is a Senior Fellow of the Design Futures Council. He was elected to the National Academy of Medicine in 2008. In 2010 he was elected a Member of the National Academy of Sciences (NAS), and elected to the National Academy of Engineering in 2011. In 2017 he was elected to the National Academy of Inventors. These achievements place him in a group of only three living people to have been elected to all four of the national academies. In 2013 he was elected to the American Academy of Arts and Sciences and was elected Fellow of the American Physical Society in 2014. He was awarded the 2015 Swartz Prize for Theoretical and Computational Neuroscience from the Society for Neuroscience. He received an Honorary Doctorate from the University of Zurich in 2017. In 2022 he was awarded the Gruber Neuroscience Prize. Neural networks His research in neural networks and computational neuroscience has been pioneering. In the early 1980s, particularly following work by John Hopfield, computer simulations of neural networks became widespread. Early applications, particularly by Sejnowski and Geoffrey Hinton, demonstrated that simple neural networks could be made to learn tasks of at least some sophistication. In 1989, Sejnowski founded Neural Computation, published by the MIT Press, the leading journal in neural networks and computational neuroscience. He is also the President of the Neural Information Processing Systems Foundation, a non-profit organization that oversees the annual NeurIPS Conference. This interdisciplinary meeting brings together researchers from many disciplines, including biology, physics, mathematics, and engineering. He co-invented the Boltzmann machine with Geoffrey Hinton and pioneered the application of learning algorithms to difficult problems in speech (NETtalk) and vision. His postdoc, Tony Bell, developed the infomax algorithm for Independent Component Analysis (ICA) which has been widely adopted in machine learning, signal processing and data mining. Research The long-range goal of 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. Hippocampal and cortical slice preparations are being used to explore the properties of single neurons and synapses, including the precision of spike firing and the influence of neuromodulators. Biophysical models of electrical and chemical signal processing within neurons are used as an adjunct to physiological experiments. New