arXiv AI June 3, 2026 By Huannan Zheng, Jingli Liu, Kezhou Yang

Signed Spiking Neuron Enabled by an Orthogonal-Easy-Axis Magnetic Tunnel Junction

arXiv:2606. 03796v1 Announce Type: cross Abstract: Signed spiking neurons carry richer information than standard spiking neurons.

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

arXiv Machine Learning

Jun 3

Quadratic integrate-and-fire neurons exhibit less fragmented loss landscapes and outperform leaky integrate-and-fire neurons in spike-based gradient descent

arXiv:2606. 03935v1 Announce Type: cross Abstract: The ability to train spiking neural networks is essential for modeling biological neural networks as well as for neuromorphic computing.

By Carlo Wenig, Raoul-Martin Memmesheimer, Christian Klos

arXiv Machine Learning

Aug 12

Delays in Spiking Neural Networks: A State Space Model Approach

arXiv:2512. 01906v3 Announce Type: replace Abstract: Spiking neural networks (SNNs) are biologically inspired, event-driven models suited for temporal data processing and energy-efficient neuromorphic computing.

By Sanja Karilanova, Subhrakanti Dey, Ay\c{c}a \"Oz\c{c}elikkale

arXiv Machine Learning

Jul 17

Scalable Training of Continuous-Time Spiking Neural Networks with Differentiable Spike-Time Discretization

arXiv:2607. 14672v1 Announce Type: new Abstract: Continuous-time spiking neural networks (SNNs) provide an event-driven framework for temporal computation, computational neuroscience, and neuromorphic hardware.

By Yusuke Sakemi, Tomoya Takeuchi, Takeo Hosomi, Kazuyuki Aihara

arXiv Machine Learning

Jun 16

Neuromorphic Wireless Split Computing with Resonate-and-Fire Neurons

arXiv:2506. 20015v2 Announce Type: replace Abstract: Neuromorphic computing offers an energy-efficient alternative to conventional deep learning accelerators, particularly for real-time processing of time-series data.

By Dengyu Wu, Jiechen Chen, H. Vincent Poor, Bipin Rajendran, Osvaldo Simeone

efficiency

arXiv AI

Aug 11

SuperNeuroMAT: An Efficient Matrix-based Simulator for Spiking Neural Networks

arXiv:2608. 08479v1 Announce Type: cross Abstract: Spiking neural networks (SNNs) offer a promising pathway to energy-efficient AI and brain-inspired computing.

By Prasanna Date, Kevin Zhu, Shruti Kulkarni, Ashish Gautam, Chathika Gunaratne, Robert Patton, Tyler Nitzsche, Ian Mulet, Zachary Johnson-Scott, Addison Helms, Duncan Rowden, Simon Weston, Maryam Parsa, Catherine Schuman, Thomas Potok

benchmarks

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

Related stories

Quadratic integrate-and-fire neurons exhibit less fragmented loss landscapes and outperform leaky integrate-and-fire neurons in spike-based gradient descent

Delays in Spiking Neural Networks: A State Space Model Approach

Scalable Training of Continuous-Time Spiking Neural Networks with Differentiable Spike-Time Discretization

Neuromorphic Wireless Split Computing with Resonate-and-Fire Neurons

SuperNeuroMAT: An Efficient Matrix-based Simulator for Spiking Neural Networks

Nanoparticle Networks for Neuromorphic Computing