Literature DB >> 11972906

The influence of limit cycle topology on the phase resetting curve.

Sorinel A Oprisan1, Carmen C Canavier.   

Abstract

Understanding the phenomenology of phase resetting is an essential step toward developing a formalism for the analysis of circuits composed of bursting neurons that receive multiple, and sometimes overlapping, inputs. If we are to use phase-resetting methods to analyze these circuits, we can either generate phase-resetting curves (PRCs) for all possible inputs and combinations of inputs, or we can develop an understanding of how to construct PRCs for arbitrary perturbations of a given neuron. The latter strategy is the goal of this study. We present a geometrical derivation of phase resetting of neural limit cycle oscillators in response to short current pulses. A geometrical phase is defined as the distance traveled along the limit cycle in the appropriate phase space. The perturbations in current are treated as displacements in the direction corresponding to membrane voltage. We show that for type I oscillators, the direction of a perturbation in current is nearly tangent to the limit cycle; hence, the projection of the displacement in voltage onto the limit cycle is sufficient to give the geometrical phase resetting. In order to obtain the phase resetting in terms of elapsed time or temporal phase, a mapping between geometrical and temporal phase is obtained empirically and used to make the conversion. This mapping is shown to be an invariant of the dynamics. Perturbations in current applied to type II oscillators produce significant normal displacements from the limit cycle, so the difference in angular velocity at displaced points compared to the angular velocity on the limit cycle must be taken into account. Empirical attempts to correct for differences in angular velocity (amplitude versus phase effects in terms of a circular coordinate system) during relaxation back to the limit cycle achieved some success in the construction of phase-resetting curves for type II model oscillators. The ultimate goal of this work is the extension of these techniques to biological circuits comprising type II neural oscillators, which appear frequently in identified central pattern-generating circuits.

Mesh:

Year:  2002        PMID: 11972906     DOI: 10.1162/089976602753633376

Source DB:  PubMed          Journal:  Neural Comput        ISSN: 0899-7667            Impact factor:   2.026


  17 in total

1.  Dynamics from a time series: can we extract the phase resetting curve from a time series?

Authors:  S A Oprisan; V Thirumalai; C C Canavier
Journal:  Biophys J       Date:  2003-05       Impact factor: 4.033

2.  Annihilation of single cell neural oscillations by feedforward and feedback control.

Authors:  Flavio Fröhlich; Saso Jezernik
Journal:  J Comput Neurosci       Date:  2004 Sep-Oct       Impact factor: 1.621

3.  Biophysical basis of the phase response curve of subthalamic neurons with generalization to other cell types.

Authors:  Michael A Farries; Charles J Wilson
Journal:  J Neurophysiol       Date:  2012-07-11       Impact factor: 2.714

4.  Stability of two cluster solutions in pulse coupled networks of neural oscillators.

Authors:  Lakshmi Chandrasekaran; Srisairam Achuthan; Carmen C Canavier
Journal:  J Comput Neurosci       Date:  2010-08-20       Impact factor: 1.621

5.  Beyond two-cell networks: experimental measurement of neuronal responses to multiple synaptic inputs.

Authors:  Theoden I Netoff; Corey D Acker; Jonathan C Bettencourt; John A White
Journal:  J Comput Neurosci       Date:  2005-06       Impact factor: 1.621

6.  Efficient estimation of phase-resetting curves in real neurons and its significance for neural-network modeling.

Authors:  Roberto F Galán; G Bard Ermentrout; Nathaniel N Urban
Journal:  Phys Rev Lett       Date:  2005-04-19       Impact factor: 9.161

7.  Using phase resetting to predict 1:1 and 2:2 locking in two neuron networks in which firing order is not always preserved.

Authors:  Selva K Maran; Carmen C Canavier
Journal:  J Comput Neurosci       Date:  2007-06-19       Impact factor: 1.621

8.  Loss of phase-locking in non-weakly coupled inhibitory networks of type-I model neurons.

Authors:  Myongkeun Oh; Victor Matveev
Journal:  J Comput Neurosci       Date:  2008-08-09       Impact factor: 1.621

9.  Rate constants rather than biochemical mechanism determine behaviour of genetic clocks.

Authors:  Emery Conrad; Avraham E Mayo; Alexander J Ninfa; Daniel B Forger
Journal:  J R Soc Interface       Date:  2008-08-06       Impact factor: 4.118

10.  A new approach for determining phase response curves reveals that Purkinje cells can act as perfect integrators.

Authors:  Elena Phoka; Hermann Cuntz; Arnd Roth; Michael Häusser
Journal:  PLoS Comput Biol       Date:  2010-04-29       Impact factor: 4.475

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