摘要
研究电突触、化学突触以及两者共存对忆阻Rulkov神经模型集体动力学行为的影响。对于两个忆阻Rulkov神经元系统,各种耦合方式都能使系统实现同步。对于不同的耦合强度,神经元呈现不同的放电模式,如方波,三角波,脉冲放电等。当电突触、化学突触同时存在时,系统的同步更依赖于电耦合强度。对全局耦合忆阻Rulkov神经网络同步的研究表明:化学突触单独作用时,同步发生在耦合参数的某个区域范围,当化学耦合强度超过某一阈值时,同步会随着耦合强度的增加而被破坏。电突触单独作用时,系统很快到达同步状态,并且电耦合强度是决定神经元处于静止还是峰放电的关键因素,随着电耦合强度增加,神经元放电频率、振幅增大。当电、化学耦合同时存在时,耦合强度的增加使神经元由静息转变为圆弧放电,并进入同步状态。本文提供了一种通过调整耦合方式和耦合强度,控制神经网络放电模式及其同步的可能方法。
We investigate collective dynamics of memristor Rulkov neural networks depend on electrical synapses and chemical synapses.It is found that for two memristive Rulkov neurons,the system can be synchronized regardless of coupling mode.At different coupling strengths,the neurons present different firing patterns,such as square wave,triangular wave,pulse firing,etc.As electrical synapses and chemical synapses coexist,synchronization of the system is more dependent on the strength of electrical coupling.Synchronization of globally coupled memristive Rulkov neural networks is studied.It is shown that as chemical synapses act alone,synchronization occurs within a certain region of coupling parameters.The synchronization is disrupted as the chemical coupling strength exceeds a certain threshold.As electrical synapses act alone,the system can reach a synchronized state quickly.It is also found that electrical coupling strength is the key factor to determine whether neurons are at rest or firing.As electrical coupling strength increases,firing frequency and amplitude of neuron increase.As electrical and chemical couplings coexist,the increase of coupling strength makes the neurons turn into arc discharge and reach synchronization.It provides a possible way to control firing patterns and synchronization of neural networks by adjusting coupling pattern and coupling strength.
作者
刘丽君
韦笃取
LIU Lijun;WEI Duqu(College of Electronic Engineering,Guangxi Normal University,Guilin,Guangxi 541004,China)
出处
《计算物理》
CSCD
北大核心
2023年第3期389-400,共12页
Chinese Journal of Computational Physics
基金
国家自然科学基金(62062014)
广西自然科学基金(2021JJA170004)资助项目。
关键词
神经网络
忆阻器
电突触
化学突触
同步放电
neural network
memristor
electrical synapse
chemical synapse
synchronized firing
