ArticlePLoS computational biology2021
Learning compositional sequences with multiple time scales through a hierarchical network of spiking neurons.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Competition and cooperation of assembly sequences in recurrent neural networks.PLoS computational biology · 2025Article
- Learning predictive signals within a local recurrent circuit.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Long- and short-term history effects in a spiking network model of statistical learning.Scientific reports · 2023Article
- Toward reproducible models of sequence learning: replication and analysis of a modular spiking network with reward-based learning.Frontiers in integrative neuroscience · 2023Article
- Voltage-dependent synaptic plasticity: Unsupervised probabilistic Hebbian plasticity rule based on neurons membrane potential.Frontiers in neuroscience · 2022Article
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Authors and funding
3 authors.
Funding
Abstract
Sequential behaviour is often compositional and organised across multiple time scales: a set of individual elements developing on short time scales (motifs) are combined to form longer functional sequences (syntax). Such organisation leads to a natural hierarchy that can be used advantageously for learning, since the motifs and the syntax can be acquired independently. Despite mounting experimental evidence for hierarchical structures in neuroscience, models for temporal learning based on neuronal networks have mostly focused on serial methods. Here, we introduce a network model of spiking neurons with a hierarchical organisation aimed at sequence learning on multiple time scales. Using biophysically motivated neuron dynamics and local plasticity rules, the model can learn motifs and syntax independently. Furthermore, the model can relearn sequences efficiently and store multiple sequences. Compared to serial learning, the hierarchical model displays faster learning, more flexible relearning, increased capacity, and higher robustness to perturbations. The hierarchical model redistributes the variability: it achieves high motif fidelity at the cost of higher variability in the between-motif timings.
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