Analysis of Span-Lateral Inhibition Neural Network and Its Application

Abstract

Abstract:

A novel neural network model S-LINN was proposed based on the neurons connection and lateral inhibition mechanism in the cortex. Considering the multilayer and span connection of cortex neurons, the model was used to simulate the cerebral cortex structure. The approximation ability was verified throuth the universial approximate analysis. Meanwhile, a supervised algorithm based on the error backpropagation and gradient descent theory was developed to train the network parameters. Simulation results for the abalone age prediction demonstrate that the proposed model can achieve higher accuracy of approximation and generalization with a comparable compact network structure.

Key words: bionics, lateral inhibition, neural network, universal approximator

CLC Number:

TP 183

YANG Gang1, 2,QIAO Junfei1. Analysis of Span-Lateral Inhibition Neural Network and Its Application[J]. Journal of Shanghai Jiaotong University, 2014, 48(07): 965-970.

References

［1］Leung F H F, Lam H K, Ling S H. et al. Tuning of the structure and parameters of a neural network using an improved genetic algorithm ［J］. IEEE Transactions on Neural Networks, 2003, 14(1): 7988.

［2］Tsai J, Chou J， Liu T. Tuning the structure and parameters of a neural network by using hybrid taguchigenetic algorithm ［J］. IEEE Transactions on Neural Networks, 2006, 17(1): 6980.

［3］Islam M M, Sattar MA, Amin M F, et al. A new constructive algorithm for architectural and functional adaptation of artificial neural networks ［J］. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 2009, 39(6): 15901605.

［4］宋彦坡, 彭小奇. 一种结构自适应神经网络及其训练方法［J］. 控制与决策, 2010, 25(8): 12651268.

SONG Yanpo, PENG Xiaoqi. New structure adapting neural network and its training method ［J］. Control and Decision, 2010, 25(8): 12651268.

［5］Jaeger H, Haas H. Harnessing Nonlinearity: Predicting chaotic systems and saving energy in wireless communication ［J］. Science, 2004, 304(5667): 7880.

［6］朱小龙, 杨建国, 代贵松. 基于AVQ聚类和OIFElman神经网络的机床热误差建模［J］. 上海交通大学学报, 2014, 48 (1): 1621.

ZHU Xiaolong, YANG Jianguo, DAI Guisong. AVQ clustering algorithm and OIFElman neural network for machine tool thermal error ［J］. Journal of Shanghai Jiaotong University, 2014, 48 (1): 1621.

［7］Lorente De Nó R. Cerebral Cortex: Architecture, intracortical connections, motor projections［C］∥ Fulton J F. Physiology of the Nervous System. New York ：Oxford University Press, 1949: 288312.

［8］Cruikshank S J, Lewis T J ，Connors B W. Synaptic basis for intense thalamocortical activation of feedforward inhibitory cells in neocortex ［J］. Nature Neuroscience, 2007, 10(4): 462468.

［9］Hornik K, Stinchcombe M, White H. Multilayer feedforward networks are universal approximators［J］.

Neural Networks, 1989, 2(5): 359366.

［10］Blake C, Merz C. UCI repository of machine learning databases［DB］. http: ∥www.ics.uci.edu/ ~mlearn/MLRepository.html, Department of Information and Computer Sciences, University of California, Irvine, USA, 1998.

［11］Huang G B, Saratchandran P, Sundararajan N. An efficient sequential learning algorithm for growing and pruning RBF (GAPRBF) networks ［J］. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2004, 34(6): 22842292.

［12］Liang N Y, Huang G B, Saratchandran P, et al. A fast and accurate online sequential learning algorithm for feedforward networks ［J］. IEEE Transactions on Neural Networks, 2006, 17(6): 14111423.

［13］Li G, Niu P. An enhanced extreme learning machine based on ridge regression for regression ［J］. Neural Computing and Applications, 2013, 22: 804810.

［14］Huynh H T, Won Y. Regularized online sequential learning algorithm for singlehidden layer feedforward neural networks ［J］. Pattern Recognition Letters, 2011, 32(14): 19301935.

［15］韩敏, 王新迎. 基于信赖域Newton算法的ELM网络［J］. 控制与决策, 2011, 26(5): 757760.

HAN Min, WANG Xinying. ELM based on trust region Newton method ［J］. Control and Decision, 2011, 26(5): 757760.

[1]	LI Shijie, LIU Taixu, LIU Jialun, DONG Zhilin, XU Chengqi. Trajectory Tracking Control of Azimuth Stern Drive Tugs Based on GRU-MPC [J]. Journal of Shanghai Jiao Tong University, 2026, 60(3): 418-426.
[2]	LIANG Jiaming, YU Yin, HU Yile. Prediction of Fatigue Crack Growth in Metal Materials via Spatiotemporal Neural Network [J]. Journal of Shanghai Jiao Tong University, 2026, 60(3): 511-521.
[3]	Chen Chengxin, Zhang Pengyuan. DSNet: Disentangled Siamese Network with Neutral Calibration for Speech Emotion Recognition [J]. J Shanghai Jiaotong Univ Sci, 2026, 31(2): 248-257.
[4]	XIA Xiaoyan, ZHANG Yu, HU Xikun, ZHONG Ping. Research on Physical Adversarial Attack Methods for UAV Remote Sensing Target Detection Based on Diffusion Models [J]. Air & Space Defense, 2026, 9(1): 52-62.
[5]	LI Xiang, CHEN Siyuan, ZHANG Jun, KE Deping, GAO Jiemai, YANG Huanhuan. Physics-Informed Fast Transient Stability Assessment of Non-Fixed Length in Power Systems [J]. Journal of Shanghai Jiao Tong University, 2025, 59(7): 962-970.
[6]	ZENG Jincan, HE Gengsheng, LI Yaowang, DU Ershun, ZHANG Ning, ZHU Haojun. A Short-Term Carbon Emission Accounting Method for Power Industry Using Electricity Data Based on a Combined Model of CNN and LightGBM [J]. Journal of Shanghai Jiao Tong University, 2025, 59(6): 746-757.
[7]	WANG Xiaoqian, ZHOU Yusheng, MAO Yuanjun, LI Bin, ZHOU Wenqing, SU Sheng. Distributed Photovoltaic Power Outlier Detection Based on Quantile Regression Neural Network [J]. Journal of Shanghai Jiao Tong University, 2025, 59(6): 836-844.
[8]	TAHIR Rizwana, CAI Yunze. Multi-Human Pose Estimation by Deep Learning-Based Sequential Approach for Human Keypoint Position and Human Body Detection [J]. J Shanghai Jiaotong Univ Sci, 2025, 30(6): 1103-1113.
[9]	HUANG Yixiang, DOU Xun, LI Linxi, YANG Hanyu, YU Jiancheng, HUO Xianxu. Quantitative Method of Response Value of Integrated Energy Equipment Based on Global Sensitivity Analysis [J]. Journal of Shanghai Jiao Tong University, 2025, 59(5): 569-579.
[10]	PAN Meiqi, HE Xing. A Fault Diagnosis Method for Wind Turbines Based on Zero-Shot Learning [J]. Journal of Shanghai Jiao Tong University, 2025, 59(5): 561-568.
[11]	ZHOU Tianlong, YAO Fangjing, RAO Weixiong. Target State Estimation Algorithms Under Partially Known State Space Models [J]. Air & Space Defense, 2025, 8(3): 111-122.
[12]	Pan Xinrong, Liu Xuewen, Zhu Bo, Wang Yingyi. Physics-Guided Neural Network with Gini Impurity-Based Structural Optimizer for Prediction of Membrane-Type Acoustic Material Transmission Loss [J]. J Shanghai Jiaotong Univ Sci, 2025, 30(3): 613-624.
[13]	Feng Lingdong, Miao Yubin. Intelligent Heart Rate Extraction Method Based on Millimeter Wave Radar [J]. J Shanghai Jiaotong Univ Sci, 2025, 30(3): 493-498.
[14]	Ma Yiyuan, Chen Huaiyuan, Chen Weidong. Real-Time Prediction of Elbow Motion Through sEMG-Based Hybrid BP-LSTM Network [J]. J Shanghai Jiaotong Univ Sci, 2025, 30(3): 455-462.
[15]	Nie Wei, Liang Xinwu. Efficient Fully Convolutional Network and Optimization Approach for Robotic Grasping Detection Based on RGB-D Images [J]. J Shanghai Jiaotong Univ Sci, 2025, 30(2): 399-416.