Tim C. Pearce's Home Page

A mathematical model of the action of a broadly tuned chemical sensor array has been developed explaining the performance of such systems to large numbers of individual stimulus components. A chapter in the Handbook of Machine Olfaction provides the details [pdf]

Research

Main scientific goals:

To understand the neuronal information processing underpinning olfaction (smell) and use this to build better machines to sense molecular stimuli (machine olfaction). How building models of sensory information processing helps us better understand the nervous system. This work combines both practical and theoretical approaches:

Practical:

This work involves developing neuronal models of the olfactory pathway that are driven by real-world chemosensors as a test-bed for biologically inspired signal processing architectures. I have worked with conducting polymer, optical, and metal oxide semiconductor chemosensor devices.

Theoretical:

The olfactory systems relies upon a large repetoire of different chemoreceptor types in order to encode different molecular stimuli. The signals can be considered to act as a population code representation. Recent work has included considering this population coding as a geometric transformation and using Fisher Information to quanitify the accuracy of stimulus estimation. These concepts are being investigated for optimising chemical sensor arrays for practical applications

Selected papers for download:

Automatic decoding of sensor types within randomly-ordered, high-density optical sensor arrays, Albert K.J., Gill D.S., Pearce T.C.,Walt D.R., Analytical and Bioanalytical Chemistry, to appear 2002 [pdf]

Chemical Sensor Array Optimization: Geometric and Information Theoretic Approaches, Pearce T.C., Sanchez-Montanes M., in Handbook of Machine Olfaction, Pearce T.C., Schiffman S.S., Nagle H.T., Gardner J.W., Wiley-VCH, 2002 [pdf]

Robust Stimulus Encoding in Olfactory Processing: Hyperacuity and Efficient Signal Transmission, Pearce T.C., Verschure P.F.M.J, White J., Kauer J.S., in Neural Computation Architectures Based on Neuroscience, Wermter S., Austin J., and Willshaw D., Springer-Verlag, 2001. [ps.gz] [pdf]

Optical Multi-bead Arrays for Simple and Complex Odor Discrimination, Albert K.A., Gill D.S., Walt D.R., Pearce T.C Analytical Chemistry, 73(11), 2501-2508, 2001 [pdf]

Fisher Information and Optimal Odor Sensors, Sanchez-Montanes M., Pearce T.C., Neurocomputing, 38, 335-341, 2001[ps.gz] [pdf]

Stimulus Encoding during the Early Stages of Olfactory Processing: A Modeling Study using an Artificial Olfactory System, Pearce T.C., Verschure P.F.M.J., White J., Kauer J.S., Neurocomputing, 38, 299-306, 2001, [ps.gz] [pdf]

Odor to Sensor Space Transformations in Biological and Artificial Noses, Pearce T.C., Neurocomputing, Vol. 32-33, (2000) pp. 941-952. [ps.gz] [pdf]

Predicting Organoleptic Scores of Sub-ppm Flavour Notes Part 1, Theoretical and Experimental Details, Pearce T.C., and Gardner J.W., Analyst, Vol. 123, (1998) pp.2047-2055. [ps.gz] [pdf]

Predicting Organoleptic Scores of Sub-ppm Flavour Notes Part 2, Computational Analysis and Results, Pearce T.C., and Gardner J.W., Analyst, Vol. 123, (1998) pp.2056-2066. [ps.gz] [pdf]

Computational Parallels between the Biological Olfactory Pathway and its Analogue "The Electronic Nose": Part I. Biological Olfaction, Pearce T.C., BioSystems, Vol. 41(1), (1997) pp.43-67. [ps.gz] [pdf]

Computational Parallels between the Biological Olfactory Pathway and its Analogue "The Electronic Nose": Part II. Sensor-Based Machine Olfaction, Pearce T.C., BioSystems, Vol. 41(2), (1997) pp.69-90. [ps.gz] [pdf]

Book:

Special Issue of Network: Computation in Neural Systems

Lab details - I am principal investigator of NeuroLab within the Centre for Bioengineering. Lab pages

Projects:

Silicon aVLSI Olfactory System Implementation Project: project pages

Artificial Chemosensing Moth Project: project pages

Research

Selected papers for download:

Book:

Teaching:

Other stuff