D-HTM adds a shared associative memory to hierarchical temporal memory so that precursor signatures learned on one entity can trigger preemptive warnings on related entities, yielding an average 8.1-sample lead time on tested datasets.
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QIF neurons outperform LIF neurons in spike-based gradient descent training of spiking neural networks by avoiding discontinuities that fragment the loss landscape.
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Distributed Hierarchical Temporal Memory with Shared Associative Memory for Cross-Entity Preemptive Warning
D-HTM adds a shared associative memory to hierarchical temporal memory so that precursor signatures learned on one entity can trigger preemptive warnings on related entities, yielding an average 8.1-sample lead time on tested datasets.
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Quadratic integrate-and-fire neurons exhibit less fragmented loss landscapes and outperform leaky integrate-and-fire neurons in spike-based gradient descent
QIF neurons outperform LIF neurons in spike-based gradient descent training of spiking neural networks by avoiding discontinuities that fragment the loss landscape.