More than a century’s worth of behavioural investigations have demonstrated that animals and humans process sensory information close to optimally, often employing subtle and powerful algorithms to do so. Our understanding of these computations at the neural level is, by contrast, quite simplistic. The goal of research in my group is to help bridge this gap, using both theoretical and data-driven approaches to understand how information is represented in neural systems, and how this representation underlies computation and learning. On the one hand, we collaborate closely with physiologists to advance the technology of neural data collection and analysis. These studies have the potential to introduce powerful new theoretically-motivated ways of looking at neural data. At the same time, we examine neural information representation and perceptual behaviour from a more theoretical point of view, addressing questions of how the brain might encode the richness of information needed to explain perceptual capabilities, what purpose might be served by adaptation in neural activities, and how experience-driven plasticity in representations is related to perceptual learning. Both the data analytic and the theoretical aspects of our neuroscience research are closely connected to the field of machine learning, which provides the tools needed for the first, and a structural framework for the second.

Current and Recent Projects

Theoretical Neuroscience

Theoretical research in my group is built around a key idea in modern theoretical neuroscience: that the act of perception is a search for the causal elements most likely to account for the activity of the sensory epithelia. This ”statistical inference” hypothesis, traceable to the work of von Helmholtz and before, has far-reaching consequences for the way we study and think about brain function, and work in my group has explored many of its different facets.

Neural Data Modelling

Ultimately, any attempt to link theory to neural circuits will demand a sophisticated understanding of both representation and dynamics within real neural systems. Thus, the group maintains substantial collaborations with neurophysiologists. Our goals in these collaborations are both technological and scientific—developing the algorithms needed to make sense of the flood of neurophysiological data available, while at the same time gaining insight into representations and computations in the brain.

Machine Learning and Signal Processing

At the heart of our work lie the methods of probabilistic (or Bayesian) modelling, particularly as they apply to the fields of Machine Perception and Learning, and Signal Processing. Some of our work is directed more specifically at understanding and developing these tools.

Older work

My Ph.D. Thesis on Latent Variable Models for Neural Data Analysis is available from this page. It covers a range of statistical modelling and data analytic topics, including:

I have also worked on

A selected list of publications appears on the next page.

version of April 9, 2013

List of Publications
Publications by topic
 Natural Statistics
 Other Neural Theory
 Neural Dynamics
 Neural Encoding
 Neural Decoding
 Machine Learning
 Signal Processing