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Dr David Barber
Department of Computer Science, UCL
Gower Street
London
WC1E 6BT
Appointment
  • Reader
  • Dept of Computer Science
  • Faculty of Engineering Science
Research Summary
My interests are in information processing in both natural and artificial systems. Principally my work develops statistical techniques to deal with large, stochastic systems.

In natural systems, my viewpoint is that biological systems encode relevant functional
characteristics with the molecular and physical tools they have available. Our task is to primarily understand what this functionality is, the mechanisms behind this being somewhat secondary to this. The rationale behind this viewpoint is that the functionalities are key to applying insights from neurobiology to different areas -- for example, to make an artificial visual or auditory system that goes beyond low-level signal processing. In doing so, I hope to shed light on observed experimental behaviour demonstrating that the observed complexity in the neural system may be explicable by simple rules which, nevertheless, lead to complex effects.

Whilst progress in understanding individual neural behaviour is clear, relatively little has been learned about the interactions and properties of neural assemblies.

My most relevant work in theoretical neurobiology concerns learning in networks of spiking neurons. I showed that effects such as Spike Time Dependent Plasticity are natural consequences of a collection of coupled neurons wishing to optimally store patterns, and given the fact that refractoriness is an unavoidable biological constraint. These results are significant in that they are able to explain STDP as a consequence of a simple objective (functional) which is implemented with the biological processing units at hand.

I'm particularly interested in auditory processing. My work on signal processing concerns learning and extracting fundamental sound units based on complex non-linear processing. My goal is to show how the cochlea mimics the objective of recognising core sub-units of sound that in-composition make up the rich acoustic events of everyday life. In doing so, we hope to derive improved artificial speech and acoustic processing systems.



Academic Background
1995 PhD Doctor of Philosophy – Neural Networks University of Edinburgh
1992 MSc Master of Science – Information Mathematics King's College London
1990 BA Bachelor of Arts – Mathematics University of Cambridge
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