ArticleeLife2024
Dynamic control of sequential retrieval speed in networks with heterogeneous learning rules.
Article in eLife, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Synaptic pruning, myelination and the emergence of psychiatric disorders in late adolescence.bioRxiv : the preprint server for biology · 2026Article
- Balancing Stability and Flow in Hippocampal Networks via Inductive Bias and Learned Symmetry Breaking.bioRxiv : the preprint server for biology · 2026Article
- Conditions for replay of neuronal assemblies.PLoS computational biology · 2026Article
- Neural dynamics outside task-coding dimensions drive decision trajectories through transient amplification.bioRxiv : the preprint server for biology · 2025Article
- Cognitive network interactions through communication subspaces in large-scale models of the neocortex.bioRxiv : the preprint server for biology · 2024Article
- Recall tempo of Hebbian sequences depends on the interplay of Hebbian kernel with tutor signal timing.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Temporal rescaling of sequential neural activity has been observed in multiple brain areas during behaviors involving time estimation and motor execution at variable speeds. Temporally asymmetric Hebbian rules have been used in network models to learn and retrieve sequential activity, with characteristics that are qualitatively consistent with experimental observations. However, in these models sequential activity is retrieved at a fixed speed. Here, we investigate the effects of a heterogeneity of plasticity rules on network dynamics. In a model in which neurons differ by the degree of temporal symmetry of their plasticity rule, we find that retrieval speed can be controlled by varying external inputs to the network. Neurons with temporally symmetric plasticity rules act as brakes and tend to slow down the dynamics, while neurons with temporally asymmetric rules act as accelerators of the dynamics. We also find that such networks can naturally generate separate 'preparatory' and 'execution' activity patterns with appropriate external inputs.
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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.