Literature DB >> 7807198

Synchronization properties of spindle oscillations in a thalamic reticular nucleus model.

D Golomb1, X J Wang, J Rinzel.   

Abstract

1. We address the hypothesis of Steriade and colleagues that the thalamic reticular nucleus (RE) is a pacemaker for thalamocortical spindle oscillations by developing and analyzing a model of a large population of all-to-all coupled inhibitory RE neurons. 2. Each RE neuron has three ionic currents: a low-threshold T-type Ca2+ current (ICa-T), a calcium-activated potassium current (IAHP) and a leakage current (IL). ICa-T underlies a cell's postinhibitory rebound properties, whereas IAHP hyperpolarizes the neuron after a burst. Each neuron, which is a conditional oscillator, is coupled to all other RE neurons via fast gamma-aminobutyric acid-A (GABAA) and slow GABAB synapses. 3. For generating network oscillations IAHP may not be necessary. Synaptic inhibition can provide the hyperpolarization for deinactivating ICa-T that causes bursting if the reversal potentials for GABAA and GABAB synapses are sufficiently negative. 4. If model neurons display sufficiently powerful rebound excitability, an isolated RE network of such neurons oscillates with partial but typically not full synchrony. The neurons spontaneously segregate themselves into several macroscopic clusters. The neurons within a cluster follow the same time course, but the clusters oscillate differently from one another. In addition to activity patterns in which clusters burst sequentially (e.g., 2 or 3 clusters bursting alternately), a two-cluster state may occur with one cluster active and one quiescent. Because the neurons are all-to-all coupled, the cluster states do not have any spatial structure. 5. We have explored the sensitivity of such partially synchronized patterns to heterogeneity in cells' intrinsic properties and to simulated neuroelectric noise. Although either precludes precise clustering, modest levels of heterogeneity or noise lead to approximate clustering of active cells. The population-averaged voltage may oscillate almost regularly but individual cells burst at nearly every second cycle or less frequently. The active-quiescent state is not robust at all to heterogeneity or noise. Total asynchrony is observed when heterogeneity or noise is too large, e.g., even at 25% heterogeneity for our reference set of parameter values. 6. The fast GABAA inhibition (with a reversal potential more negative than, say, -65 mV) favors the cluster states and prevents full synchrony. Our simulation results suggest two mechanisms that can fully synchronize the isolated RE network model. With GABAA removed or almost totally blocked, GABAB inhibition (because it is slow) can lead to full synchrony, which is partially robust to heterogeneity and noise.(ABSTRACT TRUNCATED AT 400 WORDS)

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Year:  1994        PMID: 7807198     DOI: 10.1152/jn.1994.72.3.1109

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


  49 in total

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2.  Decoding temporal information: A model based on short-term synaptic plasticity.

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3.  Synchronous clusters in a noisy inhibitory neural network.

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4.  Localized bumps of activity sustained by inhibition in a two-layer thalamic network.

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Journal:  J Comput Neurosci       Date:  2001 May-Jun       Impact factor: 1.621

5.  Dynamics of spiking neurons connected by both inhibitory and electrical coupling.

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Journal:  J Comput Neurosci       Date:  2003 May-Jun       Impact factor: 1.621

6.  Clustering in small networks of excitatory neurons with heterogeneous coupling strengths.

Authors:  Yue-Xian Li; Yu-Qing Wang; Robert Miura
Journal:  J Comput Neurosci       Date:  2003 Mar-Apr       Impact factor: 1.621

7.  Synchronization of strongly coupled excitatory neurons: relating network behavior to biophysics.

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Journal:  J Comput Neurosci       Date:  2003 Jul-Aug       Impact factor: 1.621

8.  Oscillations in large-scale cortical networks: map-based model.

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9.  A positive feedback at the cellular level promotes robustness and modulation at the circuit level.

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Journal:  J Neurophysiol       Date:  2015-08-26       Impact factor: 2.714

10.  Mathematical Frameworks for Oscillatory Network Dynamics in Neuroscience.

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Journal:  J Math Neurosci       Date:  2016-01-06       Impact factor: 1.300

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