Improved Odour Detection through Imposed Biomimetic Temporal Dynamics

We discuss a biomimetic approach for improving odour detection in artificial olfactory systems that utilises temporal dynamical delivery of odours to chemical sensor arrays deployed within stationary phase materials. This novel odour analysis technology, which we have termed an artificial mucosa, uses the principle of “nasal chromatography”; thus emulating the action of the mucous coating the olfactory epithelium. Temporal segregation of odorants due to selective phase partitioning during delivery in turn gives rise to complex spatio-temporal dynamics in the responses of the sensor array population, which we have exploited for enhanced detection performance. We consider the challenge of extracting stimulus-specific information from such responses, which requires specialised time-dependent signal processing, information measures and classification techniques.

[1]  Peter Dayan,et al.  Computational Differences between Asymmetrical and Symmetrical Networks , 1998, NIPS.

[2]  Thomas A Cleland,et al.  Anatomical contributions to odorant sampling and representation in rodents: zoning in on sniffing behavior. , 2006, Chemical senses.

[3]  Adam Kepecs,et al.  The sniff as a unit of olfactory processing. , 2006, Chemical senses.

[4]  Manuel A. Sánchez-Montañés,et al.  Chemical Sensor Array Optimization: Geometric and Information Theoretic Approaches , 2002 .

[5]  T. Pearce Computational parallels between the biological olfactory pathway and its analogue 'the electronic nose': Part I. Biological olfaction. , 1997, Bio Systems.

[6]  H. T. Nagle,et al.  Handbook of Machine Olfaction , 2002 .

[7]  Daniel J. Amit,et al.  Modeling brain function: the world of attractor neural networks, 1st Edition , 1989 .

[8]  D. S. Gill,et al.  Optical multibead arrays for simple and complex odor discrimination. , 2001, Analytical chemistry.

[9]  Thomas A Cleland,et al.  Dynamical mechanisms of odor processing in olfactory bulb mitral cells. , 2006, Journal of neurophysiology.

[10]  DeLiang Wang,et al.  Temporal pattern processing , 1998 .

[11]  Carlos D. Brody,et al.  Simple Networks for Spike-Timing-Based Computation, with Application to Olfactory Processing , 2003, Neuron.

[12]  G. Shepherd,et al.  Mechanisms of olfactory discrimination: converging evidence for common principles across phyla. , 1997, Annual review of neuroscience.

[13]  Alister Hamilton,et al.  Analog VLSI Circuit Implementation of an Adaptive Neuromorphic Olfaction Chip , 2007, IEEE Transactions on Circuits and Systems I: Regular Papers.

[14]  Michael A. Arbib,et al.  The handbook of brain theory and neural networks , 1995, A Bradford book.

[15]  H. Troy Nagle,et al.  Handbook of Machine Olfaction: Electronic Nose Technology , 2003 .

[16]  R. Guerrero-Rivera,et al.  Attractor-Based Pattern Classification in a Spiking FPGA Implementation of the Olfactory Bulb , 2007, 2007 3rd International IEEE/EMBS Conference on Neural Engineering.

[17]  K. Mori,et al.  The olfactory bulb: coding and processing of odor molecule information. , 1999, Science.

[18]  J. Brezmes,et al.  Qualitative and quantitative analysis of volatile organic compounds using transient and steady-state responses of a thick-film tin oxide gas sensor array , 1997 .

[19]  Joel White,et al.  Odor recognition in an artificial nose by spatio-temporal processing using an olfactory neuronal network , 1999, Neurocomputing.

[20]  Manuel A. Sánchez-Montañés,et al.  Fisher information and optimal odor sensors , 2001, Neurocomputing.

[21]  Tim C. Pearce,et al.  Towards an artificial olfactory mucosa for improved odour classification , 2007, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[22]  Markus Diesmann,et al.  Programmable Logic Construction Kits for Hyper-Real-Time Neuronal Modeling , 2006, Neural Computation.