Odor pattern recognition of a novel bio-inspired olfactory neural network based on kernel clustering

Xu, Xuying; Zhu, Zhenyu; Wang, Yihong; Wang, Rubin; Kong, Wanzeng; Zhang, Jianhai

doi:10.1016/j.cnsns.2022.106274

Cited by 6 publications

(3 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The characteristics of these neurophysiological responses strongly depend on the mechanism of the neural dynamics of each functional circuit. It has been found that photoreceptors and auditory receptors primarily encode the firing rate and firing time of spikes on neurons (Victor 1999;Wang 2018c, 2020;Jacobs et al 2009;Malnic et al 1999), while olfactory receptors simultaneously encode combinations of stimulus-sensitive neurons (Miyamichi and Luo 2009;Xu et al 2022a). In the recent decade, firing rate code has become the standard for describing the properties of various types of perceptual and cortical neurons.…”

Section: Receptor Codingmentioning

confidence: 99%

Brain works principle followed by neural information processing: a review of novel brain theory

Wang

et al. 2023

Artif Intell Rev

Self Cite

View full text Add to dashboard Cite

The way the brain work and its principle of work has long been a big scientific question that scientists have dreamed of solving. However, as is known to all, the brain works at different levels, and the operation at different levels is interactional and mutually coupled. Unfortunately, until now, we still do not know how the nervous system at different levels is interacting and coupling with each other. This review provides some preliminary discussions on how to address these scientific questions, for which we propose a novel theory of the brain called neural energy. Such a theoretical and research approach can couple neural information with neural energy to address the interactions of the nervous system at various levels. Therefore, this review systematically summarizes the neural energy theories and methods proposed by our research in the field of brain science, as well as the internal relationship between mechanics and neural energy theory. Focuses on how to construct a Wang–Zhang (W–Z) neuron model equivalent to Hodgkin–Huxley (H–H) model by using the idea of analytical dynamics. Then, based on this model, we proposed a large-scale neural model and a theoretical framework of global neural coding of the brain in the field of neuroscience. It includes information processing of multiple sensory and perceptual nervous systems such as visual perception, neural mechanism of coupling between default mode network and functional network of brain, memory switching and brain state switching, brain navigation, prediction of new working mechanism of neurons, and interpretation of experimental phenomena that are difficult to be explained by neuroscience. It is proved that the new W–Z neuron model and neural energy theory have unique functions and advantages in neural modeling, neural information processing and methodology. The idea of large-scale neuroscience research with neural energy as the core will provide a potentially powerful research method for promoting the fusion of experimental neuroscience and theoretical neuroscience in the future, and propose a widely accepted brain theory system between experimental neuroscience and theoretical neuroscience. It is of great scientific significance to abandon the shortcomings of reductive and holism research methods in the field of neuroscience, and effectively integrate their respective advantages in methodology.

show abstract

Section: Receptor Codingmentioning

confidence: 99%

Brain works principle followed by neural information processing: a review of novel brain theory

Wang

et al. 2023

Artif Intell Rev

Self Cite

View full text Add to dashboard Cite

show abstract

“…The precise relationship between molecular structure and odor perception, termed the structure-odor relationship (SOR), has attracted considerable attention from the aspect of data science [13,14]. Great efforts have been made in ODs identification based on various molecular features, such as physicochemical parameters, bio-inspired olfactory model, and MS [15]. Moreover, various deep neural network models (DNNs), machine learning (ML) classification frameworks, and topic models have been developed to express the SOR [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning-Based Olfactometry: An Auxiliary System for Human Assessors in Olfactory Measurement

Shang

Tang

Liu

et al. 2022

Preprint

View full text Add to dashboard Cite

Although gas chromatography/olfactometry (GC/O) has been employed as a powerful analytical tool in odor measurement, its application is limited by the variability, subjectivity, and high cost of the trained panelists who are used as detectors in the system. The advancements in data-driven science have made it possible to predict structure-odor-relationship (SOR) and thus to develop machine-learning-based olfactometry (ML-GCO) in which the human panelists may be replaced by machine learning models to obtain the sensory information of GC-separated chemical compounds. However, one challenge remained in ML-GCO is that there are too many odor descriptors (ODs) being used to describe the sensory characteristics of odorants. It is impractical to build a corresponding model for each OD. To solve this issue, we propose a SOR prediction approach based on odor descriptor clustering. 256 representative ODs are firstly classified into 20 categories using a co-occurrence Bayesian embedding model. The categorization effect is explained according to the semantic relationships using a pre-trained Word2Vec model. Various molecular structure features including molecularly parameters, molecular fingerprints, and molecular 2D graphic features extracted by convolutional neural networks, are employed to predict the aforementioned odor categories. High prediction accuracies (Area under ROC curve was 0.800±0.004) demonstrate the rationality of the proposed clustering scenario and molecular feature extraction. This study makes the ML-GCO models much closer to the practical application since they can be expected as either an auxiliary system or complete replacement of human panelists to perform the olfactory evaluation.

show abstract

“…So, clustering belongs to unsupervised learning in machine learning ( Sinaga and Yang, 2020 ). It is widely used in customer classification ( Deng and Gao, 2020 ; Li et al, 2021a ; Sun et al, 2021 ), automatic medical image detection ( Hassan et al, 2021 ), image retrieval ( Karthikeyan and Aruna, 2013 ; Gu et al, 2019 ; Anju and Shreelekshmi, 2022 ), object recognition ( Woźniak and Połap, 2018 ), data mining ( Hosseini et al, 2010 ), ( Sato et al, 2019 ), pattern recognition ( Xu et al, 2022 ), ( Singh and Ganie, 2021 ), and other fields.…”

Section: Introductionmentioning

confidence: 99%

Quantum-Inspired Moth-Flame Optimizer With Enhanced Local Search Strategy for Cluster Analysis

Cui

Luo

Zhou

et al. 2022

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

Clustering is an unsupervised learning technique widely used in the field of data mining and analysis. Clustering encompasses many specific methods, among which the K-means algorithm maintains the predominance of popularity with respect to its simplicity and efficiency. However, its efficiency is significantly influenced by the initial solution and it is susceptible to being stuck in a local optimum. To eliminate these deficiencies of K-means, this paper proposes a quantum-inspired moth-flame optimizer with an enhanced local search strategy (QLSMFO). Firstly, quantum double-chain encoding and quantum revolving gates are introduced in the initial phase of the algorithm, which can enrich the population diversity and efficiently improve the exploration ability. Second, an improved local search strategy on the basis of the Shuffled Frog Leaping Algorithm (SFLA) is implemented to boost the exploitation capability of the standard MFO. Finally, the poor solutions are updated using Levy flight to obtain a faster convergence rate. Ten well-known UCI benchmark test datasets dedicated to clustering are selected for testing the efficiency of QLSMFO algorithms and compared with the K-means and ten currently popular swarm intelligence algorithms. Meanwhile, the Wilcoxon rank-sum test and Friedman test are utilized to evaluate the effect of QLSMFO. The simulation experimental results demonstrate that QLSMFO significantly outperforms other algorithms with respect to precision, convergence speed, and stability.

show abstract

Odor pattern recognition of a novel bio-inspired olfactory neural network based on kernel clustering

Cited by 6 publications

References 29 publications

Brain works principle followed by neural information processing: a review of novel brain theory

Brain works principle followed by neural information processing: a review of novel brain theory

Machine-Learning-Based Olfactometry: An Auxiliary System for Human Assessors in Olfactory Measurement

Quantum-Inspired Moth-Flame Optimizer With Enhanced Local Search Strategy for Cluster Analysis

Contact Info

Product

Resources

About