Literature DB >> 21547616

Control of multistability in ring circuits of oscillators.

C C Canavier1, D A Baxter, J W Clark, J H Byrne.   

Abstract

The essential dynamics of some biological central pattern generators (CPGs) can be captured by a model consisting of N neurons connected in a ring. These circuits, like many oscillatory nonlinear circuits of sufficient complexity, are capable of multistability, that is, of generating different firing patterns distinguished by the phasic relationships between the firing in each circuit element (neuron). Moreover, a shift in firing pattern can be induced by a transient perturbation. A systematic approach, based on phase-response curve (PRC) theory, was used to determine the optimum timing for perturbations that induce a shift in the firing pattern. The first step was to visualize the solution space of the ring circuit, including the attractive basins for each stable firing pattern; this was possible using the relative phase of N-1 oscillators, with respect to an arbitrarily selected reference oscillator, as coordinate axes. The trajectories in this phase space were determined using an iterative mapping based only on the PRCs of the uncoupled component oscillators; this algorithm was called a circuit emulator. For an accurate mapping of the attractive basin of each pattern exhibited by the ring circuit, the emulator had to take into account the effect of a perturbation or input on the timing of two bursts following the onset of the perturbation, rather than just one. The visualization of the attractive basins for rings of two, three, and four oscillators enabled the accurate prediction of the amounts of phase resetting applied to up to N-1 oscillators within a cycle that would induce a transition from any pattern to any another pattern. Finally, the timing and synaptic characterization of an input called the switch signal was adjusted to produce the desired amount of phase resetting.

Year:  1999        PMID: 21547616     DOI: 10.1007/s004220050507

Source DB:  PubMed          Journal:  Biol Cybern        ISSN: 0340-1200            Impact factor:   2.086


  16 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.  Phase resetting and phase locking in hybrid circuits of one model and one biological neuron.

Authors:  S A Oprisan; A A Prinz; C C Canavier
Journal:  Biophys J       Date:  2004-10       Impact factor: 4.033

3.  Phase resetting reduces theta-gamma rhythmic interaction to a one-dimensional map.

Authors:  Paola Malerba; Nancy Kopell
Journal:  J Math Biol       Date:  2012-04-21       Impact factor: 2.259

4.  Phase-response curves and synchronized neural networks.

Authors:  Roy M Smeal; G Bard Ermentrout; John A White
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2010-08-12       Impact factor: 6.237

5.  Non-weak inhibition and phase resetting at negative values of phase in cells with fast-slow dynamics at hyperpolarized potentials.

Authors:  Myongkeun Oh; Victor Matveev
Journal:  J Comput Neurosci       Date:  2010-12-04       Impact factor: 1.621

6.  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

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.  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.  Phase resetting curves allow for simple and accurate prediction of robust N:1 phase locking for strongly coupled neural oscillators.

Authors:  Carmen C Canavier; Fatma Gurel Kazanci; Astrid A Prinz
Journal:  Biophys J       Date:  2009-07-08       Impact factor: 4.033

10.  When two wrongs make a right: synchronized neuronal bursting from combined electrical and inhibitory coupling.

Authors:  Reimbay Reimbayev; Kevin Daley; Igor Belykh
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2017-06-28       Impact factor: 4.226

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