神经形态工程学
计算机科学
神经科学
数码产品
生物神经网络
人工智能
计算机体系结构
人工神经网络
工程类
心理学
电气工程
机器学习
作者
Donhee Ham,Hongkun Park,Seongtaek Hwang,Kinam Kim
标识
DOI:10.1038/s41928-021-00646-1
摘要
Reverse engineering the brain by mimicking the structure and function of neuronal networks on a silicon integrated circuit was the original goal of neuromorphic engineering, but remains a distant prospect. The focus of neuromorphic engineering has thus been relaxed from rigorous brain mimicry to designs inspired by qualitative features of the brain, including event-driven signalling and in-memory information processing. Here we examine current approaches to neuromorphic engineering and provide a vision that returns neuromorphic electronics to its original goal of reverse engineering the brain. The essence of this vision is to ‘copy’ the functional synaptic connectivity map of a mammalian neuronal network using advanced neuroscience tools and then ‘paste’ this map onto a high-density three-dimensional network of solid-state memories. Our copy-and-paste approach could potentially lead to silicon integrated circuits that better approximate computing traits of the brain, including low power, facile learning, adaptation, and even autonomy and cognition. This Perspective explores the potential of an approach to neuromorphic electronics in which the functional synaptic connectivity map of a mammalian neuronal network is copied using a silicon neuro-electronic interface and then pasted onto a high-density three-dimensional network of solid-state memories.
科研通智能强力驱动
Strongly Powered by AbleSci AI