神经形态系统的通用学习算法及其电路与光学实现

项目名称： 神经形态系统的通用学习算法及其电路与光学实现

项目编号： No.61471010

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 无线电电子学、电信技术

项目作者： 任全胜

作者单位： 北京大学

项目金额： 80万元

中文摘要： 神经形态系统通过电子或光学器件模拟生物神经元、突触及脑运算原理。由此可加深对脑的理解并发展出类脑的新型计算机。美欧已开发出多种电子或光子神经形态系统，它们在实现原理、逼真度、可配置性、容量、运算精度与速度等方面存在诸多差异。国际上适用于多种神经形态系统的通用学习算法的研究才刚起步，现有研究多着眼于特定硬件系统的功能验证，且缺乏机器智能方面的应用。本项目从神经信号处理机制出发，借鉴计算神经科学的相关算法思想，研究适用于多种神经形态硬件系统的通用学习算法，并探索基于脑皮层视听觉信息处理机制的神经形态计算体系，以实现基本的机器智能。采取三个主流的技术路线，分别开发一套FPGA硅神经形态系统，一套基于飞秒激光器和SOA等光器件的光子神经形态系统，及一套忆阻器神经形态网络仿真系统。通过这些实验平台研究学习算法的有效性和通用性，实现机器智能的应用；并结合不同平台的优势，在理论与实验方面做出创新性研究。

中文关键词： 神经形态计算；仿生信息处理；人工智能；光子神经形态系统；FPGA硅神经形态系统

英文摘要： Neuromorphic systems emulate biological neurons, synapses and the principle of brain computations by electronic and optical devices, to further our understanding of the brain and develop brain-like computer. Institutes in America and Europe have developed various electronic and optical neuromorphic systems. There are differences among these systems in many aspects, such as implementation method, fidelity, configurability, capacity, computation precision and speed. The study of general learning algorithms that apply to different neuromorphic systems is just getting started. Existing researches mainly focus on functional verification tests of certain hardware system, while lack applications in artificial intelligence. Starting from the processing mechanism of neuronal spikes and borrowing relevant algorithm ideas from computational neuroscience, our project will study general learning algorithms that apply to different neuromorphic hardware systems, and explore neuromorphic computing architecture based on the visual and auditory information processing mechanism of neocortex, in order to achieve basic artificial intelligence. We will also implement a FPGA(Field Programmable Gate Array)-based neuromorphic system, a photonic neuromorphic system based on optical devices such as femtosecond laser and SOA(Semiconductor Optical Amplifier), as wall as a simulation system of a memristive neuromorphic network, which correspond to three mainstreams of technology roadmap respectively. By means of these three experimental platforms, we could study the validity and universality of the learning algorithms we proposed, and explore applications of neuromorphic intelligence. We will also carry out several innovative researches, which are relevant to the advantages of different hardware systems respectively, from the two aspects of theoretical analysis and experimental design.

英文关键词： Neuromorphic computing;Bionic information processing;Artificial intellegence;Photonic neuromorphic system;FPGA-based silicon neuromorphic system