arXiv Machine Learning July 2, 2026 By Riley Acker, Aman Desai, Garrett Kenyon, Frank Barrows

Self-Organized Learning in Oscillatory Neural Networks with Memristive Signed Couplings

Read the original on arXiv Machine Learning →

arXiv:2607. 00286v1 Announce Type: cross Abstract: Oscillatory neural networks (ONNs) have emerged as a promising neuromorphic architecture, leveraging coupled dynamical systems to perform computation and represent information through phase relationships.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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arXiv AI

Jun 9

Hybridizing Equilibrium Propagation with Ising Machines for Efficient Energy-Based Learning

arXiv:2606. 09112v1 Announce Type: cross Abstract: The rapid evolution of artificial intelligence has led to substantial advances in deep neural networks.

By Chen-Rui Fan, Bo Lu, Xing-Yu Wu, Tie-Jun Wang, Chuan Wang

arXiv Machine Learning

Jul 31

Nanoparticle Networks for Neuromorphic Computing

arXiv:2607. 27844v1 Announce Type: cross Abstract: Physical computing leverages complex dynamical systems for energy-efficient data processing.

By Jonas Mensing, Wilfred G. van der Wiel, Andreas Heuer

arXiv AI

Aug 10

Learning in Deep Networks under Dale's Constraint

arXiv:2608. 06963v1 Announce Type: new Abstract: Biologically plausible learning models aim to explain how neural circuits can implement effective learning under the constraints of real neurons.

By Roy Abel, Shimon Ullman

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arXiv AI

Jun 30

Neuromorphic Energy-Aware Learning for Adaptive Deep Brain Stimulation

arXiv:2606. 28600v1 Announce Type: cross Abstract: Neuromorphic and edge computing research has focused on reducing the inference cost of neural network controllers, yet in physical closed-loop systems the actuator can rival or exceed an efficient controller in energy.

By Binh Nguyen, Colleen Josephson, Mircea Teodorescu, Gert Cauwenberghs, Jason Eshraghian

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arXiv Machine Learning

Jun 26

Generative Models on Analog Hardware with Dynamics

arXiv:2606. 27294v1 Announce Type: cross Abstract: Analog hardware platforms such as coupled oscillators and Analog Ising Machines naturally solve differential equations at a fraction of the energy cost of digital computation, making them attractive for low-power generative modeling, yet a fundamental mismatch exists: modern generative models assume flexible, software-defined dynamics, whereas analog hardware imposes fixed, physics-determined differential equations with limited approximation capacity.

By Yu-Neng Wang, Sara Achour

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arXiv AI

Jun 24

End-to-End Radar and Communication Modulation Recognition with Neuromorphic Computing

arXiv:2606. 24075v1 Announce Type: cross Abstract: Although deep learning-based methods can achieve high accuracy in automatic modulation recognition (AMR) tasks, their high computational cost makes it difficult to strike a balance between accuracy and power consumption, thereby limiting their application on resource-constrained platforms.

By Xiaohu Li, Chongxiao Qu, Caiyong Lin, Chenxiao Dou, Wei Hua

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