ArticleeLife2021
Learning precise spatiotemporal sequences via biophysically realistic learning rules in a modular, spiking network.
Article in eLife, 2021. 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 16 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
16 citing papers in PubMed.
- Hippocampal sequences represent working memory and implicit timing.Cell reports · 2025Article
- Learning to express reward prediction error-like dopaminergic activity requires plastic representations of time.Nature communications · 2024Article
- M2 receptors are required for spatiotemporal sequence learning in mouse primary visual cortex.Journal of neurophysiology · 2024Article
- A biochemical description of postsynaptic plasticity-with timescales ranging from milliseconds to seconds.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- A neural basis for learning sequential memory in brain loop structures.Frontiers in computational neuroscience · 2024Article
- Neural Sequences and the Encoding of Time.Advances in experimental medicine and biology · 2024Review
- Cholinergic Reinforcement Signaling Is Impaired by Amyloidosis Prior to Its Synaptic Loss.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- Coherent noise enables probabilistic sequence replay in spiking neuronal networks.PLoS computational biology · 2023Article
- Editorial: Reproducibility in neuroscience.Frontiers in integrative neuroscience · 2023Article
- Optogenetic manipulation of inhibitory interneurons can be used to validate a model of spatiotemporal sequence learning.Frontiers in computational neuroscience · 2023Article
- Toward reproducible models of sequence learning: replication and analysis of a modular spiking network with reward-based learning.Frontiers in integrative neuroscience · 2023Article
- Sequence learning, prediction, and replay in networks of spiking neurons.PLoS computational biology · 2022Article
- Encoding time in neural dynamic regimes with distinct computational tradeoffs.PLoS computational biology · 2022Article
- Thunderstruck: The ACDC model of flexible sequences and rhythms in recurrent neural circuits.PLoS computational biology · 2022Article
- A series of unforced events.Neuron · 2022Article
- The spatiotemporal organization of experience dictates hippocampal involvement in primary visual cortical plasticity.Current biology : CB · 2021Article
Corrections and comments
- Erratum issued
Authors and funding
2 authors.
Funding
Abstract
Multiple brain regions are able to learn and express temporal sequences, and this functionality is an essential component of learning and memory. We propose a substrate for such representations via a network model that learns and recalls discrete sequences of variable order and duration. The model consists of a network of spiking neurons placed in a modular microcolumn based architecture. Learning is performed via a biophysically realistic learning rule that depends on synaptic 'eligibility traces'. Before training, the network contains no memory of any particular sequence. After training, presentation of only the first element in that sequence is sufficient for the network to recall an entire learned representation of the sequence. An extended version of the model also demonstrates the ability to successfully learn and recall non-Markovian sequences. This model provides a possible framework for biologically plausible sequence learning and memory, in agreement with recent experimental results.
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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.