Neural Comp. Sign up for ETOCS
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
 QUICK SEARCH:   [advanced]


     


This Article
Right arrow Full Text
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Oprisan, S. A.
Right arrow Articles by Canavier, C. C.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Oprisan, S. A.
Right arrow Articles by Canavier, C. C.
(Neural Computation. 2002;14:1027-1057.)
© 2002 The MIT Press


Letter

The Influence of Limit Cycle Topology on the Phase Resetting Curve

Sorinel A. Oprisan

soprisan{at}uno.edu, Department of Psychology, University of New Orleans, New Orleans, LA 70148, U.S.A.

Carmen C. Canavier

ccanavie{at}uno.edu, Department of Psychology, University of New Orleans, New Orleans, LA 70148, U.S.A.

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.




This article has been cited by other articles:


Home page
Neural Comput.Home page
B. Pfeuty, G. Mato, D. Golomb, and D. Hansel
The Combined Effects of Inhibitory and Electrical Synapses in Synchrony
Neural Comput., March 1, 2005; 17(3): 633 - 670.
[Abstract] [Full Text] [PDF]


Home page
J. Neurophysiol.Home page
T. I. Netoff, M. I. Banks, A. D. Dorval, C. D. Acker, J. S. Haas, N. Kopell, and J. A. White
Synchronization in Hybrid Neuronal Networks of the Hippocampal Formation
J Neurophysiol, March 1, 2005; 93(3): 1197 - 1208.
[Abstract] [Full Text] [PDF]


Home page
Biophys. JHome page
S. A. Oprisan, V. Thirumalai, and C. C. Canavier
Dynamics from a Time Series: Can We Extract the Phase Resetting Curve from a Time Series?
Biophys. J., May 1, 2003; 84(5): 2919 - 2928.
[Abstract] [Full Text] [PDF]




HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
J COGNITIVE NEUROSCIENCE NEURAL COMPUTATION MIT PRESS JOURNALS
Copyright © 2002 by The MIT Press.