Encyclopedia of Computational Neuroscience

Living Edition
| Editors: Dieter Jaeger, Ranu Jung

Connectionist Models of CPG Networks

  • Sharmila VenugopalEmail author
Living reference work entry
DOI: https://doi.org/10.1007/978-1-4614-7320-6_122-1

Central Pattern Generators (CPGs) are neural networks that can produce coordinated patterns of rhythmic activity to orchestrate repetitive behaviors such as feeding, locomotion, and respiration. CPGs are known to be composed of phylogenetically conserved connectional units that are typically organized into chains to enable coordinated activation of the effector organs (muscles). Through integration of experimental and computational approaches at the cellular and network levels, essential connectional elements of CPGs have been revealed (Grillner 2006).

Network Topology: At the connectional level, a CPG network consists of “unit CPGs” representing the nodes of the network, and the reciprocal connections between unit CPGs correspond to the branches of the network. Physiologically, a unit CPG represents a group of neurons that can generate a recurrent burst of activity (Grillner 2006).

Simple inputs such as tonic excitation can produce rhythmic outputs in these networks. Increasing the...

Keywords

Central Pattern Generator Connectionist Model Biophysical Model Brute Force Method Connectional Unit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References

  1. Buchanan JT (1992) Neural network simulations of coupled locomotor oscillators in the lamprey spinal cord. Biol Cybern 66:367–374PubMedCrossRefGoogle Scholar
  2. Collins JJ, Richmond SA (1994) Hard-wired central pattern generators for quadrupedal locomotion. Biol Cybern 71:375–385CrossRefGoogle Scholar
  3. Grillner S, Kozlov A, Kotaleski JH (2005) Integrative neuroscience: linking levels of analyses. Curr Opin Neurobiol 15:614–621PubMedCrossRefGoogle Scholar
  4. Grillner S (2006) Biological pattern generation: the cellular and computational logic of networks in motion. Neuron 52:751–766PubMedCrossRefGoogle Scholar
  5. Ijspeert AJ (2008) Central pattern generators for locomotion control in animals and robots: a review. Neural Netw 21:642–653PubMedCrossRefGoogle Scholar
  6. Jung R, Kiemel T, Cohen AH (1996) Dynamic behavior of a neural network model of locomotor control in the lamprey. J Neurophysiol 75(3):1074–1086PubMedGoogle Scholar
  7. Nasse J, Terman D, Venugopal S, Hermann G, Rogers R, Travers JB (2008) Local circuit input to the medullary reticular formation from the rostral nucleus of the solitary tract. Am J Physiol Regul Integr Comp Physiol 295(5):R1391–R1408PubMedCrossRefGoogle Scholar
  8. Prinz AA, Bucher D, Marder E (2004) Similar network activity from disparate circuit parameters. Nat Neurosci 7(12):1345–1352PubMedCrossRefGoogle Scholar
  9. Venugopal S, Travers JB, Terman DH (2007) A computational model for motor pattern switching between taste-induced ingestion and rejection oromotor behaviors. J Comput Neurosci 22(2):223–238PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Department of Physiology, David Geffen School of MedicineUniversity of California Los AngelesLos AngelesUSA