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

Biosketch

Terrence Sejnowski is the Francis Crick Professor at The Salk Institute for Biological Studies, where he directs the Computational Neurobiology Laboratory, and a Distinguished Professor of Biology and Computer Science and Engineering at the University of California, San Diego, where he is co-Director of the Institute for Neural Computation. The long-range goal of Dr. Sejnowski's research is to understand the computational resources of brains and to build linking principles from brain to behavior using computational models. This goal is being pursued with a combination of theoretical and experimental approaches at several levels of investigation ranging from the biophysical level to the systems level. His laboratory has developed new methods for analyzing the sources for electrical and magnetic signals recorded from the scalp and fMRI brain imaging by blind source separation using independent components analysis (ICA) and delay-differential analysis (DDA). Dr. Sejnowski has published over 500 scientific papers and 12 books, including The Deep Learning Revolution in 2018. He received the Hebb Prize from the International Neural Network Society in 1999, and the IEEE Neural Network Pioneer Award in 2002. He is a member of the National Academy of Sciences, the National Academy of Engineering and the National Academy of Medicine.

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).

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