available upon request to Heike Stein, heike.c.stein@gmail.com). There are no specific application or approval processes involved in requesting these datasets.
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Interplay between persistent activity and activity-silent dynamics in the prefrontal cortex underlies serial biases in working memoryhttps://github.com/comptelab/interplayPFC.
Article and author information
Author details
Funding
Fondation Bertarelli (Bertarelli Fellowship)
- Anthony MV Jakob
National Science Foundation (NSF STC award,CCF-1231216)
- Samuel J Gershman
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Paul Bays, University of Cambridge, United Kingdom
Version history
- Preprint posted: March 2, 2022 (view preprint)
- Received: April 13, 2022
- Accepted: June 22, 2023
- Accepted Manuscript published: July 12, 2023 (version 1)
- Version of Record published: July 18, 2023 (version 2)
Copyright
© 2023, Jakob & Gershman
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Rate-distortion theory of neural coding and its implications for working memory
eLife 12:e79450.
https://doi.org/10.7554/eLife.79450
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