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Cited by 454Neural dynamics and brain functionNeural Networks and ApplicationsChaos control and synchronization

Robust timing and motor patterns by taming chaos in recurrent neural networks

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Laje, Rodrigo, Buonomano, Dean V·2013·Nature Neuroscience
DOI 10.1038/nn.3405PMC PMC3753043
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Abstract

The brain's ability to tell time and produce complex spatiotemporal motor patterns is critical for anticipating the next ring of a telephone or playing a musical instrument. One class of models proposes that these abilities emerge from dynamically changing patterns of neural activity generated in recurrent neural networks. However, the relevant dynamic regimes of recurrent networks are highly sensitive to noise; that is, chaotic. We developed a firing rate model that tells time on the order of seconds and generates complex spatiotemporal patterns in the presence of high levels of noise. This is achieved through the tuning of the recurrent connections. The network operates in a dynamic regime that exhibits coexisting chaotic and locally stable trajectories. These stable patterns function as 'dynamic attractors' and provide a feature that is characteristic of biological systems: the ability to 'return' to the pattern being generated in the face of perturbations.

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  • A recurrent neural network is difficult to train and control because it may exhibit chaotic behavior.

    “A recurrent neural network is also known to be difficult to train and control because it may exhibit chaotic behavior (Sompolinsky et al., 1988; Sussillo and Abbott, 2009; Laje and Buonomano, 2013; Ben-Shushan and Tsodyks, 2017).”

    From The Cerebro-Cerebellum as a Locus of Forward Model: A Review (2020) · cites this paper as Laje and Buonomano, 2013

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Authors
Laje, R., & Buonomano, D. V.
Affiliations
University of California, Los Angeles (US)
Corresponding author
Buonomano, Dean V
ORCID
Venue
Nature Neuroscience
Publisher
Springer Science and Business Media LLC
Published
2013-05-26
Volume
16
Issue
7
Pages
925-933
ISSN
  • 1097-6256 (ISSN-L)
  • 1546-1726
DOI
10.1038/nn.3405
PMC
PMC3753043
OpenAlex
W1661597557
Cited by
454
References
3
Citations by year
2026: 27 · 2025: 33 · 2024: 28 · 2023: 49 · 2022: 39 · 2021: 37 · 2020: 40 · 2019: 43 · 2018: 33 · 2017: 39 · 2016: 45 · 2015: 21 · 2014: 16 · 2013: 4
FWCI
14.09
Type
journal article
Language
en
Open access
Licence
www.springer.com/tdm (tdm)
Locations
  • Nature Neuroscience (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • CONICET Digital (CONICET) (submittedVersion, cc-by-nc-sa)
Funders
  • National Science Foundation (1114833, MH60163)
  • Consejo Nacional de Investigaciones Científicas y Técnicas
  • National Institute of Mental Health (MH-60163)
  • National Institute of Neurological Disorders and Stroke (R03NS077340, NS077340)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
  • Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Chaos control and synchronization
Keywords
recurrent neural networks, timekeeping, chaos, noise robustness, firing rate model, dynamic attractors, chaotic dynamics, perturbation stability, biological timekeeping
MeSH terms
Action Potentials, Animals, Computer Simulation, Humans, Learning, Models, Neurological, Motor Activity, Nerve Net, Neurons, Synapses, Time Perception, Nonlinear Dynamics

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