ArticleProceedings of the National Academy of Sciences of the United States of America2020
Low-dimensional dynamics for working memory and time encoding.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 64 papers.
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Who cites it
64 citing papers in PubMed.
- Intrinsic timing, not temporal prediction, underlies ramping dynamics in visual and parietal cortex during passive behavior.Science advances · 2026Article
- Neural dynamics underlying minute-timescale persistent behaviour in the human brain.Nature human behaviour · 2026Article
- Learning shapes neural geometry in the primate prefrontal cortex.Nature neuroscience · 2026Article
- Benchmarking criteria to determine latent linear dimensionality in neural data.Scientific reports · 2026Article
- The geometry of context-dependent biased decisions during learning.bioRxiv : the preprint server for biology · 2026Article
- Hippocampal sequences represent working memory and implicit timing.Cell reports · 2025Article
- Article
- Slow Transition to Low-Dimensional Chaos in Heavy-Tailed Recurrent Neural Networks.bioRxiv : the preprint server for biology · 2025Article
- Stochastic activity in low-rank recurrent neural networks.PLoS computational biology · 2025Article
- A neural manifold view of the brain.Nature neuroscience · 2025Review
- Rarely categorical, always high-dimensional: how the neural code changes along the cortical hierarchy.bioRxiv : the preprint server for biology · 2025Article
- Recurrent neural networks with transient trajectory explain working memory encoding mechanisms.Communications biology · 2025Article
- Article
- Stable sequential dynamics in prefrontal cortex represents subjective estimation of time.eLife · 2024Article
- Different Subregions of Monkey Lateral Prefrontal Cortex Respond to Abstract Sequences and Their Components.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Semi-orthogonal subspaces for value mediate a binding and generalization trade-off.Nature neuroscience · 2024Article
- A transient high-dimensional geometry affords stable conjunctive subspaces for efficient action selection.Nature communications · 2024Article
- Ramping cells in the rodent medial prefrontal cortex encode time to past and future events via real Laplace transform.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Different subregions of monkey lateral prefrontal cortex respond to abstract sequences and their components.bioRxiv : the preprint server for biology · 2024Article
- A Transient High-dimensional Geometry Affords Stable Conjunctive Subspaces for Efficient Action Selection.bioRxiv : the preprint server for biology · 2024Article
4 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
9 authors.
Funding
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.