Dynamical memristive neural networks and associative self-learning architectures using biomimetic devices.

ABSTRACT: While there is an abundance of research on neural networks that are "inspired" by the brain, few mimic the critical temporal compute features that allow the brain to efficiently perform complex computations. Even fewer methods emulate the heterogeneity of learning produced by biological neurons. Memory devices, such as memristors, are also investigated for their potential to implement neuronal functions in electronic hardware. However, memristors in computing architectures typically operate as non-volatile memories, either as storage or as the weights in a multiply-and-accumulate function that requires direct access to manipulate memristance via a costly learning algorithm. Hence, the integration of memristors into architectures as time-dependent computational units is studied, star

SUBMITTER: Zivasatienraj B 

PROVIDER: S-EPMC10157062 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature