基于尖峰神经元的条件反射模型及其认知行为的研究  

Classical Conditioning Model Based on Spiking Neuron and research on its Cognitive Behaviors

作　　者：杨贝贝[1] 阮晓钢[1] 

机构地区：[1]北京工业大学电子信息与控制工程学院,北京100022

出　　处：《系统仿真学报》2005年第9期2134-2137,共4页Journal of System Simulation

基　　金：国家自然科学基金资助(60375017)

摘　　要：一种具有经典条件反射行为的认知模型(CMSPK),该模型以尖锋神经元为基本元素,互联形成具有反射弧结构的神经网络,能充分表现经典条件反射对时间的依赖性。基于有衰减项的Hebb规则设计了反映“刺激-响应-强化”特征的强化学习算法,使CMSPK具有经典条件反射行为和认知行为。应用CMSPK模型成功地模拟了习得、刺激间隔效应、遗忘、阻止和二阶条件反射等现象。A cognitive model is presented with classical conditioning behaviors. The model comprises a number of spiking neurons connecting to form a neural network with reflex arc structure, which made it fully exhibiting the dependency of classical conditioning on timing. A reinforcement learning method based on the Hebb rule with a decay constant was designed, which was characterized by a property of ＇stimulate-response-reinforcement＇. The model can successfully stimulate many typical experiments such as acquire, inter-stimulus effects, extinction, block, and secondary conditioning.

关 键 词：经典条件反射 认知模型 尖峰神经元 HEBB规则 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]