Perception is an ill-posed problem. Many of the sights and sounds we perceive on a regular basis are ambiguous, yet we regularly identify objects consistently by sight from a variety of angles, in multiple contexts, and even when partially occluded or in poor lighting conditions. Because of this ambiguity, perception is necessarily an act of inference, as recognized by Helmholtz in the 19th century, which combines prior knowledge with data to produce estimates about characteristics of the physical world. The neural systems that govern perception must therefore encode this prior knowledge and provide a mechanism for incorporating data from low-level sensory systems. My research interests are in identifying and understanding the mechanisms and principles that the nervous system uses to perform the inferences which allow us to perceive the world. I am particularly interested in neural implementations of Bayesian inference and mechanisms by which prior knowledge is encoded as well as the implications that coding efficiency has on the structure of neural circuits. I also wish to understand how network structure relates to network activity and how that activity corresponds to the statistics of stimuli. An important component of this endeavor is understanding the characteristics of stimuli that perceptual systems evolved to efficiently interpret, how those characteristics are represented in cortex, and how (and to what extent) they can be decoded.

Expertise

• Theoretical & computational neuroscience


• Statistical mechanics of neural networks


• Machine learning

Research Programs

• Neural coding