Terrence Sejnowski

When we listen to a lecture or read a book, each new word is interpreted in the temporal context of previous words. This is accomplished in a large language model with a long input vector, which keeps a running record of all previous input and output words. Temporal context is also needed for long-term working memory, which draws from long-term memory to interpret the input stream, form associations, and make decisions. These cognitive functions will be explored in cortical models of recurrent neural networks that support dynamical traveling waves.

Terrence Sejnowski is the Francis Crick Chair at the Salk Institute and a Distinguished Professor of Neurobiology at UC San Diego.  He pioneered Computational Neuroscience and neural network learning algorithms.  He is also a leader in NeuroAI, the recent convergence between neuroscience and AI.  He is a member of the National Academies of Sciences, Engineering, Medicine, and Inventors.  He was awarded the Brain Prize in 2024, and was elected a Fellow of the Royal Society in 2025.