Projecting the self-supervised ESN loss onto the input subspace yields an online perturbation rule with O(n_in) variance instead of O(n_r^2)/O(n_r), with the orthogonal residual removed by a static map.
(28) Zhong, Y.; Tang, J.; Li, X.; Gao, B.; Qian, H.; Wu, H., Dynamic memristor - based reservoir computing for high - efficiency temporal signal processing
7 Pith papers cite this work, alongside 2,045 external citations. Polarity classification is still indexing.
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external citations · OpenAlex
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Physical neural substrates realize inference and adaptation via native physics and occupy complementary regimes; no single platform dominates the proposed static/dynamic benchmarks.
A hybrid approach produces a simple circuit implementation of a bursting neuron from phase-locked loop equations.
Coupled Arnold cat maps on circulant graphs exhibit constant Kolmogorov-Sinai entropy independent of connectivity due to translational symmetry.
OpenPRC provides a schema-driven framework with five modules for GPU physics simulation, experimental vision ingestion, reservoir learning, information analysis, and physics-aware optimization to enable consistent PRC evaluation from simulations and real experiments.
Spin wave-based physical reservoir computing achieves 85.8% speaker classification accuracy without cochleagram preprocessing.
Non-steady-state chemical charge transport dynamics integrated into reservoir computing enable waveform recognition, voice identification, and chaos prediction, with performance governed by frequency alignment that functions as a chemically-tuned band-pass filter.
citing papers explorer
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Scalable Perturbation Learning for Online Self-Supervised Learning in Echo State Networks
Projecting the self-supervised ESN loss onto the input subspace yields an online perturbation rule with O(n_in) variance instead of O(n_r^2)/O(n_r), with the orthogonal residual removed by a static map.
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Beyond Silicon: Materials, Mechanisms, and Methods for Physical Neural Computing
Physical neural substrates realize inference and adaptation via native physics and occupy complementary regimes; no single platform dominates the proposed static/dynamic benchmarks.
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Electronic Bursting Neuron: design, equations and hardware implementation
A hybrid approach produces a simple circuit implementation of a bursting neuron from phase-locked loop equations.
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Coupled Arnol'd cat maps on circulant graphs
Coupled Arnold cat maps on circulant graphs exhibit constant Kolmogorov-Sinai entropy independent of connectivity due to translational symmetry.
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OpenPRC: A Unified Open-Source Framework for Physics-to-Task Evaluation in Physical Reservoir Computing
OpenPRC provides a schema-driven framework with five modules for GPU physics simulation, experimental vision ingestion, reservoir learning, information analysis, and physics-aware optimization to enable consistent PRC evaluation from simulations and real experiments.
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Spoken Digit Recognition and Speaker Classification by Nonlinear Interfered Spin Wave-Based Physical Reservoir Computing
Spin wave-based physical reservoir computing achieves 85.8% speaker classification accuracy without cochleagram preprocessing.
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Exploring Non-Steady-State Charge Transport Dynamics in Information Processing: Insights from Reservoir Computing
Non-steady-state chemical charge transport dynamics integrated into reservoir computing enable waveform recognition, voice identification, and chaos prediction, with performance governed by frequency alignment that functions as a chemically-tuned band-pass filter.