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.

Low-dimensional dynamics for working memory and time encoding / Cueva, C. J.; Saez, A.; Marcos, E.; Genovesio, A.; Jazayeri, M.; Romo, R.; Salzman, C. D.; Shadlen, M. N.; Fusi, S.. - In: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA. - ISSN 0027-8424. - 117:37(2020), pp. 23021-23032. [10.1073/pnas.1915984117]

Low-dimensional dynamics for working memory and time encoding

Genovesio A.
Membro del Collaboration Group
;
2020

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 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.
2020
neural dynamics; recurrent networks; reservoir computing; time decoding; working memory; animals; brain; brain mapping; memory, short-term; nerve net; neural networks, computer; neurons; primates
01 Pubblicazione su rivista::01a Articolo in rivista
Low-dimensional dynamics for working memory and time encoding / Cueva, C. J.; Saez, A.; Marcos, E.; Genovesio, A.; Jazayeri, M.; Romo, R.; Salzman, C. D.; Shadlen, M. N.; Fusi, S.. - In: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA. - ISSN 0027-8424. - 117:37(2020), pp. 23021-23032. [10.1073/pnas.1915984117]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1453209
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