2019/02/02 by Mingyong Zhou, Zhou, Mingyong
Computer Science · Engineering · Neuroscience · #Advanced Memory and Neural Computing #Emerging Technologies (cs.ET) #FOS: Computer and information sciences #Neural Networks and Reservoir Computing #Neural dynamics and brain function #cs.ET
paper · pdf · doi:10.48550/arxiv.1903.02524
8 Pages
arxiv created 2019/02/02 · openalex publication_date 2019/02/02 · arxiv updated 2019/03/07 · openalex created_date 2019/04/11 · openalex updated_date 2026/07/28
In this position paper, we present a discussion on neuromorphic computing and especially the learning/training algorithm to design a series of brains with different memristive values to solve complex ill-posed inverse problems based on a Finite Element(FE) method. First, the neuromorphic computing is addressed and we focus on a type of memristive circuit computing that falls into the scope of neuromorphic computing. Secondly based on reference [1] in which the complex dynamics of the complex memristive circuit was studied, we design a method and an approach to train the memristive circuit so that the memristive values are optimally obtained.