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An oscillation-driven neural network for the simulation of an olfactory system

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Abstract

Understanding the nonlinear dynamics of an olfactory bulb (OB) is essential for the modelling of the brain and nervous system. We have analysed the nature of odour-receptor interactions and the conditions controlling neural oscillations. This analysis is the basis for the proposed biologically plausible three-tiered model of an oscillation-driven neural network (ODNN) with three non-linearities. The layered architecture of the bulb is viewed as a composition of different processing stages performing specific computational tasks. The presented three-tiered model of the olfactory system (TTOS) contains the sensory, olfactory bulb and anterior nucleus tiers. The number of excitatory (mitral/tufted) cells differs from the number of inhibitory (granule) cells, which improves the cognitive ability of the model. The odour molecules are first received at the sensory layer, where receptor neurons spatio-temporally encode them in terms of spiking frequencies. Neurons expressing a specific receptor project to two or more topographically fixed glomeruli in the OB and create a sensory map. Excitatory postsynaptic potentials are formed in the primary dendrite of mitral cells and are encoded in an exclusive way to present them to the coupled non-linear oscillatory model of the next mitral-granule layer. In a noisy background, our model functions as an associative memory, although it operates in oscillatory mode.

While feed-forward networks and recurrent networks with symmetric connections always converge to static states, learning and pattern retrieval in an asymmetrically connected neural network based on oscillations are not well studied. We derive the requirements under which a state is stable and test whether a given equilibrium state is stable against noise. The ODNN demonstrates its capability to discriminate odours by using nonlinear dendro-dendritic interactions between neurons. This model allows us to visualise and analyse how the brain is able to encode information from countless molecules with different odour receptors.

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Acknowledgements

This work is funded in part by the UMD Foundation grant #UMDF- 525360 “Modelling Of Odour Information Processing In The Human Brain—Nonlinear Simulations Of The Olfactory System” of the University of Massachusetts Dartmouth, PSC-CUNY Awards #61782–00–30, #63374–00–32, GRTI’01 grant “Human-Brain Modelling and Simulations”.

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Authors and Affiliations

  1. Computer and Information Sciences, University of Massachusetts Dartmouth, 285 Old Westport Rd., North Dartmouth, MA, 02747, USA

    Iren Valova

  2. Computer Science, College of Staten Island, City University of New York, 2800 Victory Blvd., Staten Island, NY, 10314, USA

    Natacha Gueorguieva

  3. Precision Machinery Systems, Tokyo Institute of Technology, 4259 Nagatsuta, Midori-ku, Yokohama 8502, Japan

    Yukio Kosugi

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  1. Iren Valova
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  2. Natacha Gueorguieva
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  3. Yukio Kosugi
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Correspondence to Iren Valova.

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Valova, I., Gueorguieva, N. & Kosugi, Y. An oscillation-driven neural network for the simulation of an olfactory system. Neural Comput & Applic 13, 65–79 (2004). https://doi.org/10.1007/s00521-003-0392-x

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  • DOI: https://doi.org/10.1007/s00521-003-0392-x

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