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Department of Cognitive Science
Johns Hopkins University
A high-dimensional view of computational neuroscience
Brains and artificial neural networks represent information in population codes, defined by the activity patterns of many neurons. Understanding the statistical principles that govern these population codes is critical to the paradigms of cognitive computational neuroscience. In this talk, I will present converging lines of work from machine learning and neuroscience that reveal the statistical underpinnings of neural populations from the perspective of their principal components. Key findings include the surprisingly high-dimensional latent structure of population codes in human visual cortex and neural network models of vision and the universality of latent dimensions in diverse vision systems. The statistical framework presented here identifies general principles of neural representation that abstract over the lower-level details of biological and artificial neural networks, and it points toward a simplifying statistical theory of cortical sensory representation.
A pizza lunch will be served. Please bring your own beverage.