河南师范大学学报(自然科学版)

2021, v.49;No.221(06) 55-63+2

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运动目标预测跟踪的神经计算机制
Neural Mechanisms for Anticipative Tracking of Moving Objects

弭元元;谭碧蓝;王彬又;
Mi Yuanyuan;Tan Bilan;Wang Binyou;School of Medicine,Chongqing University;School of Bioengineering,Chongqing University;

摘要(Abstract):

迄今为止,人们对神经系统如何提取外部输入中静态信息的机制已经有所了解,但对其处理动态信息的机制却知之甚少.在处理运动信息时,神经系统面临的一个根本性挑战是克服神经信号在大脑内传输的时间延迟.这种延迟是显著的,同时又是不可避免的.它是层次化的神经信号通路和模块化的脑功能分区在传递、交流信息时必然产生的结果.如果这些时间延迟得不到补偿,神经系统对快速运动物体的空间位置的感知就会滞后于物体的真实位置,从而不能实时处理运动信息.大量实验表明大脑补偿延迟的策略是对运动物体将要到达的空间位置做出预测.回顾已有的数理模型,主要是具有负反馈效应的连续吸引子神经网络模型,及其实现运动预测的神经计算机制,并对相关神经网络模型在类脑计算中的应用前景进行了展望.
Up to now, we know more about how neural systems process static information, the equally important issue of how neural systems process motion information has remained much less understood. A big challenge in processing motion information is to compensate for time delays which are pervasive in neural systems. These delays are significant and also inevitable, which are the consequence of neural signals transmitting over layers of neurons and between cortical regions. If these delays are not compensated properly, our perception of a fast moving object will lag behind its true position in the external world significantly, impairing our vision and motor control. A large volume of experimental study has revealed that the brain compensates time delays by predicting the future position of a moving object. Recently, a computational model which exploits continuous attractor neural networks, a generic model for neural information representation, and negative feedback, a phenomenon widely existing in neuronal and synaptic activities, was proposed. This model achieves a constant anticipative time as observed in the experiments successfully. In this paper, we review the neural mechanisms for implementing anticipative tracking of objects, and discuss the potential applications of these neural models for brain-inspired computing.

关键词(KeyWords): 预测跟踪;连续吸引子神经网络;负反馈;类脑计算
anticipative tracking;continuous attractor neural network;negative feedback;brain-inspired computing

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金(31771146;11734004);; 北京市科技新星项目(Z181100006218118);; 中央高校经费(2020CDJQY-A073)

作者(Author): 弭元元;谭碧蓝;王彬又;
Mi Yuanyuan;Tan Bilan;Wang Binyou;School of Medicine,Chongqing University;School of Bioengineering,Chongqing University;

Email:

DOI: 10.16366/j.cnki.1000-2367.2021.06.008

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