ArticleeLife2018
Learning recurrent dynamics in spiking networks.
Article in eLife, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Taming the chaos gently: a predictive alignment learning rule in recurrent neural networks.Nature communications · 2025Article
- Article
- Geometry of population activity in spiking networks with low-rank structure.PLoS computational biology · 2023Article
- Intrinsic neural diversity quenches the dynamic volatility of neural networks.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Distributing task-related neural activity across a cortical network through task-independent connections.Nature communications · 2023Article
- Article
- A scalable implementation of the recursive least-squares algorithm for training spiking neural networks.Frontiers in neuroinformatics · 2023Article
- Input correlations impede suppression of chaos and learning in balanced firing-rate networks.PLoS computational biology · 2022Article
- Pruning recurrent neural networks replicates adolescent changes in working memory and reinforcement learning.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Intrinsic bursts facilitate learning of Lévy flight movements in recurrent neural network models.Scientific reports · 2022Article
- Consciousness is supported by near-critical slow cortical electrodynamics.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Learning the Synaptic and Intrinsic Membrane Dynamics Underlying Working Memory in Spiking Neural Network Models.Neural computation · 2021Article
- Dendritic normalisation improves learning in sparsely connected artificial neural networks.PLoS computational biology · 2021Article
- Training Spiking Neural Networks in the Strong Coupling Regime.Neural computation · 2021Article
- Target spike patterns enable efficient and biologically plausible learning for complex temporal tasks.PloS one · 2021Article
- Embodied working memory during ongoing input streams.PloS one · 2021Article
- Dynamical Characteristics of Recurrent Neuronal Networks Are Robust Against Low Synaptic Weight Resolution.Frontiers in neuroscience · 2021Article
- Optimal learning with excitatory and inhibitory synapses.PLoS computational biology · 2020Article
- Nonlinear stimulus representations in neural circuits with approximate excitatory-inhibitory balance.PLoS computational biology · 2020Article
- Simple framework for constructing functional spiking recurrent neural networks.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
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Authors and funding
2 authors.
Funding
Abstract
Spiking activity of neurons engaged in learning and performing a task show complex spatiotemporal dynamics. While the output of recurrent network models can learn to perform various tasks, the possible range of recurrent dynamics that emerge after learning remains unknown. Here we show that modifying the recurrent connectivity with a recursive least squares algorithm provides sufficient flexibility for synaptic and spiking rate dynamics of spiking networks to produce a wide range of spatiotemporal activity. We apply the training method to learn arbitrary firing patterns, stabilize irregular spiking activity in a network of excitatory and inhibitory neurons respecting Dale's law, and reproduce the heterogeneous spiking rate patterns of cortical neurons engaged in motor planning and movement. We identify sufficient conditions for successful learning, characterize two types of learning errors, and assess the network capacity. Our findings show that synaptically-coupled recurrent spiking networks possess a vast computational capability that can support the diverse activity patterns in the brain.
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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.