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Research on an olfactory neural system model and its applications based on deep learning

  • Advances in Parallel and Distributed Computing for Neural Computing
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Abstract

The idea of constructing the biological neural system model as realistic as possible can not only provide a new artificial neural network (ANN), but also offer an effective object to study biological neural systems. As a very meaningful attempt about the idea, a bionic model of olfactory neural system, KIII model, is introduced in this paper. There are the unique characteristics of KIII model different from those of general ANNs. The KIII model realistically simulates the structure of the real olfactory neural system and the process of odor molecules gradually transformed by the core components of the olfactory system including olfactory receptor, olfactory bulb and olfactory cortex. The neuron model of the KIII model is constructed and optimized based on neurophysiological experimental data and accurately reflects the response of olfactory neurons to odor stimulation. In particular, the noise introduced to KIII model can further improve the performance of the model. In addition, the KIII model is analyzed based on the idea of deep learning. The qualitative analysis shows that there are obvious similarities between the KIII model and the deep learning model. Furthermore, with the epileptic electroencephalograph (EEG) recognition task, two groups of experiments are designed to comprehensively analyze the performance of the KIII model. In the first group of experiments, a typical pattern recognition experiment with feature extraction stage is shown. The features of epileptic EEG were extracted based on Empirical Mode Decomposition (EMD), and the KIII model was used as a classifier. The experimental results show that the KIII model only needs a small number of iterations to memorize different modes and has a high recognition rate, over 91%. In the second group of experiments, a direct recognition experiment without feature extraction stage is shown. The original epileptic EEG signals as KIII model inputs directly were recognized. The experimental results show that there is still an excellent performance in the KIII model, over 96%, and the recognition result is similar to the characteristics of the deep learning model. The theoretical analysis and experimental results prove that KIII model with the idea of deep learning is an excellent bionic model of olfactory neural system and gets a good balance between high bionics and good performance, which is a good reference for related research.

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Acknowledgements

This work was supported by the education and research projects of Hunan Provincial Education Department (15K082, JG2018A012, XiangJiaoTong [2018]-107) and the education and research projects of Ministry of Education of the People’s Republic of China (201702001043, 201801037136).

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Authors and Affiliations

  1. College of Information Science and Engineering, Hunan Normal University, Changsha, 410081, China

    Jin Zhang, Tiantian Tian & Shengchun Wang

  2. College of Mathematics and Statistics, Hunan Normal University, Changsha, 410081, China

    Xiaofei Liu & Xuanyu Shu

  3. School of Humanities and Management, Hunan University of Chinese Medicine, Changsha, 410208, China

    Ying Wang

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  1. Jin Zhang
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  2. Tiantian Tian
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  3. Shengchun Wang
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  4. Xiaofei Liu
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  6. Ying Wang
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Correspondence to Ying Wang.

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Zhang, J., Tian, T., Wang, S. et al. Research on an olfactory neural system model and its applications based on deep learning. Neural Comput & Applic 32, 5713–5724 (2020). https://doi.org/10.1007/s00521-019-04498-x

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