I am a member of the Pattern Analysis Research Group in Oxford and the Adaptive behavior Research Group in Sheffield. I previously studied Computer Science at Cambridge and Informatics at Edinburgh. I spent two years in London working on automated financial prediction, trading and valuation for private hedge funds. I am currently working part-time on the Sheffield site of the EU ICEA and BIOTACT projects and finishing off my thesis.
I'm interested in building intelligent machines, both to automate real-world tasks and as models of biological cognition. In practice this means using Bayesian graphical models; statistical signal processing; optimization and decision theory; and also more symbolic, high-level heuristics to make decisions tractable. My thesis uses tasks from the example minidomain of musical audio scene perception to illustrate a constructive, pragmatist, Bayesian view of the perception process. It draws heavily from Hofstadter's classic CopyCat architecture as well as Machine Learning. Inference in scene perception is very, very hard in the computational sense so -- more out of desparation than anything else -- I looked at how future quantum computing algorithms could speed it up.
In ICEA and BIOTACT I'm thinking about how these constructivist, pragmatist and Bayesian algorithms could be implemented biologically, to form part of a theory of cognition. For example, how could cortical microcircuits implement a Gibbs sampler node? Could the hippocampus be viewed usefully as a Particle Filter?
The constructivist and quantum ideas link in nicely with a Philosophy of Mind stance I developed at Edinburgh called pseudonoumenalism. I have a continuing interest in models of creativity (which is kind of the inverse problem to perception), especially in music, this is mostly just for fun at the moment.
Computational Neuroscience models of rat whisker signal processing
I am working part-time at Sheffield, helping to integrate models of cognition, emotion and autonomy.
Basically, a bunch of EU universities have developed models of different parts of the rat brain, and we are joining them together to run in a real robot.
- Fox, C., Girdhar, N. and Gurney, K. A Causal Bayesian Network View of Reinforcement Learning. Proc. AAAI Int. FLAIRS, 2008 preprint.
- Fox C.W. Review of 'Bayesian Brain'. Connection Science 20(1):67-68. 2008. informaworld
- Fox C.W., Pearson M., Mitchinson B.. Pipe T., and Prescott T.J. Simple features for texture classification from robot whisker strains. Barrels XIX, Atlanta, 2006. abstract in Somatosensory and Motor Research 24(3):139-162, 2007.
- Grant R., Fox C.W., Mitchinson B., and Prescott T.J. Active touch in rat whiskers increases contact synchronicity. SFN 2007, San Diego, 2007. abstract
- S.P.Wilson; C.W.Fox; T.J.Prescott; J.A.Bednar. A Self-Organizing Model of Whisker Deflection Maps in the Barrel Cortex . Barrels XX, San Diego 2007.
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