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Temporal credit assignment for one-shot learning utilizing a phase transition material
Design of hardware based on biological principles of neuronal computation and plasticity in the brain is a leading approach to realizing energy- and sample-efficient artificial intelligence and learning machines.
Quantum materials for energy-efficient neuromorphic computing
Neuromorphic computing approaches become increasingly important as we address future needs for efficiently processing massive amounts of data.
Universal phase dynamics in VO2 switches revealed by ultrafast operando diffraction
Strongly correlated materials that exhibit an insulator-metal transition are key candidates in the search for new computing platforms.
Materials Science Mesoscale and Nanoscale Physics
ASP: Learning to Forget with Adaptive Synaptic Plasticity in Spiking Neural Networks
Against this backdrop, we present a novel unsupervised learning mechanism ASP (Adaptive Synaptic Plasticity) for improved recognition with Spiking Neural Networks (SNNs) for real time on-line learning in a dynamic environment.
