nips nips2008 nips2008-27 nips2008-27-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Mehmet K. Muezzinoglu, Alexander Vergara, Ramon Huerta, Thomas Nowotny, Nikolai Rulkov, Henry Abarbanel, Allen Selverston, Mikhail Rabinovich
Abstract: The odor transduction process has a large time constant and is susceptible to various types of noise. Therefore, the olfactory code at the sensor/receptor level is in general a slow and highly variable indicator of the input odor in both natural and artificial situations. Insects overcome this problem by using a neuronal device in their Antennal Lobe (AL), which transforms the identity code of olfactory receptors to a spatio-temporal code. This transformation improves the decision of the Mushroom Bodies (MBs), the subsequent classifier, in both speed and accuracy. Here we propose a rate model based on two intrinsic mechanisms in the insect AL, namely integration and inhibition. Then we present a MB classifier model that resembles the sparse and random structure of insect MB. A local Hebbian learning procedure governs the plasticity in the model. These formulations not only help to understand the signal conditioning and classification methods of insect olfactory systems, but also can be leveraged in synthetic problems. Among them, we consider here the discrimination of odor mixtures from pure odors. We show on a set of records from metal-oxide gas sensors that the cascade of these two new models facilitates fast and accurate discrimination of even highly imbalanced mixtures from pure odors. 1
[1] V. Bhandawat, S. R. Olsen, N. W. Gouwens, M. L. Schlief, and R. I. Wilson. Sensory processing in the drosphila antennal lobe increases reliability and separability of ensemble odor representations. Nature Neuroscience, 10:1474–1482, 2007.
[2] C.C. Chang and C. J. Lin. LibSVM - A library for support vector machines, v2.85, 2007.
[3] M de Bruyne, P. J. Clyne, and J. R. Carlson. Odor coding in a model olfactory organ: The Drosophila maxillary palp. Journal of Neuroscience, 11:4520–4532, 1999.
[4] R. Huerta, T. Nowotny, M. Garcia-Sanchez, H. D. I. Abarbanel, and M. I. Rabinovich. Learning classification in the olfactory system of insects. Neural Computation, 16:1601–1640, 2004.
[5] W. Maass, T. Natschlaeger, and H. Markram. Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural Computation, 14:2531–2560, 2002.
[6] P. R. Montague, P. Dayan, C. Person, and T. J. Sejnowski. Bee foraging in uncertain environments using predictive Hebbian learning. Nature, 337:725–728, 1995.
[7] J. Perez-Orive, O. Mazor, G. C. Turner, S. Cassenaer, R. I. Wilson, and G. Laurent. Oscillations and sparsening of odor representations in the mushroom body. Science, 297:359–365, 2002.
[8] M. I. Rabinovich, R. Huerta, and G. Laurent. Transient dynamics for neural processing. Science, 321:48– 50, 2008.
[9] B. Raman and R. Gutierrez-Osuna. Chemosensory processing in a spiking model of the olfactory bulb: Chemotopic convergence and center surround inhibition. In L. K. Saul, Y. Weiss, and L. Bottou, editors, NIPS 17, pages 1105–1112. MIT Press, Cambridge, MA, 2005.
[10] M. Schmuker and G. Schneider. Processing and classification of chemical data inspired by insect olfaction. Proc. Nat. Acad. Sci., 104:20285–20289, 2007.
[11] H. R. Wilson and J. D. Cowan. A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue. Kybernetik, 13:55–80, 1973. 8