Single neuromorphic memristor closely emulates multiple synaptic mechanisms for energy efficient neural networks.

ABSTRACT: Biological neural networks do not only include long-term memory and weight multiplication capabilities, as commonly assumed in artificial neural networks, but also more complex functions such as short-term memory, short-term plasticity, and meta-plasticity - all collocated within each synapse. Here, we demonstrate memristive nano-devices based on SrTiO₃ that inherently emulate all these synaptic functions. These memristors operate in a non-filamentary, low conductance regime, which enables stable and energy efficient operation. They can act as multi-functional hardware synapses in a class of bio-inspired deep neural networks (DNN) that make use of both long- and short-term synaptic dynamics and are capable of meta-learning or learning-to-learn. The resulting bio-inspired DNN is

SUBMITTER: Weilenmann C 

PROVIDER: S-EPMC11322324 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature