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Intracortical motor decoding can now adapt to new sessions without retraining by learning a compact, four-dimensional "association profile" for each neuron.
Injecting calibrated noise from RRAM's inherent randomness allows for high-utility, privacy-preserving training with minimal accuracy loss and substantial energy savings.
