By imitating the relevant principles of the biological olfactory epithelium layer and the olfactory bulb, a bionic spike coding and a bionic olfactory bulb model are proposed to process the signals of electronic noses. Bionic spike coding converts the sensor's response into spike signals through a process of accumulation and release. This spike signal has similar characteristics to the spikes generated by cells in the biological olfactory epithelium layer, which is more in line with biological systems. The bionic olfactory bulb model is built according to the key cell types and connection pathways found in the biological olfactory bulb. It can effectively simulate the lateral inhibition mechanism of the biological olfactory bulb and achieve contrast enhancement between signals. In order to compare the performance of the proposed method and traditional methods, experiments were first conducted on two collected data sets. When compared with traditional methods, the average classification accuracies of the proposed method reached 95.5 % and 97.2 % respectively, exceeding the sub-optimal methods by 2.1 % and 0.6 % respectively. Experiments were also conducted on three challenging public datasets with the following characteristics: low concentration, small sample size, and highly similar mixed gases. The experiments prove that the proposed method has stronger recognition ability and universality than traditional methods. In addition, other coding methods and bionic olfactory bulb models are also compared. The proposed coding method and bionic olfactory bulb model both achieve the best results, improving by 8.3 % and 1 % respectively compared to the sub-optimal method. (c) 2025 Elsevier Science. All rights reserved.
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