UCL Ear Institute
332 Gray's Inn Rd
The Ear Institute
Faculty of Brain Sciences
My research is focused on understanding how the brain represents and processes information about the outside world. My primary interest is hearing and, in particular, understanding how single neurons and small neural networks contribute to auditory function and dysfunction. What are the neural mechanisms that enable the brain to separate multiple sound sources and solve the so-called ‘cocktail party problem?’ How are these mechanisms impaired by peripheral and central damage due to aging and noise exposure? How can we better correct or compensate for these impairments to restore normal perception?
My research group uses a combination of in vivo electrophysiology and computational modelling to address these questions. We perform studies in rodents to gain a detailed understanding of the processing that takes place throughout the central auditory pathway from the brainstem to the cortex. We are based in the Ear Institute, where we collaborate with a number of partners in basic and clinical auditory science and neuroscience.
For more detailed information about our research, please visit our lab homepage:
I also have an interest in the science of eating. I have written an open-access book that describes the scientific basis of a healthy diet for a general audience. The book synthesizes results from recent studies in nutrition and metabolism, as well as neuroscience, immunology and microbiology. I have an active program of engagement related to healthy eating that includes regular events for the public.
For more information about the science of healthy eating, please visit:
Doctor of Philosophy
Bachelor of Science
|Massachusetts Institute of Technology|
I began my research career as a PhD student with Garrett Stanley at Harvard University, where I studied how visual information is encoded in the spiking activity of neurons in the thalamus. I investigated the role of the thalamic ‘burst-mode’ and adaptation to stimulus contrast and correlations during the processing of natural visual scenes. In parallel with these experimental studies, I also developed several computational approaches for the analysis of neural activity patterns to assess the role of adaptive filtering. This work shaped my perspective on a number of issues that remain central to my research, such as the importance of studying sensory processing under natural conditions, the power of combining experimental and modeling approaches across multiple levels of detail, and the importance (and pleasure) of pursuing ambitious agendas through multi-lab collaborative efforts.
After finishing my PhD, I received a short fellowship to study the encoding of visual information in the retina under Toshihiko Hosoya at the RIKEN Brain Science Institute, and then worked for several years as a postdoctoral researcher in the Bernstein Center for Computational Neuroscience at the Ludwig-Maximilians-University Munich with Benedikt Grothe. During this time, I completed several studies of the auditory midbrain focusing on the encoding of sound intensity and location. My experience in Munich was critical as it provided my foundational training in auditory neuroscience and expanded my perspective on sensory processing to include consideration of broader ecological context. It was also during this time that I began to develop the techniques for recording activity from networks of neurons that still form the basis of my experimental research today.
I started my own research group in Munich with support from the Emmy Noether Programme of the German Research Foundation in 2009, and moved to UCL as a Wellcome Trust Research Career Development Fellow in 2010. My research at UCL has been primarily focused on understanding how information about speech is encoded in the activity patterns of networks of neurons in the central auditory pathway. The representation of information at the level of neuronal networks is particularly important for hearing, because the nature of speech is such that there is no single feature that provides a consistently reliable basis for separating multiple voices; this is the difficulty that underlies the famous cocktail party problem. As a result, the internal neural representations of each voice must be distributed across networks of neurons that are sensitive to different sound features, and these representations must be constantly modified to distinguish the voice of interest from the rest.
Our work began with the development of new tools to facilitate the analysis of network activity patterns. We then carried out experimental studies of population coding in the inferior colliculus (IC) and primary auditory cortex (A1). These studies revealed a clear transformation at the network level between IC and A1; IC neurons are independent encoders with high temporal precision and broad spatial tuning, while A1 neurons are strongly correlated (at least in some brain states) with low temporal precision and sharp spatial tuning. Together, these results provide a comprehensive description of the network-level representation of speech in the healthy auditory system and serve as a basis for our ongoing and future studies of the effects of hearing loss on speech perception and development of related technologies and therapies.