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Found 3 papers, 2 papers with code
Learning-to-learn enables rapid learning with phase-change memory-based in-memory computing
In this work, we pair L2L with in-memory computing NMHW based on phase-change memory devices to build efficient AI models that can rapidly adapt to new tasks.
Memory-enriched computation and learning in spiking neural networks through Hebbian plasticity
Memory is a key component of biological neural systems that enables the retention of information over a huge range of temporal scales, ranging from hundreds of milliseconds up to years.
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H-Mem: Harnessing synaptic plasticity with Hebbian Memory Networks
We show that the network can be optimized to utilize the Hebbian plasticity processes for its computations.
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