Neuromorphic one-shot learning utilizing a phase-transition material

April 17, 2024
digital image of the human brain

Plasticity of synapses in the brain is responsible for memory, learning and intelligence. Consequently, emulating aspects of biological synaptic plasticity is a fundamental goal in electrical engineering, machine learning and neuromorphic computing. Recently, a new form of synaptic plasticity was discovered in the rodent hippocampus that stores temporally extended memories after just a single trial. Here, we present the first hardware-level emulation of this behavioral timescale synaptic plasticity (BTSP) utilizing a prototypical quantum material, vanadium dioxide (VO2). We apply our circuit design and algorithm to real-time maze solving, which results in rapid learning of an efficient path to reward. Our results open a path to implementation of cutting-edge neuroscience principles in AI hardware with quantum materials.

Alessandro R. Galloni, Yifan Yuan, Minning Zhu, Haoming Yu, Ravindra S. Bisht, Chung-Tse Michael Wu, Christine Grienberger, Shriram Ramanathan, and Aaron D. Milstein

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