Literature DB >> 9663552

Estimation of coupling strength in regenerated lamprey spinal cords based on a stochastic phase model.

T Kiemel1, A H Cohen.   

Abstract

We present a simple stochastic model of two coupled phase oscillators and a method of fitting the model to experimental spike-train data or to sequences of burst times. We apply the method to data from lesioned isolated lamprey spinal cords. The remaining tracts at the lesion site are either regenerated medial tracts, regenerated lateral tracts, control medial tracts, or control lateral tracts. We show that regenerated tracts on average provide significantly weaker coupling than control tracts. We compare our model-dependent estimate of coupling strength to a measure of coordination based on the size of deflections in the spike-train cross-correlation histogram (CCH). Using simulated data, we show that our estimates are able to detect changes in coupling strength that do not change the size of deflections in the CCH. Our estimates are also more resistant to changes in the level of dynamic noise and to changes in relative oscillator frequency than is the CCH. In simulations with high levels of dynamic noise and in one experimental preparation, we are able detect significant coupling strength although there are no significant deflections in the CCH.

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Year:  1998        PMID: 9663552     DOI: 10.1023/a:1008835011799

Source DB:  PubMed          Journal:  J Comput Neurosci        ISSN: 0929-5313            Impact factor:   1.621


  9 in total

1.  A computer-based model for realistic simulations of neural networks. II. The segmental network generating locomotor rhythmicity in the lamprey.

Authors:  P Wallén; O Ekeberg; A Lansner; L Brodin; H Tråvén; S Grillner
Journal:  J Neurophysiol       Date:  1992-12       Impact factor: 2.714

2.  Phase coupling by synaptic spread in chains of coupled neuronal oscillators.

Authors:  T L Williams
Journal:  Science       Date:  1992-10-23       Impact factor: 47.728

3.  Neural network simulations of coupled locomotor oscillators in the lamprey spinal cord.

Authors:  J T Buchanan
Journal:  Biol Cybern       Date:  1992       Impact factor: 2.086

4.  Evidence for functional regeneration in the adult lamprey spinal cord following transection.

Authors:  A H Cohen; M T Baker; T A Dobrov
Journal:  Brain Res       Date:  1989-09-04       Impact factor: 3.252

5.  Neuronal spike trains and stochastic point processes. II. Simultaneous spike trains.

Authors:  D H Perkel; G L Gerstein; G P Moore
Journal:  Biophys J       Date:  1967-07       Impact factor: 4.033

6.  Correlational analysis of fictive swimming in the lamprey reveals strong functional intersegmental coupling.

Authors:  N Mellen; T Kiemel; A H Cohen
Journal:  J Neurophysiol       Date:  1995-03       Impact factor: 2.714

7.  The neuronal correlate of locomotion in fish. "Fictive swimming" induced in an in vitro preparation of the lamprey spinal cord.

Authors:  A H Cohen; P Wallén
Journal:  Exp Brain Res       Date:  1980       Impact factor: 1.972

8.  Quantification, smoothing, and confidence limits for single-units' histograms.

Authors:  M Abeles
Journal:  J Neurosci Methods       Date:  1982-05       Impact factor: 2.390

9.  The nature of the coupling between segmental oscillators of the lamprey spinal generator for locomotion: a mathematical model.

Authors:  A H Cohen; P J Holmes; R H Rand
Journal:  J Math Biol       Date:  1982       Impact factor: 2.259

  9 in total
  6 in total

1.  Estimating the strength and direction of functional coupling in the lamprey spinal cord.

Authors:  Tim Kiemel; Kevin M Gormley; Li Guan; Thelma L Williams; Avis H Cohen
Journal:  J Comput Neurosci       Date:  2003 Sep-Oct       Impact factor: 1.621

2.  Intersegmental coordination of cockroach locomotion: adaptive control of centrally coupled pattern generator circuits.

Authors:  Einat Fuchs; Philip Holmes; Tim Kiemel; Amir Ayali
Journal:  Front Neural Circuits       Date:  2011-01-20       Impact factor: 3.492

3.  Extending transfer entropy improves identification of effective connectivity in a spiking cortical network model.

Authors:  Shinya Ito; Michael E Hansen; Randy Heiland; Andrew Lumsdaine; Alan M Litke; John M Beggs
Journal:  PLoS One       Date:  2011-11-15       Impact factor: 3.240

4.  Intersegmental coupling and recovery from perturbations in freely running cockroaches.

Authors:  Einat Couzin-Fuchs; Tim Kiemel; Omer Gal; Amir Ayali; Philip Holmes
Journal:  J Exp Biol       Date:  2015-01-15       Impact factor: 3.312

5.  Nonlinear muscles, passive viscoelasticity and body taper conspire to create neuromechanical phase lags in anguilliform swimmers.

Authors:  T McMillen; T Williams; P Holmes
Journal:  PLoS Comput Biol       Date:  2008-08-29       Impact factor: 4.475

6.  Functional Recovery of a Locomotor Network after Injury: Plasticity beyond the Central Nervous System.

Authors:  Joshua G Puhl; Anthony W Bigelow; Mara C P Rue; Karen A Mesce
Journal:  eNeuro       Date:  2018-07-11
  6 in total

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