Computational neuroscience is a specific framework to approach questions about how the brain works. It focuses on building models that emulate neural function, as well as on developing and implementing advanced analysis tools for interpreting neural and behavioral data. It is a broad field with a wide range of methods, from classical biophysics to Bayesian methods and the recently blooming area of artificial neural networks. Because brain function is extraordinarily complex, theoretical and computational neuroscience provide a powerful testing ground—generating new hypotheses that inspire experiments and uncovering hidden principles within data. In some cases, theory and computation reveal how emergent processes connect molecules, cells, and behavior, offering a framework to understand how genetic alterations in proteins can ultimately shape human behavior.
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Assistant Professor Visual cortical coding and circuit neurophysiology |
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Assistant Professor Computational Neuroscience |
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Assistant Professor Computational Neuroscience |
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Assistant Professor Dendritic integration and visual processing in the retina |