Placing trainable nonlinear functions on connections in analogue networks enables efficient representation of smooth continuous targets with hardware transfer at projected 30 microwatt power.
The missing memristor found.Nature, 453(7191):80–83
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cond-mat.dis-nn 1 cond-mat.mes-hall 1 cond-mat.mtrl-sci 1 cs.AR 1 cs.LG 1 cs.NE 1 physics.app-ph 1 q-bio.QM 1roles
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A neuron-astrocyte network with dual-timescale memory reduces median path lengths up to sixfold in partially observable grid-world navigation tasks.
Rigid single-molecule junctions show memristive hysteresis from extrinsic mechanical rearrangements at contacts, not internal molecular states.
Annealing-optimized Ag/HZO memristors demonstrate artificial neurons with TTFS, spike-count, and firing-rate coding modes using minimal circuitry.
Gate-tunable analog memcapacitance is demonstrated in LaAlO3/SrTiO3 interface devices, originating from lateral floating gate charge localization with a supporting model.
Self-organising memristive networks exhibit collective nonlinear dynamics that can support physical learning with parallels to biological plasticity and potential for energy-efficient edge intelligence.
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Low-power analogue neural networks with trainable nonlinear connections for continuous control
Placing trainable nonlinear functions on connections in analogue networks enables efficient representation of smooth continuous targets with hardware transfer at projected 30 microwatt power.