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Multiplexing working memory and time: encoding retrospective and prospective information in neural trajectories

View ORCID ProfileShanglin Zhou, Michael Seay, Jiannis Taxidis, Peyman Golshani, Dean V. Buonomano
doi: https://doi.org/10.1101/2022.07.08.499383
Shanglin Zhou
1Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
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  • ORCID record for Shanglin Zhou
Michael Seay
2Department of Psychology, University of California, Los Angeles, CA, USA
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Jiannis Taxidis
3Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
4Integrative Center for Learning and Memory, Brain Research Institute, University of California, Los Angeles, Los Angeles, CA, USA
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Peyman Golshani
3Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
4Integrative Center for Learning and Memory, Brain Research Institute, University of California, Los Angeles, Los Angeles, CA, USA
5UCLA Semel Institute for Neuroscience and Behavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
6West Lost Angeles VA Medical Center, Los Angeles, CA, USA
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Dean V. Buonomano
1Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
2Department of Psychology, University of California, Los Angeles, CA, USA
4Integrative Center for Learning and Memory, Brain Research Institute, University of California, Los Angeles, Los Angeles, CA, USA
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  • For correspondence: dbuono@ucla.edu
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ABSTRACT

Working memory (WM) and timing are generally considered distinct cognitive functions, but similar neural signatures have been implicated in both. To explore the hypothesis that WM and timing may rely on shared neural mechanisms, we used psychophysical tasks that contained either task-irrelevant timing or WM components. In both cases the task-irrelevant component influenced performance. RNN simulations revealed that cue-specific neural sequences, which multiplexed WM and time, emerged as the dominant regime that captured the behavioral findings. Over the course of training RNN dynamics transitioned from low-dimensional ramps to high-dimensional neural sequences, and depending on task requirements, steady-state or ramping activity was also observed. Analysis of RNN structure revealed that neural sequences relied primarily on inhibitory connections, and could survive the deletion of all excitatory-to- excitatory connections. Our results suggest that in some instances WM is encoded in time-varying neural activity because of the importance of predicting when WM will be used.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted July 10, 2022.
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Multiplexing working memory and time: encoding retrospective and prospective information in neural trajectories
Shanglin Zhou, Michael Seay, Jiannis Taxidis, Peyman Golshani, Dean V. Buonomano
bioRxiv 2022.07.08.499383; doi: https://doi.org/10.1101/2022.07.08.499383
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Multiplexing working memory and time: encoding retrospective and prospective information in neural trajectories
Shanglin Zhou, Michael Seay, Jiannis Taxidis, Peyman Golshani, Dean V. Buonomano
bioRxiv 2022.07.08.499383; doi: https://doi.org/10.1101/2022.07.08.499383

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