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J Neurophysiol. 2009 Jan;101(1):372-86. doi: 10.1152/jn.01290.2007. Epub 2008 Jul 2.

Activity and neuromodulatory input contribute to the recovery of rhythmic output after decentralization in a central pattern generator.

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Federated Department of Biological Sciences, Rutgers University-Newark, Newark, NJ, USA.

Erratum in

  • J Neurophysiol. 2009 Apr;101(4):2194. Golowaschi, Jorge [corrected to Golowasch, Jorge].


Central pattern generators (CPGs) are neuronal networks that control vitally important rhythmic behaviors including breathing, heartbeat, and digestion. Understanding how CPGs recover activity after their rhythmic activity is disrupted has important theoretical and practical implications. Previous experimental and modeling studies indicated that rhythm recovery after central neuromodulatory input loss (decentralization) could be based entirely on activity-dependent mechanisms, but recent evidence of long-term conductance regulation by neuromodulators suggest that neuromodulator-dependent mechanisms may also be involved. Here we examined the effects of altering activity and the neuromodulatory environment before decentralization of the pyloric CPG in Cancer borealis on the initial phase of rhythmic activity recovery after decentralization. We found that pretreatments altering the network activity through shifting the ionic balance or the membrane potential of pyloric pacemaker neurons reduced the delay of recovery initiation after decentralization, consistent with the recovery process being triggered already during the pretreatment period through an activity-dependent mechanism. However, we observed that pretreatment with neuromodulators GABA and proctolin, acting via metabotropic receptors, also affected the initial phase of the recovery of pyloric activity after decentralization. Their distinct effects appear to result from interactions of their metabotropic effects with their effects on neuronal activity. Thus we show that the initial phase of the recovery process can be accounted for by the existence of distinct activity-and neuromodulator-dependent pathways. We propose a computational model that includes activity- and neuromodulator-dependent mechanisms of the activity recovery process, which successfully explains the experimental observations and predicts the results of key biological experiments.

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