Our goal is to develop a comprehensive framework for understanding synchronization in circuits containing biological neurons, where synchronization is broadly defined to include all phase-locked rhythmic activity. We will focus on two types of biological exemplars of neural oscillators: repetitively spiking neurons with a gradual dependence of the firing frequency on stimulus current, and bursting neurons that apparently have a single slow variable. A large number of neural oscillators are likely to fall into one of these two categories, hence these results should be general and useful in order to compare and contrast phase resetting and phase locking in single spike firing versus bursting neurons. The experimental preparations were chosen because they contain spiking or bursting neurons that are easily identified, readily isolated, and oscillate with minimal variability. This makes them an optimal in vitro proving ground for answering the following questions, using theoretical methods based on phase resetting curves (PRCs): Are PRCs sufficient to predict phase locking and convergence in pairs of coupled spiking neurons? Can PROs predict the existence and stability of m:n phase-locking modes? Can the PRC in response to excitatory stimuli be predicted from the voltage trajectory of a bursting neuron? Can we predict the activity of pairs of bursting neurons from their PROs for excitatory coupling and for variable burst durations? The answers should have wide applicability in the study of central pattern generators that produce repetitive motor activity, as well as to the collective synchronization phenomena underlying various aspects of cognition. Epileptic seizures are associated with excessive synchronization in certain brain regions, as is the tremor associated with Parkinson's disease. A better understanding of the general mechanisms of synchronization may eventually suggest new therapeutic approaches for these diseases. ? ?

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Research Project (R01)
Project #
5R01NS054281-04
Application #
7435293
Study Section
Special Emphasis Panel (ZRG1-IFCN-B (50))
Program Officer
Liu, Yuan
Project Start
2005-09-15
Project End
2010-01-31
Budget Start
2008-06-01
Budget End
2010-01-31
Support Year
4
Fiscal Year
2008
Total Cost
$347,714
Indirect Cost
Name
Louisiana State Univ Hsc New Orleans
Department
Neurosciences
Type
Schools of Medicine
DUNS #
782627814
City
New Orleans
State
LA
Country
United States
Zip Code
70112
Canavier, Carmen C; Tikidji-Hamburyan, Ruben A (2017) Globally attracting synchrony in a network of oscillators with all-to-all inhibitory pulse coupling. Phys Rev E 95:032215
Norman, Sharon E; Butera, Robert J; Canavier, Carmen C (2016) Stochastic slowly adapting ionic currents may provide a decorrelation mechanism for neural oscillators by causing wander in the intrinsic period. J Neurophysiol 116:1189-98
Hooper, Ryan M; Tikidji-Hamburyan, Ruben A; Canavier, Carmen C et al. (2015) Feedback control of variability in the cycle period of a central pattern generator. J Neurophysiol 114:2741-52
Tikidji-Hamburyan, Ruben A; Martínez, Joan José; White, John A et al. (2015) Resonant Interneurons Can Increase Robustness of Gamma Oscillations. J Neurosci 35:15682-95
Canavier, Carmen C (2015) Phase-resetting as a tool of information transmission. Curr Opin Neurobiol 31:206-13
Soofi, Wafa; Prinz, Astrid A (2015) Differential effects of conductances on the phase resetting curve of a bursting neuronal oscillator. J Comput Neurosci 38:539-58
Tikidji-Hamburyan, Ruben; Lin, Eric C; Gasparini, Sonia et al. (2014) Effect of heterogeneity and noise on cross frequency phase-phase and phase-amplitude coupling. Network 25:38-62
Thounaojam, Umeshkanta S; Cui, Jianxia; Norman, Sharon E et al. (2014) Slow noise in the period of a biological oscillator underlies gradual trends and abrupt transitions in phasic relationships in hybrid neural networks. PLoS Comput Biol 10:e1003622
Canavier, Carmen C; Wang, Shuoguo; Chandrasekaran, Lakshmi (2013) Effect of phase response curve skew on synchronization with and without conduction delays. Front Neural Circuits 7:194
Wang, Shuoguo; Musharoff, Maximilian M; Canavier, Carmen C et al. (2013) Hippocampal CA1 pyramidal neurons exhibit type 1 phase-response curves and type 1 excitability. J Neurophysiol 109:2757-66

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