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Computer simulation of the segmental neural network generating locomotion in lamprey by using populations of network interneurons
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.ORCID iD: 0000-0002-0550-0739
1992 (English)In: Biological cybernetics, ISSN 0340-1200, E-ISSN 1432-0770, Vol. 68, 1-13 p.Article in journal (Refereed) Published
Abstract [en]

Realistic computer simulations of the experimentally established local spinal cord neural network generating swimming in the lamprey have been performed. Populations of network interneurons were used in which cellular properties, like cell size and membrane conductance including voltage dependent ion channels were randomly distributed around experimentally obtained mean values, as were synaptic conductances (kainate/AMPA, NMDA, glycine) and delays. This population model displayed more robust burst activity over a wider frequency range than the more simple subsample model used previously, and the pattern of interneuronal activity was appropriate. The strength of the reciprocal inhibition played a very important role in the regulation of burst frequency, and just by changing the inhibitory bias the entire physiological range could be covered. At the lower frequency range of bursting the segmental excitatory interneurons provide stability as does the activation of voltage dependent NMDA receptors. Spike frequency adaptation by means of summation of afterhyperpolarization (AHP) serves as a major burst terminating factor, and at lower rates the membrane properties conferred by the NMDA receptor activation. The lateral interneurons were not of critical importance for the burst termination. They may, however, be of particular importance for inducing a rapid burst termination during for instance steering and righting reactions. Several cellular factors combine to provide a secure and stable motor pattern in the entire frequency range.

Place, publisher, year, edition, pages
1992. Vol. 68, 1-13 p.
National Category
Computer and Information Science
URN: urn:nbn:se:kth:diva-58751OAI: diva2:473935
NR 20140805Available from: 2012-01-08 Created: 2012-01-08Bibliographically approved

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