hypothesize that the synergy between temporal-coding, spike-sparsity, and LZC-driven complexity analysis enables more-efficient feature extraction. Our findings demonstrate that SNNs combined with LZC offer promising, biologically plausible [7/8 of https://arxiv.org/abs/2506.06265v1]

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We are excited to present the new paper on NESTML, a user-friendly language and toolchain for simulating SNNs with advanced plasticity rules. NESTML makes it easy for anyone to create and run models across platforms, enhancing accessibility and reproducibility in neuroscience! tinyurl.com/2x7vcc48

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In this paper is presented FeNN, a RISC-V vector soft-processor, tailored to the needs of SNN simulation and the hardware resources available on modern FPGAs. arxiv.org/pdf/2506.11760

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Very cool paper from the University of Chile investigating how local metabolic constraints influence the structure and activity of spiking neural networks (SNNs) journals.plos.org/ploscompbiol... #nestsim

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New preprint for #neuromorphic and #SpikingNeuralNetwork folk (with @pengfei-sun.bsky.social). arxiv.org/abs/2507.16043 Surrogate gradients are popular for training SNNs, but some worry whether they really learn complex temporal spike codes. TLDR: we tested this, and yes they can! 🧵👇 🤖🧠🧪

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