Literature DB >> 2986532

Oscillatory neural networks.

A I Selverston, M Moulins.   

Abstract

Despite the fact that a large number of neuronal oscillators have been described, there are only a few good examples that illustrate how they operate at the cellular level. For most, there is some isolated information about different aspects of the oscillator network, but too little to explain the whole mechanism. Two quite remarkable features do seem to be emerging from ongoing studies, however. One is that there are very few generalizable features common to neural oscillators. Many utilize reciprocal inhibitory circuits and endogenous burst-generating currents to some extent. All that have been well worked out utilize a combination of both cellular and network properties, but little else in the way of common mechanism is noteworthy. Perhaps the most interesting aspect of recent work is the ability of a particular oscillator to produce a large repertoire of different outputs. This is separate and in addition to changes occurring via phasic sensory feedback. It is in fact a radical functional "rewiring" of the network in response to neuromodulators. The CPG circuits represent only the most basic form of a given pattern. Finally, concerning the role of sensory feedback in generating oscillatory patterns, the concept of the CPG as a group of neurons able to produce oscillatory patterns without any sensory feedback is, in our opinion, still valid. There is no doubt that some oscillators may be quite weak when isolated, but they can still produce bursts with firing sequences similar to those seen in vivo. The fact that sensory feedback can both control and enhance the oscillations has never been in doubt. Similarly, entrainment of the pattern by sensory feedback does not mean that the receptor is part of the generator, only that it has access to it (as do command and coordinating fibers). The real question remains: Can a group of cells produce an oscillatory pattern without phasic sensory input? We must answer this affirmatively even for the insect-flight motor CPG, while emphasizing the fact that for this system sensory feedback plays a larger role than in most other CPGs. Most neural oscillators will probably fall on some continuum between those like insect flight, which need and use a large amount of phasic feedback, and those that can oscillate in a near-normal manner without it.

Mesh:

Year:  1985        PMID: 2986532     DOI: 10.1146/annurev.ph.47.030185.000333

Source DB:  PubMed          Journal:  Annu Rev Physiol        ISSN: 0066-4278            Impact factor:   19.318


  59 in total

1.  Spatiotemporal patterns of activity in an intact mammalian network with single-cell resolution: optical studies of nicotinic activity in an enteric plexus.

Authors:  A L Obaid; T Koyano; J Lindstrom; T Sakai; B M Salzberg
Journal:  J Neurosci       Date:  1999-04-15       Impact factor: 6.167

2.  In vitro analog of operant conditioning in aplysia. I. Contingent reinforcement modifies the functional dynamics of an identified neuron.

Authors:  R Nargeot; D A Baxter; J H Byrne
Journal:  J Neurosci       Date:  1999-03-15       Impact factor: 6.167

3.  Rebound from Inhibition: Self-Correction against Neurodegeneration?

Authors:  Shobhana Sivaramakrishnan; William P Lynch
Journal:  J Clin Cell Immunol       Date:  2017-03-13

4.  Computer simulation of the segmental neural network generating locomotion in lamprey by using populations of network interneurons.

Authors:  J Hellgren; S Grillner; A Lansner
Journal:  Biol Cybern       Date:  1992       Impact factor: 2.086

Review 5.  Neurophysiological and computational principles of cortical rhythms in cognition.

Authors:  Xiao-Jing Wang
Journal:  Physiol Rev       Date:  2010-07       Impact factor: 37.312

6.  A mathematical modeling study of inter-segmental coordination during stick insect walking.

Authors:  Silvia Daun-Gruhn
Journal:  J Comput Neurosci       Date:  2010-06-22       Impact factor: 1.621

7.  An inter-segmental network model and its use in elucidating gait-switches in the stick insect.

Authors:  Silvia Daun-Gruhn; Tibor Istvan Tóth
Journal:  J Comput Neurosci       Date:  2010-12-17       Impact factor: 1.621

Review 8.  Neuronal control of swimming behavior: comparison of vertebrate and invertebrate model systems.

Authors:  Olivia J Mullins; John T Hackett; James T Buchanan; W Otto Friesen
Journal:  Prog Neurobiol       Date:  2010-11-18       Impact factor: 11.685

Review 9.  Signaling in large-scale neural networks.

Authors:  Rune W Berg; Jørn Hounsgaard
Journal:  Cogn Process       Date:  2008-11-14

Review 10.  Auditory-vocal mirroring in songbirds.

Authors:  Richard Mooney
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2014-04-28       Impact factor: 6.237

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