基于精细神经元的类脑感知学习模型  

Biophysically Detailed Model for Brain-like Perceptual Learning

作　　者：张祎晨 何干 杜凯 黄铁军 ZHANG Yi-Chen;HE Gan;DU Kai;HUANG Tie-Jun(School of Computer Science,Peking University,Beijing 100871,China;Institute for Artificial Intelligence,Peking University,Beijing 100871,China)

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

出　　处：《软件学报》2024年第3期1403-1417,共15页Journal of Software

基　　金：国家自然科学基金(61425025);国家重点研发计划(2020AAA0130400)。

摘　　要：大脑如何实现学习以及感知功能对于人工智能和神经科学领域均是一个重要问题.现有人工神经网络由于结构和计算机制与真实大脑相差较大,无法直接用于理解真实大脑学习以及处理感知任务的机理.树突神经元模型是一种对大脑神经元树突信息处理过程进行建模仿真的计算模型,相比人工神经网络更接近生物真实.使用树突神经网络模型处理学习感知任务对理解真实大脑的学习过程有重要作用.然而,现有基于树突神经元网络的学习模型大都局限于简化树突模型,无法完整建模树突的信号处理过程.针对这一问题,提出一种基于精细中型多棘神经元网络的学习模型,使得精细神经网络可以通过学习完成相应感知任务.实验表明,在经典的图像分类任务上,所提模型可以达到很好的分类性能.此外,精细神经网络对于噪声干扰有很强的鲁棒性.对网络特性进行进一步分析,发现学习后网络中的神经元表现出了刺激选择性这种神经科学中的经典现象,表明所提模型具有一定的生物可解释性,同时也表明刺激选择特性可能是大脑通过学习完成感知任务的一种重要特性.How brains realize learning and perception is an essential question for both artificial intelligence and neuroscience communities.Since the existing artificial neural networks(ANNs)are different from the real brain in terms of structures and computing mechanisms,they cannot be directly used to explore the mechanisms of learning and dealing with perceptual tasks in the real brain.The dendritic neuron model is a computational model to model and simulate the information processing process of neuron dendrites in the brain and is closer to biological reality than ANNs.The use of the dendritic neural network model to deal with and learn perceptual tasks plays an important role in understanding the learning process in the real brain.However,current learning models based on dendritic neural networks mainly focus on simplified dendritic models and are unable to model the entire signal-processing mechanisms of dendrites.To solve this problem,this study proposes a learning model of the biophysically detailed neural network of medium spiny neurons(MSNs).The neural network can fulfill corresponding perceptual tasks through learning.Experimental results show that the proposed model can achieve high performance on the classical image classification task.In addition,the neural network shows strong robustness under noise interference.By further analyzing the network features,this study finds that the neurons in the network after learning show stimulus selectivity,which is a classical phenomenon in neuroscience.This indicates that the proposed model is biologically plausible and implies that stimulus selectivity is an essential property of the brain in fulfilling perceptual tasks through learning.

关 键 词：大脑仿真 类脑计算 感知学习 图像分类 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]