My research involves computational modeling and mathematical analysis of neural networks. We develop theoretical approaches to understanding circuit dynamics and information processing in sensory systems. Currently, I am interested in how different task and stimulus contexts change neuronal responses and the implications on neural coding, with an emphasis on neural variability. Spike trains from individual neurons are highly variable in response to repeated stimulus. The shared variability among neurons reflects the underlying circuitry and the dynamical state of the network. By studying neural variability in network models, we identify circuit features that shape the dimensional structure of neuronal responses. In addition, how highly variable neural responses supports stable percept remains a puzzle. By combining information theory and network modeling, we investigate the context-dependence of neural computation and behavior.
C. Huang, D.A. Ruff, R. Pyle, R. Rosenbaum, M.R. Cohen and B. Doiron (2019) Circuit models of low dimensional shared variability in cortical networks. Neuron 101, 1-12.
C. Huang and B. Doiron (2017) Once upon a (slow) time in the land of recurrent neuronal networks. Curr Opin Neurobiol 46:31-38.
C. Huang and J. Rinzel (2016) A neuronal network model for pitch selectivity and representation. Front Comput Neurosci 10:57.
C. Huang, B. Englitz, S. Shamma and J. Rinzel (2015). A neuronal network model for biasing ambiguous pitch comparison. Frontiers Comput Neurosci 9:101.