Reliable circuits from irregular neurons: A dynamical approach to understanding central pattern generators

Allen I. Selverston, Mikhail I. Rabinovich, Henry D.I. Abarbanel, Robert Elson, Attila Szücs, Reynaldo D. Pinto, Ramón Huerta, Pablo Varona

Research output: Contribution to journalReview article

58 Citations (Scopus)

Abstract

Central pattern generating neurons from the lobster stomatogastric ganglion were analyzed using new nonlinear methods. The LP neuron was found to have only four or five degrees of freedom in the isolated condition and displayed chaotic behavior. We show that this chaotic behavior could be regularized by periodic pulses of negative current injected into the neuron or by coupling it to another neuron via inhibitory connections. We used both a modified Hindmarsh-Rose model to simulate the neurons behavior phenomenologically and a more realistic conductance-based model so that the modeling could be linked to the experimental observations. Both models were able to capture the dynamics of the neuron behavior better than previous models. We used the Hindmarsh-Rose model as the basis for building electronic neurons which could then be integrated into the biological circuitry. Such neurons were able to rescue patterns which had been disabled by removing key biological neurons from the circuit.

Original languageEnglish
Pages (from-to)357-374
Number of pages18
JournalJournal of Physiology Paris
Volume94
Issue number5-6
DOIs
Publication statusPublished - Jan 1 2000

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Keywords

  • Central pattern generator
  • Chaotic behavior
  • Neural modeling
  • Nonlinear systems electronic neurons

ASJC Scopus subject areas

  • Neuroscience(all)
  • Physiology (medical)

Cite this

Selverston, A. I., Rabinovich, M. I., Abarbanel, H. D. I., Elson, R., Szücs, A., Pinto, R. D., Huerta, R., & Varona, P. (2000). Reliable circuits from irregular neurons: A dynamical approach to understanding central pattern generators. Journal of Physiology Paris, 94(5-6), 357-374. https://doi.org/10.1016/S0928-4257(00)01101-3