Multiplexing working memory and time in the trajectories of neural networks

Shanglin Zhou, Michael Seay, Jiannis Taxidis, Peyman Golshani and Dean V. Buonomano ()
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Shanglin Zhou: David Geffen School of Medicine, University of California
Michael Seay: University of California
Jiannis Taxidis: Hospital for Sick Children
Peyman Golshani: University of California, Los Angeles
Dean V. Buonomano: David Geffen School of Medicine, University of California

Nature Human Behaviour, 2023, vol. 7, issue 7, 1170-1184

Abstract: 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. We then developed recurrent neural network (RNN) simulations that revealed that cue-specific neural sequences, which multiplexed WM and time, emerged as the dominant regime that captured the behavioural findings. During 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 indicate that in some instances WM is encoded in time-varying neural activity because of the importance of predicting when WM will be used.

Date: 2023
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DOI: 10.1038/s41562-023-01592-y

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