面向生物视觉的神经编码模型研究:进展与挑战  

Research on Neural Encoding Models for Biological Vision:Progress and Challenges

作　　者：贾杉杉 余肇飞[1,2] 刘健 黄铁军 JIA Shanshan;YU Zhaofei;LIU Jian;HUANG Tiejun(School of Computer Science,Peking University,Beijing 100871,China;Institute for Artificial Intelligence,Peking University,Beijing 100871,China;School of Computing,University of Leeds,Leeds LS29JT,UK)

机构地区：[1]北京大学计算机学院,北京100871 [2]北京大学人工智能研究院,北京100871 [3]利兹大学计算学院,利兹LS29JT

出　　处：《电子与信息学报》2023年第8期2689-2698,共10页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(62176003)。

摘　　要：视觉系统通过神经元将丰富且密集的动态视觉刺激编码成时变的神经响应。探寻视觉刺激与神经响应之间函数关系是理解神经编码机理的一种常见手段。该文首先介绍了视觉系统的神经编码模型,归纳为两类:生物物理编码模型和人工神经网络编码模型。然后介绍了各种模型的参数估计方法。通过对比各种模型的特性,总结了各自的优势、应用场景及所存在问题。最后,对视觉编码研究的现状以及未来面对的挑战进行了展望。The visual system encodes rich and dense dynamic visual stimuli into time-varying neural responses through neurons.Exploring the functional relationship between visual stimuli and neural responses is a common approach to understanding neural encoding mechanisms.Neural encoding models of the visual system are presented throughout this paper,which can be grouped into two categories:biophysical encoding models and artificial neural network encoding models.Then parameter estimation methods for various models are introduced.By comparing the characteristics of various models,the respective advantages,application scenarios and existing problems are summarized.Finally,the current situation and future challenges of visual encoding research are summarized and forecasted.

关 键 词：类脑视觉 生物视觉系统 生物物理编码模型 人工神经网络编码模型 系统辨识 

分 类 号：TN919.31[电子电信—通信与信息系统] TP183[电子电信—信息与通信工程]