ArticleProceedings of the National Academy of Sciences of the United States of America2020
Understanding the computation of time using neural network models.
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. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed, 72 citations in OpenAlex.
- Cerebellar microcircuits enable robust evidence-based decisions through cortico-cerebellar coupling.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- The speed limit of visual perception: Bidirectional influence of image memorability and processing speed on perceived duration and recognition.PLoS computational biology · 2026Article
- Analyzing rescaling, discretization, and linearization in RNNs for neural system modeling.Frontiers in computational neuroscience · 2026Article
- Multistable bimodal perceptual coding within the ventral premotor cortex.Science advances · 2025Article
- Stimuli are perceived as lasting longer when there is something bright on the screen.Attention, perception & psychophysics · 2025Article
- The effects of the post-delay epochs on working memory error reduction.PLoS computational biology · 2025Article
- Contextual neural dynamics during time perception in the primate ventral premotor cortex.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Benchmarking the speed-accuracy tradeoff in object recognition by humans and neural networks.Journal of vision · 2025Article
- Brain-inspired multisensory integration neural network for cross-modal recognition through spatiotemporal dynamics and deep learning.Cognitive neurodynamics · 2024Article
- Boosting efficiency in a clinical literature surveillance system with LightGBM.PLOS digital health · 2024Article
- Orthogonality of sensory and contextual categorical dynamics embedded in a continuum of responses from the second somatosensory cortex.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Memorability shapes perceived time (and vice versa).Nature human behaviour · 2024Article
- Probing latent brain dynamics in Alzheimer's disease via recurrent neural network.Cognitive neurodynamics · 2024Article
- Effect in the spectra of eigenvalues and dynamics of RNNs trained with excitatory-inhibitory constraint.Cognitive neurodynamics · 2024Article
- Neuroimaging Signatures of Metacognitive Improvement in Sensorimotor Timing.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Pre-supplementary Motor Cortex Mediates Learning Transfer from Perceptual to Motor Timing.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Cognition of Time and Thinking Beyond.Advances in experimental medicine and biology · 2024Review
- Exploring Flip Flop memories and beyond: training Recurrent Neural Networks with key insights.Frontiers in systems neuroscience · 2024Article
- Neurocomputational Models of Interval Timing: Seeing the Forest for the Trees.Advances in experimental medicine and biology · 2024Review
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Corrections and comments
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To maximize future rewards in this ever-changing world, animals must be able to discover the temporal structure of stimuli and then anticipate or act correctly at the right time. How do animals perceive, maintain, and use time intervals ranging from hundreds of milliseconds to multiseconds in working memory? How is temporal information processed concurrently with spatial information and decision making? Why are there strong neuronal temporal signals in tasks in which temporal information is not required? A systematic understanding of the underlying neural mechanisms is still lacking. Here, we addressed these problems using supervised training of recurrent neural network models. We revealed that neural networks perceive elapsed time through state evolution along stereotypical trajectory, maintain time intervals in working memory in the monotonic increase or decrease of the firing rates of interval-tuned neurons, and compare or produce time intervals by scaling state evolution speed. Temporal and nontemporal information is coded in subspaces orthogonal with each other, and the state trajectories with time at different nontemporal information are quasiparallel and isomorphic. Such coding geometry facilitates the decoding generalizability of temporal and nontemporal information across each other. The network structure exhibits multiple feedforward sequences that mutually excite or inhibit depending on whether their preferences of nontemporal information are similar or not. We identified four factors that facilitate strong temporal signals in nontiming tasks, including the anticipation of coming events. Our work discloses fundamental computational principles of temporal processing, and it is supported by and gives predictions to a number of experimental phenomena.
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Registered trials
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.