Effects of aging and self-organized criticality in a pulse-coupled integrate-and-fire neuron model based on small world networks have been studied. We give the degree distribution of aging network, average shortest p...Effects of aging and self-organized criticality in a pulse-coupled integrate-and-fire neuron model based on small world networks have been studied. We give the degree distribution of aging network, average shortest path length, the diameter of our network, and the clustering coefficient, and find that our neuron model displays the power-law behavior, and with the number of added links increasing, the effects of aging become smaller and smaller. This shows that if the brain works at the self-organized criticality state, it can relieve some effects caused by aging.展开更多
Spiking neural network,inspired by the human brain,consisting of spiking neurons and plastic synapses,is a promising solution for highly efficient data processing in neuromorphic computing.Recently,memristor-based neu...Spiking neural network,inspired by the human brain,consisting of spiking neurons and plastic synapses,is a promising solution for highly efficient data processing in neuromorphic computing.Recently,memristor-based neurons and synapses are becoming intriguing candidates to build spiking neural networks in hardware,owing to the close resemblance between their device dynamics and the biological counterparts.However,the functionalities of memristor-based neurons are currently very limited,and a hardware demonstration of fully memristor-based spiking neural networks supporting in-situ learning is very challenging.Here,a hybrid spiking neuron combining a memristor with simple digital circuits is designed and implemented in hardware to enhance neuron functions.The hybrid neuron with memristive dynamics not only realizes the basic leaky integrate-and-fire neuron function but also enables the in-situ tuning of the connected synaptic weights.Finally,a fully hardware spiking neural network with the hybrid neurons and memristive synapses is experimentally demonstrated for the first time,and in-situ Hebbian learning is achieved with this network.This work opens up a way towards the implementation of spiking neurons,supporting in-situ learning for future neuromorphic computing systems.展开更多
基金The project supported by National Natural Science Foundation of China under Grant No. 10675060
文摘Effects of aging and self-organized criticality in a pulse-coupled integrate-and-fire neuron model based on small world networks have been studied. We give the degree distribution of aging network, average shortest path length, the diameter of our network, and the clustering coefficient, and find that our neuron model displays the power-law behavior, and with the number of added links increasing, the effects of aging become smaller and smaller. This shows that if the brain works at the self-organized criticality state, it can relieve some effects caused by aging.
基金The authors thank the National High Technology Research Development Program(2017YFB0405600 and 2018YFA0701500)the National Key R&D Program(2019FYB2205101)+4 种基金the National Natural Science Foundation of China(61825404,61732020,61821091,61851402,61751401,and 61804171)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB44000000)the China Postdoctoral Science Foundation(2020 M681167)the Major Scientific Research Project of Zhejiang Lab(2019KC0AD02)CASCroucher Funding(CAS18EG01 and 172511KYSB20180135).
文摘Spiking neural network,inspired by the human brain,consisting of spiking neurons and plastic synapses,is a promising solution for highly efficient data processing in neuromorphic computing.Recently,memristor-based neurons and synapses are becoming intriguing candidates to build spiking neural networks in hardware,owing to the close resemblance between their device dynamics and the biological counterparts.However,the functionalities of memristor-based neurons are currently very limited,and a hardware demonstration of fully memristor-based spiking neural networks supporting in-situ learning is very challenging.Here,a hybrid spiking neuron combining a memristor with simple digital circuits is designed and implemented in hardware to enhance neuron functions.The hybrid neuron with memristive dynamics not only realizes the basic leaky integrate-and-fire neuron function but also enables the in-situ tuning of the connected synaptic weights.Finally,a fully hardware spiking neural network with the hybrid neurons and memristive synapses is experimentally demonstrated for the first time,and in-situ Hebbian learning is achieved with this network.This work opens up a way towards the implementation of spiking neurons,supporting in-situ learning for future neuromorphic computing systems.
