Statistical modeling

With modern neurophysiological methods able to record neural activity throughout the visual pathway in the context of arbitrarily complex visual stimulation, our understanding of visual system function is becoming lim- ited by the available models of visual neurons that can be directly related to such data. Different forms of statistical models are now being used to probe the cellular and circuit mechanisms shaping neural activity, understand how neural selectivity to complex visual features is computed, and derive the ways in which neurons contribute to systems-level visual processing. However, models that are able to more accurately reproduce observed neural activity often defy simple interpretations. As a result, rather than being used solely to connect with existing theories of visual processing, statistical modeling will increasingly drive the evolution of more sophisticated theories.

We specialize in two main approaches in statistical modeling: (1) supervised "feed-forward" models that predicts neural responses direcly from known experimental covariances, such as the stimulus; and (2) latent variable models that infer unobserved shared factors that can explain some aspects of neural activity across populations. Often the second approach benefits from detailed feedforward models, and together can be leveraged to develop a full picture of representations of sensory and behavioral variables across neural populations.

Supervised statistical models

Latent variable models