仿生嗅觉模型构建方法及其应用综述

doi:10.3969/j.issn.1006-2475.2024.10.016

摘要/Abstract

摘要： 嗅觉对于生物的生存和繁衍有着至关重要的作用，仿生嗅觉模型是研究嗅觉的重要途径，对其进行研究能够推动生物学、神经科学、环境监测、计算科学、医学等众多领域的发展。针对现有仿生嗅觉模型繁多的问题，首先，对生物嗅觉神经系统的组成及工作原理进行全面的梳理，为仿生嗅觉模型的构建提供理论基础；其次，根据模拟嗅觉受体细胞与气味分子结合过程的实现原理不同，对构建仿生嗅觉模型的方法进行细致的划分和归纳，针对每一种方法都深入剖析其工作机制和原理，并总结其在仿生嗅觉模型研究中的优势和不足；最后，针对仿生嗅觉模型构建过程中存在的问题，基于理论推导和实际应用经验提供可行的建议。

关键词: 嗅觉, 生物学, 神经科学, 环境监测, 计算科学

Abstract: Olfaction plays a crucial role in the survival and reproduction of organisms. Bio-inspired olfactory models， as a significant approach to studying smell， have the potential to drive the development of various fields such as biology， neuroscience， environmental monitoring， computer science， and medicine. In addressing the challenges presented by the numerous existing bio-inspired olfactory models， this work initially conducts a comprehensive review of the composition and operation principles of the biological olfactory nervous system， providing a theoretical foundation for constructing bio-inspired olfactory models. Secondly， based on the principle of simulating the binding process between olfactory receptor cells and odor molecules， the methods for constructing bio-inspired olfactory models are meticulously categorized and summarized. Each method is thoroughly dissected in terms of its working mechanisms and principles， and their strengths and limitations in bio-inspired olfactory model research are summarized. Finally， addressing the issues encountered during the construction of bio-inspired olfactory models， feasible recommendations are provided based on theoretical derivations and practical application experiences.

Key words: , olfaction, biology, neuroscience, environmental monitoring, computational science

中图分类号:

TP391.9

李威1, 何浩波1, 李光2, 邝利丹1, 张锦1. 仿生嗅觉模型构建方法及其应用综述[J]. 计算机与现代化, 2024, 0(10): 100-106.

LI Wei1, HE Haobo1, LI Guang2, KUANG Lidan1, ZHANG Jin1. A Review of Bionic Olfactory Model Construction Methods and Applications[J]. Computer and Modernization, 2024, 0(10): 100-106.

参考文献

［1］ DOTY R L. Olfaction in Parkinson’s disease and related disorders［J］. Neurobiology of Disease， 2012，46（3）:527-552.
［2］ ALI I， ALHARBI O M L. COVID-19: Disease， management， treatment， and social impact［J］. Science of the Total Environment， 2020，728. DOI: 10.1016/j.scitotenv.2020.
138861.
［3］ RESSLER K J， SULLIVAN S L， BUCK L B. Information coding in the olfactory system: Evidence for a stereotyped and highly organized epitope map in the olfactory bulb［J］. Cell， 1994，79（7）:1245-1255.
［4］ VASSAR R， CHAO S K， SITCHERAN R， et al. Topographic organization of sensory projections to the olfactory bulb［J］. Cell， 1994，79（6）:981-991.
［5］ LUO M M， FEE M S， KATZ L C. Encoding pheromonal signals in the accessory olfactory bulb of behaving mice［J］. Science， 2003，299（5610）: 1196-1201.
［6］ WU Q X， MCGINNITY T M， MAGUIRE L P， et al. Learning under weight constraints in networks of temporal encoding spiking neurons［J］. Neurocompting， 2006，69（16-18）:1912-1922.
［7］ ROBERTS P D， BELL C C. Spike-timing dependant synaptic plasticity in biological systems［J］. Biological Cybernetics， 2002，87（5-6）:392-403.
［8］ SHARMA A， KUMAR R， AIER I， et al. Sense of smell: Structural， functional， mechanistic advancements and challenges in human olfactory research［J］. Current Neuropharmacology， 2019，17（9）:891-911.
［9］ GIESSEL A J， DATTA S R. Olfactory maps， circuits and computations［J］. Current Opinion in Neurobiology， 2014， 24:120-132.
［10］ ESPINOSA‐JOVEL C， TOLEDANO R， JIMÉNEZ‐HUETE A， et al. Olfactory function in focal epilepsies: Understanding mesial temporal lobe epilepsy beyond the hippocampus［J］. Epilepsia Open， 2019，4（3）:487-492.
［11］ MAINLAND J D， LUNDSTRÖM J N， REISERT J， et al. From molecule to mind: An integrative perspective on odor intensity［J］. Trends in Neurosciences， 2014，37（8）:443-454.
［12］ OBOTI L， RUSSO E， TRAN T， et al. Amygdala corticofugal input shapes mitral cell responses in the accessory olfactory bulb［J］. Eneuro， 2018，5（3）DOI: 10.1523/ENEU RO.0175-18.2018.
［13］ CRASTO C， MARENCO L， MILLER P， et al. Olfactory receptor database: A metadata-driven automated population from sources of gene and protein sequences［J］. Nucleic Acids Research， 2002，30（1）:354-360.
［14］ LIU N， CRASTO C J， MA M H. Integrated olfactory receptor and microarray gene expression databases［J］. BMC Bioinformatics， 2007，8. DOI: 10.1186/1471-2105-8-231.
［15］ LIU X Y， SU X B， WANG F， et al. ODORactor: A web server for deciphering olfactory coding［J］. Bioinformatics， 2011，27（16）:2302-2303.
［16］ MODENA D， TRENTINI M， CORSINI M， et al. OlfactionDB: A database of olfactory receptors and their ligands［J］. Advances in Life Sciences， 2011，1（1）:1-5.
［17］ ZHANG W W， WANG L， CHEN J， et al. A novel gas recognition and concentration detection algorithm for artificial olfaction［J］. IEEE Transactions on Instrumentation and Measurement， 2021，70. DOI: 10.1109/TIM.2021.3071313.
［18］宋健宇. 电子鼻技术在果蔬检测中的应用探讨［J］. 食品安全导刊， 2018（9）:101.
［19］ PALACÍN J， CLOTET E， RUBIES E. Assessing over time performance of an eNose composed of 16 Single-Type MOX gas sensors applied to classify two volatiles［J］. Chemosensors， 2022，10（3）. DOI: 10.3390/chemosensors10030118.
［20］刘弘禹，孟钢，邓赞红，等. VOCs分子的半导体型传感器识别检测研究进展［J］. 物理化学学报， 2022，38（5）:26-42.
［21］ MELENDEZ F， ARROYO P， SUAREZ J I， et al. NanoElectroOptical nose （NEONOSE） for the detection of climate change gases［C］// 2022 IEEE International Symposium on Olfaction and Electronic Nose （ISOEN）. IEEE， 2022. DOI: 10.1109/ISOEN54820.2022.9789628.
［22］ KAUSHAL S， NAYI P， RAHADIAN D， et al. Applications of electronic nose coupled with statistical and intelligent pattern recognition techniques for monitoring tea quality: A review［J］. Agriculture， 2022，12（9）. DOI: 10.3390/
agriculture12091359.
［23］ GHASEMI-VARNAMKHASTI M， MOHTASEBI S S， SIAD
AT M， et al. Meat quality assessment by electronic nose （machine olfaction technology）［J］. Sensors， 2009，9（8）:6058-6083.
［24］ ZHAO Z Z， TIAN F C， LIAO H L， et al. A novel spectrum analysis technique for odor sensing in optical electronic nose［J］. Sensors and Actuators B: Chemical， 2016，222:769-779.
［25］陈通，祁兴普，陈斌，等. 基于电子鼻技术的猪肉脯品质判别分析［J］. 肉类研究， 2021，35（2）:31-34.
［26］ EL BARBRI N， AMARI A， VINAIXA M， et al. Building of a metal oxide gas sensor-based electronic nose to assess the freshness of sardines under cold storage［J］. Sensors and Actuators B: Chemical， 2007，128（1）:235-244.
［27］ BALASUBRAMANIAN S， PANIGRAHI S， LOGUE C M， et al. Neural networks-integrated metal oxide-based artificial olfactory system for meat spoilage identification［J］. Journal of Food Engineering， 2009，91（1）:91-98.
［28］ ROMAIN A C， GODEFROID D， KUSKE M， et al. Monitoring the exhaust air of a compost pile as a process variable with an e-nose［J］. Sensors and Actuators B: Chemical， 2005，106（1）:29-35.
［29］傅得锋，沈卫东，莫卫民，等. 超快速气相色谱电子鼻分析技术在汽油标号判定中的应用研究［J］. 刑事技术， 2012，37（5）:18-21.
［30］ GHOSH R， GARDNER J W， GUHA P K. Air pollution monitoring using near room temperature resistive gas sensors: A review［J］. IEEE Transactions on Electron Devices 2019，66（8）:3254-3264.
［31］ LIU H X， LI Q， YAN B， et al. Bionic electronic nose based on MOS sensors array and machine learning algorithms used for wine properties detection［J］. Sensors， 2019，19（1）. DOI: 10.3390/s19010045.
［32］ SCHUMAN C D， KULKARNI S R， PARSA M， et al. Opportunities for neuromorphic computing algorithms and applications［J］. Nature Computational Science， 2022，2（1）:10-19.
［33］ MEROLLA P A， ARTHUR J V， ALVAREZ-ICAZA R， et al. A million spiking-neuron integrated circuit with a scalable communication network and interface［J］. Science，2014， 345（6197）: 668-673.
［34］ ESSER S K， MEROLLA P A， ARTHUR J V， et al. Convolutional networks for fast， energy-efficient neuromorphic computing［J］. Proceedings of the National Academy of Sciences of the United States of America， 2016，113（41）:11441-11446.
［35］ MORAN S， GAONKAR B， WHITEHEAD W， et al. Deep learning for medical image segmentation-using the IBM TrueNorth neurosynaptic system［C］// Medical Imaging 2018: Imaging Informatics for Healthcare， Research， and Applications. SPIE， 2018:279-286.
［36］ TSAI W Y， BARCH D R， CASSIDY A S， et al. Always-on speech recognition using TrueNorth， a reconfigurable， neurosynaptic processor［J］. IEEE Transactions on Computers， 2017，66（6）:996-1007.
［37］ AMIR A， TABA B， BERG D， et al. A low power， fully event-based gesture recognition system［C］// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE， 2017:7388-7397.
［38］ FAIR K L， MENDAT D R， ANDREOU A G， et al. Sparse coding using the locally competitive algorithm on the TrueNorth neurosynaptic system［J］. Frontiers in Neuroscience， 2019，13. DOI: 10.3389/fnins.2019.00754.
［39］ CASSIDY A S， ALVAREZ-ICAZA R， AKOPYAN F， et al. Real-time scalable cortical computing at 46 giga-synaptic OPS/watt with ~100× speedup in time-to-solution and ~100，000 reduction in energy-to-solution［C］// 2014 International Conference for High Performance Computing， Networking， Storage and Analysis. IEEE， 2014:27-38.
［40］ IMAM N， CLELAND T A. Rapid online learning and robust recall in a neuromorphic olfactory circuit［J］. Nature Machine Intelligence， 2020，2（3）:181-191.
［41］ SHARMA A， KUMAR R， SEMWAL R， et al. DeepOlf: Deep neural network based architecture for predicting odorants and their interacting olfactory receptors［J］. IEEE/ACM Transactions on Computational Biology and Bioinformatics， 2022，19（1）:418-428.
［42］ ZHU P Y， ZHANG Y L， CHOU Y X， et al. Recognition of the storage life of mitten crab by a machine olfactory system with deep learning［J］. Journal of Food Process Engineering， 2019，42（7）. DOI: 10.1111/jfpe.13095.
［43］ PAREEK V， CHAUDHURY S. Deep learning-based gas identification and quantification with auto-tuning of hyper-parameters［J］. Soft Computing， 2021，25（22）:14155-14170.
［44］ ZHANG J， TIAN T T， WANG S C， et al. Research on an olfactory neural system model and its applications based on deep learning［J］. Neural Computing and Applications， 2020，32（10）:5713-5724.
［45］ LI Z P， HERTZ J. Odor recognition and segmentation by coupled olfactory bulb and cortical networks［J］. Neurocomputing， 1999，26-27:789-794.
［46］ SIMÕES-DE-SOUZA F M， ANTUNES G. Investigating the properties of adaptation in a computational model of the olfactory sensory neuron［J］. Neurocomputing， 2007，70（10-12）:1663-1667.
［47］ LILJENSTRÖM H. Modeling the dynamics of olfactory cortex using simplified network units and realistic architecture［J］. International Journal of Neural Systems， 1991，2（1-2）:1-15.
［48］ YAO Y， FREEMAN W J. Model of biological pattern recognition with spatially chaotic dynamics［J］. Neural Networks， 1990，3（2）:153-170.
［49］ FREEMAN W J. Characteristics of the synchronization of brain activity imposed by finite conduction velocities of axons［J］. International Journal of Bifurcation and Chaos， 2000，10（10）: 2307-2322.
［50］ CHANG H J， FREEMAN W J. Parameter optimization in models of the olfactory neural system［J］. Neural Networks， 1996，9（1）:1-14.
［51］ LI Z P， HOPFIELD J J. Modeling the olfactory bulb and its neural oscillatory processings［J］. Biological Cybernetics， 1989，61（5）:379-392.
［52］ SOH Z， TSUJI T， TAKIGUCHI N， et al. Neuro-based olfactory model for artificial organoleptic tests［J］. Artificial Life and Robotics， 2009，14（4）:474-479.
［53］ ZHANG Y L， SHARPEE T O. A robust feedforward model of the olfactory system［J］. PLoS Computational Biology， 2016，12（4）. DOI: 10.1371/journal.pcbi.1004850.
［54］ BARREIRO A K， GAUTAM S H， SHEW W L， et al. A theoretical framework for analyzing coupled neuronal networks: Application to the olfactory system［J］. PLoS Computational Biology， 2017，13（10）. DOI: 10.1371/journal.pcbi.
1005780.
［55］ CHANG H J， FREEMAN W J， BURKE B C. Biologically modeled noise stabilizing neurodynamics for pattern recognition［J］. International Journal of Bifurcation and Chaos， 1998，8（2）:321-345.
［56］杨如乃，胡志忠，卢健. 生物嗅觉神经系统模型的模拟与分析［J］. 生物医学工程研究， 2006，25（3）:131-136.
［57］张锦，李司铎，王如龙，等. KIII 模型动力学特性研究［J］. 湖南大学学报（自然科学版）， 2008，35（3）:76-79.
［58］ YANG X L， FU J， LOU Z G， et al. Tea classification based on artificial olfaction using bionic olfactory neural network［C］// Advances in Neural Networks-ISNN 2006: 3rd International Symposium on Neural Networks. Springer， 2006:343-348.
［59］张锦，李司铎，李光. KIII模型及其在人脸识别中的应用［J］. 计算机工程与应用， 2008，44（13）: 245-248.
［60］ CARLOS L F M， ROSA J L G. Face recognition through a chaotic neural network model［C］// 2014 International Joint Conference on Neural Networks （IJCNN）. IEEE， 2014:859-863.
［61］ OBAYASHI M， KOGA S， FENG L B， et al. Handwriting character classification using Freeman’s olfactory KIII model［J］. Artificial Life and Robotics， 2012，17（2）:227-232.
［62］汪乐，李光，郭宏记，等. 非线性嗅觉生理模型在一维序列识别中的应用［J］. 系统仿真学报， 2004，16（3）:564-569.
［63］ OBAYASHI M， SUDA R， KUREMOTO T， et al. A class identification method using Freeman’s olfactory KIII model［J］. Journal of Image and Graphics， 2016，4（2）:130-135.
［64］ ZHANG J， LI G， FREEMAN W J. Application of novel chaotic neural networks to text classification based on PCA［C］// Advances in Image and Video Technology: 1st Pacific Rim Symposium. Springer， 2006:1041-1048.
［65］ KOZMA R， FREEMAN W J. Basic principles of the KIV model and its application to the navigation problem［J］. Journal of Integrative Neuroscience， 2003，2（1）:125-145.
［66］ KOZMA R. Intentional systems: Review of neurodynamics， modeling， and robotics implementation［J］. Physics of Life Reviews， 2008，5（1）:1-21.
［67］刘宏，陈玲钰，韦小平，等. 基于KIV模型的脑电识别方法［J］. 计算机系统应用， 2022，31（10）:356-367.
［68］ HEBB D O. The Organization of Behavior: A Neuropsychological Theory［M］. New York: John Wiley & Sons Inc， 2002.

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