Terrence J. Sejnowski

Salk Institute for Biological Studies


Election Year: 2010
Primary Section: 28, Systems Neuroscience
Secondary Section: 34, Computer and Information Sciences
Membership Type: Member

Research Interests

The long-range goal of my research is to build linking principles from brain to behavior using computational models. Starting with relatively simple models of neural networks and learning algorithms, I demonstrated that they nonetheless had powerful computational capabilities. A combination of theoretical and experimental approaches in my laboratory ranging from biophysical models of single synapses and neurons to dynamic network models were then used to explore a wide range of neural systems. The central issues being addressed are how dendrites integrate synaptic signals in neurons, how neural circuits generate behavior, and how learning and sleep adaptively modify these circuits. In particular, neural model were proposed for how dopamine neurons predict future rewards, how songbirds learn their songs, how sleep spindles are generated in the thalamus, and how fluctuating synpatic inputs give rise to reliable patterns in spike trains. Fast-spiking parvalbumin-positive interneurons are currently the focus of both computational and experimental studies of attention in the visual cortex and dysfunction in schizophrenia. Synapses are explored with Monte Carlo methods (MCell) and brain activity is analyzed with the independent components analysis (ICA).

Powered by Blackbaud
nonprofit software