GS140033   The Synaptic Basis for Learning and Memory: A Theoretical and Computational Approach

Shouval, Harel. Three semester hours. Spring annually. Prerequisites: one semester of college level calculus and linear algebra as well as some programming experience.

Synaptic plasticity is the change in synaptic connections between two neurons due to the activity of one or both of these neurons. It is believed to be the basis of learning memory and some forms of brain development. The course will study both abstract models and biophysical models of synaptic plasticity. Abstract models of synaptic plasticity demonstrate how the concept of synaptic plasticity can contribute to different forms of learning, memory and development and how this might contribute to machine learning. Biophysical models of synaptic plasticity are based on actual cellular and molecular mechanisms observed in neurons and demonstrate how synaptic plasticity can arise from real biological mechanisms. The class will also have guest lectures from experimentalists working in this field. Students taking the course should have taken an introductory neuroscience and a college level class in Calculus and Linear algebra. Most homework problems are Matlab based.