The spike-response model(SRM)describes the adaptive behaviors of a biological neuron in response to repeated or prolonged stimulation,so that SRM neurons can avoid information overload and support neural networks for ...The spike-response model(SRM)describes the adaptive behaviors of a biological neuron in response to repeated or prolonged stimulation,so that SRM neurons can avoid information overload and support neural networks for competitive learning.In this work,an artificial SRM neuron with the leaky integrate-and-fire(LIF)functions and the adaptive threshold is firstly implemented by the volatile memris-tive device of Pt/NbO_(x)/TiN.By modulating the volatile speed of the device,the threshold of the SRM neuron is adjusted to achieve the adaptive behaviors,such as the refractory period and the lateral inhi-bition.To demonstrate the function of the SRM neuron,a spiking neu-ral network(SNN)is constructed with the SRM neurons and trained by the unsupervised learning rule,which successfully classifies letters with noises,while a similar SNN with LIF neurons fails.This work demonstrates that the SRM neuron not only emulates the adaptive behaviors of a biological neuron,but also enriches the functionality and unleashes the computational power of SNNs.展开更多
基金This work is supported by the National Key Research and Develop-ment Program of China(Grant no.2018YFE0203802).
文摘The spike-response model(SRM)describes the adaptive behaviors of a biological neuron in response to repeated or prolonged stimulation,so that SRM neurons can avoid information overload and support neural networks for competitive learning.In this work,an artificial SRM neuron with the leaky integrate-and-fire(LIF)functions and the adaptive threshold is firstly implemented by the volatile memris-tive device of Pt/NbO_(x)/TiN.By modulating the volatile speed of the device,the threshold of the SRM neuron is adjusted to achieve the adaptive behaviors,such as the refractory period and the lateral inhi-bition.To demonstrate the function of the SRM neuron,a spiking neu-ral network(SNN)is constructed with the SRM neurons and trained by the unsupervised learning rule,which successfully classifies letters with noises,while a similar SNN with LIF neurons fails.This work demonstrates that the SRM neuron not only emulates the adaptive behaviors of a biological neuron,but also enriches the functionality and unleashes the computational power of SNNs.
