Literature DB >> 10400940

Dynamic control of irregular bursting in an identified neuron of an oscillatory circuit.

R C Elson1, R Huerta, H D Abarbanel, M I Rabinovich, A I Selverston.   

Abstract

In the oscillatory circuits known as central pattern generators (CPGs), most synaptic connections are inhibitory. We have assessed the effects of inhibitory synaptic input on the dynamic behavior of a component neuron of the pyloric CPG in the lobster stomatogastric ganglion. Experimental perturbations were applied to the single, lateral pyloric neuron (LP), and the resulting voltage time series were analyzed using an entropy measure obtained from power spectra. When isolated from phasic inhibitory input, LP generates irregular spiking-bursting activity. Each burst begins in a relatively stereotyped manner but then evolves with exponentially increasing variability. Periodic, depolarizing current pulses are poor regulators of this activity, whereas hyperpolarizing pulses exert a strong, frequency-dependent regularizing action. Rhythmic inhibitory inputs from presynaptic pacemaker neurons also regularize the bursting. These inputs 1) reset LP to a similar state at each cycle, 2) extend and further stabilize the initial, quasi-stable phase of its bursts, and 3) at sufficiently high frequencies terminate ongoing bursts before they become unstable. The dynamic time frame for stabilization overlaps the normal frequency range of oscillations of the pyloric CPG. Thus, in this oscillatory circuit, the interaction of rhythmic inhibitory input with intrinsic burst properties affects not only the phasing, but also the dynamic stability of neural activity.

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Mesh:

Year:  1999        PMID: 10400940     DOI: 10.1152/jn.1999.82.1.115

Source DB:  PubMed          Journal:  J Neurophysiol        ISSN: 0022-3077            Impact factor:   2.714


  14 in total

1.  State-dependent effects of Na channel noise on neuronal burst generation.

Authors:  Peter F Rowat; Robert C Elson
Journal:  J Comput Neurosci       Date:  2004 Mar-Apr       Impact factor: 1.621

Review 2.  A neural infrastructure for rhythmic motor patterns.

Authors:  Allen I Selverston
Journal:  Cell Mol Neurobiol       Date:  2005-03       Impact factor: 5.046

3.  Artificial synaptic modification reveals a dynamical invariant in the pyloric CPG.

Authors:  Marcelo B Reyes; Ramón Huerta; Mikhail I Rabinovich; Allen I Selverston
Journal:  Eur J Appl Physiol       Date:  2007-12-13       Impact factor: 3.078

4.  Functional phase response curves: a method for understanding synchronization of adapting neurons.

Authors:  Jianxia Cui; Carmen C Canavier; Robert J Butera
Journal:  J Neurophysiol       Date:  2009-05-06       Impact factor: 2.714

5.  Control and synchronization of laser bursting and its implications in neuroscience.

Authors:  F Tito Arecchi; Riccardo Meucci; Kais Al Naimee; Francesco Salvadori
Journal:  Cogn Process       Date:  2008-12-02

6.  Transistor analogs of emergent iono-neuronal dynamics.

Authors:  Guy Rachmuth; Chi-Sang Poon
Journal:  HFSP J       Date:  2008-04-18

7.  Predictions of phase-locking in excitatory hybrid networks: excitation does not promote phase-locking in pattern-generating networks as reliably as inhibition.

Authors:  Fred H Sieling; Carmen C Canavier; Astrid A Prinz
Journal:  J Neurophysiol       Date:  2009-04-08       Impact factor: 2.714

8.  Variability of bursting patterns in a neuron model in the presence of noise.

Authors:  Paul Channell; Ibiyinka Fuwape; Alexander B Neiman; Andrey L Shilnikov
Journal:  J Comput Neurosci       Date:  2009-06-20       Impact factor: 1.621

9.  A modeling approach on why simple central pattern generators are built of irregular neurons.

Authors:  Marcelo Bussotti Reyes; Pedro Valadão Carelli; José Carlos Sartorelli; Reynaldo Daniel Pinto
Journal:  PLoS One       Date:  2015-03-23       Impact factor: 3.240

10.  Probing the dynamics of identified neurons with a data-driven modeling approach.

Authors:  Thomas Nowotny; Rafael Levi; Allen I Selverston
Journal:  PLoS One       Date:  2008-07-09       Impact factor: 3.240

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