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Low-dimensional dynamics for working memory and time encoding
Name
23021.full.pdf
Description
Published version
Size
3.07 MB
Format
Adobe PDF
Checksum (MD5)
8f3b4e12f4db0a06d6aada1e4858670d
Author(s)
Cueva, Christopher J
Saez, Alex
Marcos, Encarni
Genovesio, Aldo
Jazayeri, Mehrdad
Romo, Ranulfo
Salzman, C Daniel
Shadlen, Michael N
Fusi, Stefano
Date Issued
2020
Journal
Proceedings of the National Academy of Sciences of the United States of America
Publisher
Proceedings of the National Academy of Sciences
Version
Final published version
Abstract
© 2020 National Academy of Sciences. All rights reserved. Our decisions often depend on multiple sensory experiences separated by time delays. The brain can remember these experiences and, simultaneously, estimate the timing between events. To understand the mechanisms underlying working memory and time encoding, we analyze neural activity recorded during delays in four experiments on nonhuman primates. To disambiguate potential mechanisms, we propose two analyses, namely, decoding the passage of time from neural data and computing the cumulative dimensionality of the neural trajectory over time. Time can be decoded with high precision in tasks where timing information is relevant and with lower precision when irrelevant for performing the task. Neural trajectories are always observed to be low-dimensional. In addition, our results further constrain the mechanisms underlying time encoding as we find that the linear “ramping” component of each neuron’s firing rate strongly contributes to the slow timescale variations that make decoding time possible. These constraints rule out working memory models that rely on constant, sustained activity and neural networks with high-dimensional trajectories, like reservoir networks. Instead, recurrent networks trained with backpropagation capture the time-encoding properties and the dimensionality observed in the data.
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
10.1073/PNAS.1915984117
