Carbajal Juan Pablo, Martin Daniel A, Chialvo Dante R
Institute for Energy Technology, University of Applied Sciences of Eastern Switzerland, Oberseestrasse 10, 8640 Rapperswil, Switzerland.
Center for Complex Systems and Brain Sciences (CEMSC3) and Instituto de Ciencias Físicas, CONICET, Escuela de Ciencia y Tecnología, Universidad Nacional de General San Martín, Campus Miguelete, CP 1650, 25 de Mayo y Francia, San Martín, Buenos Aires, Argentina.
Phys Rev E. 2022 May;105(5-1):054306. doi: 10.1103/PhysRevE.105.054306.
Recent results revived the interest in the implementation of analog devices able to perform brainlike operations. Here we introduce a training algorithm for a memristor network which is inspired by previous work on biological learning. Robust results are obtained from computer simulations of a network of voltage-controlled memristive devices. Its implementation in hardware is straightforward, being scalable and requiring very little peripheral computation overhead.
近期的研究成果重新唤起了人们对能够执行类脑操作的模拟设备的兴趣。在此，我们介绍一种受先前生物学习研究启发的忆阻器网络训练算法。通过对压控忆阻器件网络进行计算机模拟，获得了可靠的结果。其在硬件中的实现很简单，具有可扩展性，并且几乎不需要外围计算开销。
