ArticleProceedings of the National Academy of Sciences of the United States of America2020
Characteristics of sequential activity in networks with temporally asymmetric Hebbian learning.
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 24 papers.
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24 citing papers in PubMed.
- Synaptic pruning, myelination and the emergence of psychiatric disorders in late adolescence.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
- Representational drift as the consequence of ongoing memory storage.Scientific reports · 2025Article
- Inferring neural population codes forProceedings of the National Academy of Sciences of the United States of America · 2025Article
- Cognitive network interactions through communication subspaces in large-scale models of the neocortex.bioRxiv : the preprint server for biology · 2024Article
- Dynamic control of sequential retrieval speed in networks with heterogeneous learning rules.eLife · 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
- Differences in the consolidation by spontaneous and evoked ripples in the presence of active dendrites.PLoS computational biology · 2024Article
- Anti-Hebbian plasticity drives sequence learning in striatum.Communications biology · 2024Article
- Rapid memory encoding in a recurrent network model with behavioral time scale synaptic plasticity.PLoS computational biology · 2023Article
- Spiking Recurrent Neural Networks Represent Task-Relevant Neural Sequences in Rule-Dependent Computation.Cognitive computation · 2023Article
- Bayesian reconstruction of memories stored in neural networks from their connectivity.PLoS computational biology · 2023Article
- Self-healing codes: How stable neural populations can track continually reconfiguring neural representations.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Thunderstruck: The ACDC model of flexible sequences and rhythms in recurrent neural circuits.PLoS computational biology · 2022Article
- Metastable attractors explain the variable timing of stable behavioral action sequences.Neuron · 2022Article
- Chimera Patterns of Synchrony in a Frustrated Array of Hebb Synapses.Frontiers in computational neuroscience · 2022Article
- Nonlinear transient amplification in recurrent neural networks with short-term plasticity.eLife · 2021Article
- Drifting assemblies for persistent memory: Neuron transitions and unsupervised compensation.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- From synapse to network: models of information storage and retrieval in neural circuits.Current opinion in neurobiology · 2021Review
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3 authors.
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Abstract
Sequential activity has been observed in multiple neuronal circuits across species, neural structures, and behaviors. It has been hypothesized that sequences could arise from learning processes. However, it is still unclear whether biologically plausible synaptic plasticity rules can organize neuronal activity to form sequences whose statistics match experimental observations. Here, we investigate temporally asymmetric Hebbian rules in sparsely connected recurrent rate networks and develop a theory of the transient sequential activity observed after learning. These rules transform a sequence of random input patterns into synaptic weight updates. After learning, recalled sequential activity is reflected in the transient correlation of network activity with each of the stored input patterns. Using mean-field theory, we derive a low-dimensional description of the network dynamics and compute the storage capacity of these networks. Multiple temporal characteristics of the recalled sequential activity are consistent with experimental observations. We find that the degree of sparseness of the recalled sequences can be controlled by nonlinearities in the learning rule. Furthermore, sequences maintain robust decoding, but display highly labile dynamics, when synaptic connectivity is continuously modified due to noise or storage of other patterns, similar to recent observations in hippocampus and parietal cortex. Finally, we demonstrate that our results also hold in recurrent networks of spiking neurons with separate excitatory and inhibitory populations.
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