Low dimensional dynamics for working memory and time encoding
| Autor: | Stefano Fusi, C. Daniel Salzman, Christopher J. Cueva, Mehrdad Jazayeri, Ranulfo Romo, Encarni Marcos, Aldo Genovesio, Michael N. Shadlen, Alex Saez |
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| Přispěvatelé: | National Science Foundation (US), Gatsby Charitable Foundation, Simons Foundation, Universidad Nacional Autónoma de México, National Institutes of Health (US), Consejo Nacional de Ciencia y Tecnología (México), Fondazione Regionale per la Ricerca Biomedica, National Institute of Mental Health (US) |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Primates
Computer science Chaotic Sensory system 03 medical and health sciences 0302 clinical medicine Encoding (memory) Attractor Animals Backpropagation through time 030304 developmental biology Neurons Brain Mapping 0303 health sciences Multidisciplinary Artificial neural network Quantitative Biology::Neurons and Cognition Working memory business.industry Reservoir computing Brain Pattern recognition Biological Sciences Backpropagation 3. Good health Memory Short-Term Recurrent neural network neural dynamics recurrent networks reservoir computing time decoding working memory animals brain brain mapping memory short-term nerve net neural networks computer neurons primates Trajectory Neural Networks Computer Artificial intelligence Nerve Net business 030217 neurology & neurosurgery Decoding methods Curse of dimensionality |
| Zdroj: | Digital.CSIC. Repositorio Institucional del CSIC instname Proc Natl Acad Sci U S A |
| DOI: | 10.1101/504936 |
| Popis: | 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. Research was supported by NSF Next Generation Network for Neuroscience Award DBI-1707398, the Gatsby Charitable Foundation, the Simons Foundation, the Swartz Foundation (C.J.C. and S.F.), NIH Training Grant 5T32NS064929 (to C.J.C.), and the Kavli Foundation (S.F.). M.N.S. was supported by National Institute of Neurological Disorders and Stroke Brain Initiative Grant R01NS113113. R.R. was supported by the Dirección General de Asuntos del Personal Académico de la Universidad Nacional Autónoma de México (PAPIIT-IN210819) and Consejo Nacional de Ciencia y Tecnología (CONACYT-240892). A.G. was supported by National Institute of Mental Health Division of Intramural Research Grant Z01MH-01092 and by Italian Fondo per gli investimenti della ricerca di base 2010 Grant RBFR10G5W9_001. |
| Databáze: | OpenAIRE |
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