Synaptic and neural behaviours in a standard silicon transistor.

ABSTRACT: Hardware implementations of artificial neural networks (ANNs)-the most advanced of which are made of millions of electronic neurons interconnected by hundreds of millions of electronic synapses-have achieved higher energy efficiency than classical computers in some small-scale data-intensive computing tasks¹. State-of-the-art neuromorphic computers, such as Intel's Loihi² or IBM's NorthPole³, implement ANNs using bio-inspired neuron- and synapse-mimicking circuits made of complementary metal-oxide-semiconductor (CMOS) transistors, at least 18 per neuron and six per synapse. Simplifying the structure and size of these two building blocks would enable the construction of more sophisticated, larger and more energy-efficient ANNs. Here we show that a single CMO

SUBMITTER: Pazos S 

PROVIDER: S-EPMC11964925 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature