CAREER:Prediction of Synchrony and Phase-Locked Modes in Neural Networks based on Stimulus Time Resetting Curve

College Of Charleston, Charleston SC

Investigators

Abstract

Nervous systems are composed of complex networks of neurons, which involve chemical and electrical signal lines. These neural networks send signals within the brain and to muscles controlling bodily actions such as walking and breathing. Although each neuron is also complex, for the purpose of studying firing patterns in large neural networks a given cell can be modeled as a pacemaker that fires at regular intervals. When a neuron receives signals or inputs, from other neurons, it maps them into measurable changes of its firing activity or signaling rate, and passes this output to the next neuron. The focus of this research is to understand how the input-output, or resetting curve, of individual neurons and the coupling between them can generate complex firing patterns in large networks. New computer algorithms for classifying and storing resetting curves generated both by model and experimental neurons will be developed. The resetting curves will be used for predicting the spectrum of possible firing patterns of neural networks made of such neurons. The stability of numerically predicted firing patterns and the mechanisms leading to firing mode switch will be experimentally tested. The broader impacts of this project include possible new solutions for predicting the emergent coherent firing pattern and its stability, at the network level, based on the resetting curves of individual neurons. Studying how the nervous system processes and responds to external stimuli will aid our understanding of how neural networks function, including the human nervous system and its related disorders. Educationally, the project will attract undergraduate students into the interdisciplinary field of computational neuroscience. A seminar in Computational Biology for incoming freshmen coupled with an upper-level class exploring computational models of neurons and their networks will offer a mentored pathway to related careers. Undergraduate students will also benefit from in-depth research experience throughout the project.