ArticleNeural computation2018
Encoding Time in Feedforward Trajectories of a Recurrent Neural Network Model.
Article in Neural computation, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- 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
- Single-unit activations confer inductive biases for emergent circuit solutions to cognitive tasks.Nature machine intelligence · 2025Article
- Dynamical mechanisms of how an RNN keeps a beat, uncovered with a low-dimensional reduced model.Scientific reports · 2024Article
- Neural Sequences and the Encoding of Time.Advances in experimental medicine and biology · 2024Review
- Neurocomputational Models of Interval Timing: Seeing the Forest for the Trees.Advances in experimental medicine and biology · 2024Review
- Multiplexing working memory and time in the trajectories of neural networks.Nature human behaviour · 2023Article
- Spiking Recurrent Neural Networks Represent Task-Relevant Neural Sequences in Rule-Dependent Computation.Cognitive computation · 2023Article
- Cognitive and plastic recurrent neural network clock model for the judgment of time and its variations.Scientific reports · 2023Article
- Cerebellar contributions across behavioural timescales: a review from the perspective of cerebro-cerebellar interactions.Frontiers in systems neuroscience · 2023Review
- Temporal encoding: Relative and absolute representations of time guide behavior.Journal of experimental psychology. Animal learning and cognition · 2023Article
- Optogenetic manipulation of inhibitory interneurons can be used to validate a model of spatiotemporal sequence learning.Frontiers in computational neuroscience · 2023Article
- Encoding time in neural dynamic regimes with distinct computational tradeoffs.PLoS computational biology · 2022Article
- Quantitative properties of the creation and activation of a cell-intrinsic duration-encoding engram.Frontiers in computational neuroscience · 2022Article
- Stimulus-Driven and Spontaneous Dynamics in Excitatory-Inhibitory Recurrent Neural Networks for Sequence Representation.Neural computation · 2021Article
- Learning compositional sequences with multiple time scales through a hierarchical network of spiking neurons.PLoS computational biology · 2021Article
- Article
- Understanding the computation of time using neural network models.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Learning spatiotemporal signals using a recurrent spiking network that discretizes time.PLoS computational biology · 2020Article
- The amplitude in periodic neural state trajectories underlies the tempo of rhythmic tapping.PLoS biology · 2019Article
- Modeling Interval Timing by Recurrent Neural Nets.Frontiers in integrative neuroscience · 2019Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Brain activity evolves through time, creating trajectories of activity that underlie sensorimotor processing, behavior, and learning and memory. Therefore, understanding the temporal nature of neural dynamics is essential to understanding brain function and behavior. In vivo studies have demonstrated that sequential transient activation of neurons can encode time. However, it remains unclear whether these patterns emerge from feedforward network architectures or from recurrent networks and, furthermore, what role network structure plays in timing. We address these issues using a recurrent neural network (RNN) model with distinct populations of excitatory and inhibitory units. Consistent with experimental data, a single RNN could autonomously produce multiple functionally feedforward trajectories, thus potentially encoding multiple timed motor patterns lasting up to several seconds. Importantly, the model accounted for Weber's law, a hallmark of timing behavior. Analysis of network connectivity revealed that efficiency-a measure of network interconnectedness-decreased as the number of stored trajectories increased. Additionally, the balance of excitation (E) and inhibition (I) shifted toward excitation during each unit's activation time, generating the prediction that observed sequential activity relies on dynamic control of the E/I balance. Our results establish for the first time that the same RNN can generate multiple functionally feedforward patterns of activity as a result of dynamic shifts in the E/I balance imposed by the connectome of the RNN. We conclude that recurrent network architectures account for sequential neural activity, as well as for a fundamental signature of timing behavior: Weber's law.
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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.