ArticlePLoS computational biology2022
Input correlations impede suppression of chaos and learning in balanced firing-rate networks.
Article in PLoS computational biology, 2022. 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.
- Entropy, Inhibition and Memory in Balanced Spiking Reservoirs.Entropy (Basel, Switzerland) · 2026Article
- Desegregation of neuronal predictive processing.Nature communications · 2026Article
- Noise-Limited Failure of OGY Chaos Control in Regulating Monosynaptic Reflex Variability in the In Vivo Cat Spinal Cord.NeuroSci · 2026Article
- Balanced state of networks of winner-take-all units.PLoS computational biology · 2025Article
- Exploring Flip Flop memories and beyond: training Recurrent Neural Networks with key insights.Frontiers in systems neuroscience · 2024Article
- Multitasking via baseline control in recurrent neural networks.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
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Authors and funding
5 authors.
Funding
Abstract
Neural circuits exhibit complex activity patterns, both spontaneously and evoked by external stimuli. Information encoding and learning in neural circuits depend on how well time-varying stimuli can control spontaneous network activity. We show that in firing-rate networks in the balanced state, external control of recurrent dynamics, i.e., the suppression of internally-generated chaotic variability, strongly depends on correlations in the input. A distinctive feature of balanced networks is that, because common external input is dynamically canceled by recurrent feedback, it is far more difficult to suppress chaos with common input into each neuron than through independent input. To study this phenomenon, we develop a non-stationary dynamic mean-field theory for driven networks. The theory explains how the activity statistics and the largest Lyapunov exponent depend on the frequency and amplitude of the input, recurrent coupling strength, and network size, for both common and independent input. We further show that uncorrelated inputs facilitate learning in balanced networks.
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Registered trials
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