SADP replaces exact spike-pair timing with population-level agreement metrics (e.g., Cohen's kappa) to learn in spiking neural networks, and is claimed to beat classical STDP on MNIST and Fashion-MNIST.
A change of direction in pairwise neutrino conversion physics: The effect of collisions
1 Pith paper cite this work. Polarity classification is still indexing.
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abstract
Fast pairwise conversions of neutrinos may affect the flavor distribution in the core of neutrino-dense sources. We explore the interplay between collisions and fast conversions within a simplified framework that assumes angle-independent, direction-changing collisions in a neutrino gas that has no spatial inhomogeneity. Counter to expectations, we find that collisions may enhance fast flavor conversions instead of damping them. Our work highlights the need to take into account the feedback of collisions on the neutrino angular distributions self-consistently, in order to predict the flavor outcome in the context of fast pairwise conversions reliably.
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cs.NE 1years
2025 1verdicts
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Spike Agreement Dependent Plasticity: A scalable Bio-Inspired learning paradigm for Spiking Neural Networks
SADP replaces exact spike-pair timing with population-level agreement metrics (e.g., Cohen's kappa) to learn in spiking neural networks, and is claimed to beat classical STDP on MNIST and Fashion-MNIST.