
Brian DePasquale
Assistant Professor, Biomedical Engineering
The DePasquale lab develops mathematical models to understand how populations of neurons perform computations to produce behavior. Broadly, we take two approaches. One is data-driven: we collaborate with experimental neuroscientists to develop tailored machine learning models of neural activity to identify the algorithms that drive behaviors such as decision-making or movement. Our second approach is theoretical: we construct and analyze artificial neural network models to understand how their structure gives rise to analogous computations and other functional features observed in biological neural circuits. A key area of interest has been understanding how structured connections between neurons produce network activity that is both coherent and irregular, a common yet paradoxical feature of neural responses.
Profile Google Scholar- Expertise
- Computational Neuroscience, Learning & Memory, Molecular & Cellular Mechanisms, and Neurophotonics
- Departments (Colleges)
- Biomedical Engineering (ENG)