Learning algorithms for oscillatory neural networks as associative memory for pattern recognition.

ABSTRACT: Alternative paradigms to the von Neumann computing scheme are currently arousing huge interest. Oscillatory neural networks (ONNs) using emerging phase-change materials like VO₂ constitute an energy-efficient, massively parallel, brain-inspired, in-memory computing approach. The encoding of information in the phase pattern of frequency-locked, weakly coupled oscillators makes it possible to exploit their rich non-linear dynamics and their synchronization phenomena for computing. A single fully connected ONN layer can implement an auto-associative memory comparable to that of a Hopfield network, hence Hebbian learning rule is the most widely adopted method for configuring ONNs for such applications, despite its well-known limitations. An extensive amount of literature is availabl

SUBMITTER: Jimenez M 

PROVIDER: S-EPMC10716297 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature