A modelling framework for the olfactory system of the honeybee using GeNN (GPU enhanced Neuronal Network simulation environment)

Esin Yavuz,Thomas Nowotny

doi:10.1186/2044-7248-3-s1-p23

Abstract

We present an example implementation of a minimalmodel of honeybee olfactory system on massively paral-lel GPU hardware using GPU-specific code generationwith GeNN[1]. This will be a first step to provide a phy-siologically coherent model of the honeybee olfactorysystem to be implemented in real time on fying autono-mous robots for the “Green Brain Project”.The “Green Brain Project” will combine computationalneuroscience modelling, learning and decision theory,modern parallel computing methods and robotics withdata from state-of-the-art neurobiological experimentson cognition in the honeybee Apis mellifera, to buildand deploy a modular model of the honeybee braindescribing detection, classification and learning in theolfactory and optic pathwaysaswellasmulti-sensoryintegration across these sensory modalities. Unlike otherbrain models, which use expensive traditional supercom-puting resources,the ‘Green Brain’ will be implementedon massively parallel, but affordable GPU technology.The ‘Green Brain’ will be deployed for the real-timecontrol of a flying robot able to sense and act autono-mously. This robot testbed will be used to demonstratethe development of new biomimetic control algorithmsfor artificial intelligence and robotics applications.The objective for modelling olfaction in the“GreenBrain Project” will extend previous attempts to model theantennal lobes and their constituent glomeruli (whichencode olfactory cues), the projection neurons and themushroom bodies. Odours are known to have a distribu-ted representation in the antennal lobe, encoded as differ-ential activation levels of glomerular populations. Odourmixtures are represented as a non-trivial combination ofconstituent odours’ representations, and formation oflong-term memories associated with such odour mixtureshas been shown to induce volume changes in glomeruliindicating a cross-inhibitory effect between neural codings[2]. The modelling will also consider how mechanismsmight implement known classification rules, such as in themodels of insect olfactory classification by Huerta et al. [3]and Nowotny et al. [4].In this study, we present some benchmarking results.We perform performance and scalability tests on an NVI-DIA Tesla C2070 GPU with an Intel

Highlights

We present an example implementation of a minimal model of honeybee olfactory system on massively parallel GPU hardware using GPU-specific code generation with GeNN[1]
The ‘Green Brain’ will be deployed for the real-time control of a flying robot able to sense and act autonomously. This robot testbed will be used to demonstrate the development of new biomimetic control algorithms for artificial intelligence and robotics applications
Odour mixtures are represented as a non-trivial combination of constituent odours’ representations, and formation of long-term memories associated with such odour mixtures has been shown to induce volume changes in glomeruli indicating a cross-inhibitory effect between neural codings [2]

Summary

Introduction

We present an example implementation of a minimal model of honeybee olfactory system on massively parallel GPU hardware using GPU-specific code generation with GeNN[1]. The “Green Brain Project” will combine computational neuroscience modelling, learning and decision theory, modern parallel computing methods and robotics with data from state-of-the-art neurobiological experiments on cognition in the honeybee Apis mellifera, to build and deploy a modular model of the honeybee brain describing detection, classification and learning in the olfactory and optic pathways as well as multi-sensory integration across these sensory modalities.

Results

Conclusion