Chemosensor-driven artificial antennal lobe transient dynamics enable fast recognition and working memory

Authors:
Mehmet K. Muezzinoglu;Ramon Huerta;Henry D. I. Abarbanel;Margaret A. Ryan;Mikhail I. Rabinovich
Affiliations:
Institute for Nonlinear Science, University of California, San Diego, La Jolla, CA;Institute for Nonlinear Science, University of California, San Diego, La Jolla, CA;Institute for Nonlinear Science, Department of Physics, and Marine Physical Laboratory, Scripps Institute of Oceanography, University of California, San Diego, La Jolla, CA;Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA;Institute for Nonlinear Science, University of California, San Diego, La Jolla, CA
Venue:
Neural Computation
Year:
2009

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Abstract

The speed and accuracy of odor recognition in insects can hardly be resolved by the raw descriptors provided by olfactory receptors alone due to their slow time constant and high variability. The animal overcomes these barriers by means of the antennal lobe (AL) dynamics, which consolidates the classificatory information in receptor signal with a spatiotemporal code that is enriched in odor sensitivity, particularly in its transient. Inspired by this fact, we propose an easily implementable AL-like network and show that it significantly expedites and enhances the identification of odors from slow and noisy artificial polymer sensor responses. The device owes its efficiency to two intrinsic mechanisms: inhibition (which triggers a competition) and integration (due to the dynamical nature of the network). The former functions as a sharpening filter extracting the features of receptor signal that favor odor separation, whereas the latter implements a working memory by accumulating the extracted features in trajectories. This cooperation boosts the odor specificity during the receptor transient, which is essential for fast odor recognition.