Adding temporal memory via LIF, precision-weighted gating, and anticipatory prediction to MoE routers recovers effective expert selection at distribution transitions, with ablation confirming a super-additive beta-ant interaction.
Training spiking neural networks using lessons from deep learning
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SNN-HDC decoding delivers better accuracy, lower latency, and 1.24x-3.67x lower estimated energy than standard methods on DvsGesture and SL-Animals-DVS while detecting 100% of samples from an untrained class.
Quantum neuromorphic kernels outperform parameterized quantum kernels on low-dimensional datasets like Iris but underperform on high-dimensional SDSS data in spectral clustering tasks.
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Affinity Is Not Enough: Recovering the Free Energy Principle in Mixture-of-Experts
Adding temporal memory via LIF, precision-weighted gating, and anticipatory prediction to MoE routers recovers effective expert selection at distribution transitions, with ablation confirming a super-additive beta-ant interaction.
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Hyperdimensional Decoding of Spiking Neural Networks
SNN-HDC decoding delivers better accuracy, lower latency, and 1.24x-3.67x lower estimated energy than standard methods on DvsGesture and SL-Animals-DVS while detecting 100% of samples from an untrained class.
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Quantum Spectral Clustering: Comparing Parameterized and Neuromorphic Quantum Kernels
Quantum neuromorphic kernels outperform parameterized quantum kernels on low-dimensional datasets like Iris but underperform on high-dimensional SDSS data in spectral clustering tasks.