ArticlePLoS computational biology2018
Bridging structure and function: A model of sequence learning and prediction in primary visual cortex.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed, 29 citations in OpenAlex.
- Competition and cooperation of assembly sequences in recurrent neural networks.PLoS computational biology · 2025Article
- A neural basis for learning sequential memory in brain loop structures.Frontiers in computational neuroscience · 2024Article
- Coherent noise enables probabilistic sequence replay in spiking neuronal networks.PLoS computational biology · 2023Article
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- Toward reproducible models of sequence learning: replication and analysis of a modular spiking network with reward-based learning.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
- Sequence learning, prediction, and replay in networks of spiking neurons.PLoS computational biology · 2022Article
- High-frequency oscillations and sequence generation in two-population models of hippocampal region CA1.PLoS computational biology · 2022Article
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- Self-Organized Structuring of Recurrent Neuronal Networks for Reliable Information Transmission.Biology · 2021Article
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Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent experiments have demonstrated that visual cortex engages in spatio-temporal sequence learning and prediction. The cellular basis of this learning remains unclear, however. Here we present a spiking neural network model that explains a recent study on sequence learning in the primary visual cortex of rats. The model posits that the sequence learning and prediction abilities of cortical circuits result from the interaction of spike-timing dependent plasticity (STDP) and homeostatic plasticity mechanisms. It also reproduces changes in stimulus-evoked multi-unit activity during learning. Furthermore, it makes precise predictions regarding how training shapes network connectivity to establish its prediction ability. Finally, it predicts that the adapted connectivity gives rise to systematic changes in spontaneous network activity. Taken together, our model establishes a new conceptual bridge between the structure and function of cortical circuits in the context of sequence learning and prediction.
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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.