Encyclopedia of Computational Neuroscience

Living Edition
| Editors: Dieter Jaeger, Ranu Jung

Comparative Analysis of Half-Center Central Pattern Generators (CPGs)

  • Jonathan E. RubinEmail author
Living reference work entry

Latest version View entry history

DOI: https://doi.org/10.1007/978-1-4614-7320-6_39-2

Definition

CPGs are networks of neurons that can produce rhythmic activity, featuring a stereotyped sequence of phases that repeats in a periodic-like manner, without receiving rhythmic inputs from an outside source. Half-center CPGs, also known as half-center oscillators, are CPGs consisting of two components connected by reciprocal synaptic inhibition. While the coupled network exhibits a two-phase pattern, with one component exhibiting a high activity level and the other at a low activity level in each phase, the individual components are not able to switch between sustained high and low activity phases in isolation. Comparative analysis of half-center CPGs is the study of how specific features of half-center CPGs translate into particular properties.

Detailed Description

A half-center CPG consists of a pair of components and the coupling between them. Each component may be an individual neuron or a population of neurons. In a half-center oscillation, each component switches back...

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References

  1. Ausborn J, Snyder A, Shevtsova N, Rybak I, Rubin J (2018) State-dependent rhythmo-genesis and frequency control in a half-center locomotor CPG. J Neurophysiol 119:96–117CrossRefGoogle Scholar
  2. Curtu R, Shpiro A, Rubin N, Rinzel J (2008) Mechanisms for frequency control in neuronal competition models. SIAM J Appl Dyn Syst 7:609–649CrossRefGoogle Scholar
  3. Daun S, Rubin JE, Rybak IA (2009) Control of oscillation periods and phase durations in half-center central pattern generators: a comparative mechanistic analysis. J Comput Neurosci 27:3–36CrossRefGoogle Scholar
  4. Ermentrout GB, Terman D (2010) Mathematical foundations of neuroscience. Springer, New YorkCrossRefGoogle Scholar
  5. Hooper S (2001) Central pattern generators. In: Encyclopedia of life sciences. Wiley. http://onlinelibrary.wiley.com/book/10.1002/047001590X. Accessed 17 Aug 2013
  6. Izhikevich E (2007) Dynamical systems in neuroscience: the geometry of excitability and bursting. MIT Press, CambridgeGoogle Scholar
  7. Rubin J, Smith J (2019) Robustness of respiratory rhythm generation across dynamic regimes. PLoS Comp Biol 15:e1006860CrossRefGoogle Scholar
  8. Rybak IA, Shevtsova NA, Lafreniere-Roula M, McCrea DA (2006) Modeling spinal circuitry involved in locomotor pattern generation: insights from deletions during fictive locomotion. J Physiol 577:617–639CrossRefGoogle Scholar
  9. Skinner FK, Turrigiano GG, Marder E (1993) Frequency and burst duration in oscillating neurons and two-cell networks. Biol Cybern 69:375–383CrossRefGoogle Scholar
  10. Skinner FK, Kopell N, Marder E (1994) Mechanisms for oscillation and frequency control in reciprocally inhibitory model neural networks. J Comput Neurosci 1:69–87CrossRefGoogle Scholar
  11. Wang X-J, Rinzel J (1992) Alternating and synchronous rhythms in reciprocally inhibitory model neurons. Neural Comput 4:84–97CrossRefGoogle Scholar
  12. Yakovenko S, McCrea DA, Stecina K, Prochazka A (2005) Control of locomotor cycle durations. J Neurophysiol 94:1057–1065CrossRefGoogle Scholar
  13. Zhang C, Lewis TJ (2013) Phase response properties of half-center oscillators. J Comput Neurosci 35:55–74CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of MathematicsUniversity of PittsburghPittsburghUSA

Section editors and affiliations

  • Jessica Ausborn
    • 1
  • Ilya Rybak
    • 2
  1. 1.Department of Neurobiology and AnatomyDrexel University, College of MedicinePhiladelphiaUSA
  2. 2.Department of Neurobiology and AnatomyDrexel University College of MedicinePhiladelphiaUSA