A neuron-astrocyte network with dual-timescale memory reduces median path lengths up to sixfold in partially observable grid-world navigation tasks.
The missing memristor found
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
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cond-mat.dis-nn 1 cond-mat.mes-hall 1 cond-mat.mtrl-sci 1 cs.AR 1 cs.NE 1 physics.app-ph 1 q-bio.QM 1roles
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background 3representative citing papers
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.
citing papers explorer
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Dual-Timescale Memory in a Spiking Neuron-Astrocyte Network for Efficient Navigation
A neuron-astrocyte network with dual-timescale memory reduces median path lengths up to sixfold in partially observable grid-world navigation tasks.
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Memristive Switches in Rigid Conjugated Single-Molecule Junctions
Rigid single-molecule junctions show memristive hysteresis from extrinsic mechanical rearrangements at contacts, not internal molecular states.
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Multiple spiking functionalities in annealing-optimized Ag/Hf$_{0.5}$Zr$_{0.5}$O$_2$-based memristive neurons
Annealing-optimized Ag/HZO memristors demonstrate artificial neurons with TTFS, spike-count, and firing-rate coding modes using minimal circuitry.
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Gate-controlled analog memcapacitance in LaAlO3/SrTiO3 interface-based devices
Gate-tunable analog memcapacitance is demonstrated in LaAlO3/SrTiO3 interface devices, originating from lateral floating gate charge localization with a supporting model.
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Self-Organising Memristive Networks as Physical Learning Systems
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.
- Hardware-Software Co-Design of Scalable, Energy-Efficient Analog Recurrent Computations
- Beyond Silicon: Materials, Mechanisms, and Methods for Physical Neural Computing