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Structured neural variability and its roles in neural computation

Cristina Savin, New York University
Tuesday March 30, 12-1pm
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Abstract: Across brain regions and species, one key feature of neural activity is that responses are highly variable. Computationally, it has been argued one of the main challenges of brain information processing is compensating for its internal noise. However, this interpretation is challenged by experimental data: in many contexts the brain seems to actively put itself in a dynamic regime where responses are highly variable, which suggests that there may be computational advantages to having a seemingly ‘noisy’ brain. In this talk I will review a combination of recent theory and experimental data analysis demonstrating how intrinsic noise can be recruited for useful computational goals, in particular for routing information across brain regions and reward dependent learning.