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Low dimensional dynamics for working memory and time encoding

Christopher J. Cueva, Encarni Marcos, Alex Saez, Aldo Genovesio, Mehrdad Jazayeri, Ranulfo Romo, C. Daniel Salzman, Michael N. Shadlen, Stefano Fusi
doi: https://doi.org/10.1101/504936
Christopher J. Cueva
1Department of Neuroscience, Columbia University, New York, USA
2Center for Theoretical Neuroscience, Columbia University, New York, USA
3Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, USA
4Kavli Institute for Brain Sciences, Columbia University, New York, USA
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Encarni Marcos
5University of Rome “La Sapienza”, Rome, Italy
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Alex Saez
1Department of Neuroscience, Columbia University, New York, USA
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Aldo Genovesio
5University of Rome “La Sapienza”, Rome, Italy
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Mehrdad Jazayeri
6McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, USA
7Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, USA
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Ranulfo Romo
8Instituto de Fisiología Celular - Neurociencias, Universidad Nacional Autónoma de México, Mexico City, Mexico
9El Colegio Nacional, Mexico City, Mexico
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C. Daniel Salzman
1Department of Neuroscience, Columbia University, New York, USA
3Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, USA
4Kavli Institute for Brain Sciences, Columbia University, New York, USA
10Department of Psychiatry, Columbia University, New York, USA
11New York State Psychiatric Institute, New York, USA
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Michael N. Shadlen
1Department of Neuroscience, Columbia University, New York, USA
3Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, USA
4Kavli Institute for Brain Sciences, Columbia University, New York, USA
12Howard Hughes Medical Institute, Columbia University, New York, USA
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Stefano Fusi
1Department of Neuroscience, Columbia University, New York, USA
2Center for Theoretical Neuroscience, Columbia University, New York, USA
3Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, USA
4Kavli Institute for Brain Sciences, Columbia University, New York, USA
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Abstract

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 non-human 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. 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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Posted January 31, 2019.
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Low dimensional dynamics for working memory and time encoding
Christopher J. Cueva, Encarni Marcos, Alex Saez, Aldo Genovesio, Mehrdad Jazayeri, Ranulfo Romo, C. Daniel Salzman, Michael N. Shadlen, Stefano Fusi
bioRxiv 504936; doi: https://doi.org/10.1101/504936
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Low dimensional dynamics for working memory and time encoding
Christopher J. Cueva, Encarni Marcos, Alex Saez, Aldo Genovesio, Mehrdad Jazayeri, Ranulfo Romo, C. Daniel Salzman, Michael N. Shadlen, Stefano Fusi
bioRxiv 504936; doi: https://doi.org/10.1101/504936

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