"Neural codes for the sense of taste" with Dr Mark Stopfer

Past Event

Date
30 October 2017, 1:00pm - 2:00pm

Location
Lecture Theatre, Oxford Martin School
34 Broad Street (corner of Holywell and Catte Streets), Oxford, OX1 3BD

This lecture is organised by the Programme on Mind and Machine and The Centre for Neural Circuits and Behaviour

Four of the five major sensory systems (vision, olfaction, somatosensation, and audition) are thought to use different but partially overlapping sets of neurons to form unique representations of vast numbers of stimuli. Gustation is considered an exception, by representing only small numbers of basic taste categories. Using new methods for delivering tastant chemicals and making electrophysiological recordings from the gustatory system of the moth Manduca sexta, we found that chemical-specific information is initially encoded in the population of gustatory receptor neurons as broadly distributed spatiotemporal patterns of activity, dramatically integrated and temporally transformed as it propagates to monosynaptically connected second-order neurons, and observed in tastant-specific behaviour. Our results suggest that the gustatory system, rather than constructing basic taste categories, uses a spatiotemporal population code to generate unique neural representations of individual tastant chemicals.

For further information, please contact Fiona Woods at fiona.woods@cncb.ox.ac.uk


About the speaker

Mark Stopfer received his BS and PhD degrees from Yale University, where, with Tom Carew, he applied behavioural and electrophysiological techniques to study mechanisms underlying simple forms of learning. He then joined Gilles Laurent's laboratory at the California Institute of Technology, where he examined the information processing properties that emerge within ensembles of neurons, focusing particularly upon oscillatory and synchronous neural activity. Dr Stopfer came to NIH in 2002. His laboratory studies neural ensemble mechanisms underlying sensory coding in relatively simple animals.