Equilibrium Propagation for Memristor-Based Recurrent Neural Networks.

ABSTRACT: Among the recent innovative technologies, memristor (memory-resistor) has attracted researchers attention as a fundamental computation element. It has been experimentally shown that memristive elements can emulate synaptic dynamics and are even capable of supporting spike timing dependent plasticity (STDP), an important adaptation rule that is gaining particular interest because of its simplicity and biological plausibility. The overall goal of this work is to provide a novel (theoretical) analog computing platform based on memristor devices and recurrent neural networks that exploits the memristor device physics to implement two variations of the backpropagation algorithm: recurrent backpropagation and equilibrium propagation. In the first learning technique, the use of memristor-based sy

SUBMITTER: Zoppo G 

PROVIDER: S-EPMC7105894 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature