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A neuromorphic model of dual pathway odour classification

Chris Häusler1, 2, 3*, Martin P. Nawrot1, 2 and Michael Schmuker1, 2
  • 1 Humboldt-Universität zu Berlin, Bernstein Center for Computational Neuroscience Berlin, Germany
  • 2 Freie Universität Berlin, Neuroinformatics and Theoretical Neuroscience, Institute of Biology, Germany
  • 3 Technische Universität Berlin, Machine Learning, Germany

Insects can identify odorant stimuli in a fast and highly reliable manner. This is evident from a large body of behavioral discrimination experiments, e.g. in the honeybee (Giurfa 2007). The neuronal circuits involved in odor discrimination are well described on the structural level. We investigate the functional implications at the circuit level in order to improve our understanding of insect olfaction at each processing step. Our ultimate goal is to employ neural working principles for processing of chemical sensor data in technical applications.Here, we show that strong lateral inhibitory connections found in the insect antennal lobe (AL) facilitate more accurate odour classification by increasing the linear separability of presented stimuli. Our findings are based on a spiking neural network extension of prior work with rate coding in the AL (Schmuker et al. 2007). By leveraging the pyNN neural network modeling language (Eppler et al 2009), we are able to run simulations using well established neuronal simulators such as Neuron and Nest along with neuromorphic hardware that employs up to several hundred analog implementations of Integrate and Fire neurons and their synaptic interconnections (Brüderle et al. 2009).As an extension of our current work, we will also investigate a spiking network model of the dual olfactory pathways in the AL of Hymenoptera (bees and ants). The first is characterised by a narrow tuning to different odours whilst being largely concentration invariant. The second is less selective toward odour identity but responds strongly to different odour concentrations (Yamagata et al. 2009). These complementary features imply a functional diversification, which possibly results in an improved odor discriminability in bees and ants over other insect species.

References

Brüderle D, Müller E, Davison A, Muller E, Schemmel J, Meier K (2009).Establishing a Novel Modeling Tool: A Python-based Interface for a Neuromorphic Hardware System
Front. Neuroinform. 3:17

Eppler J, Kremkow J, Muller E, Pecevski D, Perrinet L, Yger P (2009). PyNN: a common interface for neuronal network simulators. Neuroinformatics 2 (January) p. 1-10

Giurfa, M. (2007). Behavioral and neural analysis of associative learning in the honeybee: a taste from the magic well. J Comp Physiol. A 193, 801-24.

Schmuker, M., and Schneider, G. (2007). Processing and classification of chemical data inspired by insect olfaction. PNAS 104, 20285-20289.

Yamagata N, Schmuker M, Szyszka P, Mizunami M, Menzel R (2009). Differential odor processing in two olfactory pathways in the honeybee. Front. Neuroscience 3

Keywords: computational neuroscience

Conference: Bernstein Conference on Computational Neuroscience, Berlin, Germany, 27 Sep - 1 Oct, 2010.

Presentation Type: Poster Abstract

Topic: Bernstein Conference on Computational Neuroscience

Citation: Häusler C, Nawrot MP and Schmuker M (2010). A neuromorphic model of dual pathway odour classification. Front. Comput. Neurosci. Conference Abstract: Bernstein Conference on Computational Neuroscience. doi: 10.3389/conf.fncom.2010.51.00043

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Received: 17 Sep 2010; Published Online: 23 Sep 2010.

* Correspondence: Dr. Chris Häusler, Humboldt-Universität zu Berlin, Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany, chris.hausler@bccn-berlin.de