The goal of the proposed work is to build a theoretical framework based on phase resetting curves (PRC) for understanding the basic mechanisms of synchronization and phase locking in networks of biological neurons. These phenomena underlie central pattern generation for the production of repetitive motor activity such as locomotion and respiration. Transiently synchronized assemblies of neurons are hypothesized to underlie many cognitive functions, and synchronization is altered in psychiatric disorders such as schizophrenia. Pathological synchronization is involved in epilepsy and tremor. PRCs measure the change in length of a periodic cycle due to an input perturbation to an isolated neuron, and provide useful information regarding the activity of the neuron within a network context. During the previous progress period, two major approaches were explored as theoretical substrates: 1: the phase resetting curves using a spike (or burst) as a perturbation under the assumption of pulsatile coupling and 2) a functional PRC (FPRC) consisting of repeated stimulation with a similar perturbation applied at a fixed delay after each spike (or burst) initiation. The overall goal of the project is to create a very broad theoretical framework that can be used to analyze the biological basis of coordinated oscillatory activity in the nervous system. Therefore we need to develop and test methods to analyze circuits that include neurons exhibiting spike frequency adaptation, neurons that are not intrinsic oscillators, noisy circuits, and circuits whose parameters cannot be defined in detail for every component neuron. The following objectives will be accomplished in the next project period: we will develop and test PRC-based methods to include neurons that are not endogenous pacemakers, to analyze circuits of adapting neurons, and to estimate the robustness of synchronization in the presence of noise and heterogeneity. We will analyze a biological central pattern generator, the pyloric circuit of the lobster and crab, using the methods that we develop. General principles will emerge that should be applicable to many oscillatory networks in the nervous system. A better understanding of these mechanisms will provide tools to determine how synchronization goes awry in neurological disorders such as epilepsy and tremor, or in psychiatric disorders in which cognition is impaired.
The purpose of this work is to understand basic mechanisms of synchronization and pattern formation in the nervous system. Networks of biological neurons can spontaneously generate patterned activity that underlies rhythmic, repetitive motor behaviors such as respiration and locomotion, and transient synchronization in cortical networks is hypothesized to mediate cognitive functions such as attention, perception, and information storage and retrieval. A better understanding of these mechanisms will provide tools to determine how synchronization goes awry in neurological disorders such as epilepsy and tremor, or in psychiatric disorders in which cognition is impaired.
|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|
Showing the most recent 10 out of 24 publications