CADAD adds activity-dependent dynamic delays to SNNs, improving accuracy on speech datasets while cutting parameter count by about 50% versus prior static delay approaches.
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A recurrent SNN with heterogeneous synaptic delays (D=41) achieves perfect F1=1.0 recall of 16 arbitrary spike patterns on a synthetic benchmark by representing them as chains of overlapping spiking motifs.
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Congestion-Aware Dynamic Axonal Delay for Spiking Neural Networks
CADAD adds activity-dependent dynamic delays to SNNs, improving accuracy on speech datasets while cutting parameter count by about 50% versus prior static delay approaches.
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Working Memory in a Recurrent Spiking Neural Networks With Heterogeneous Synaptic Delays
A recurrent SNN with heterogeneous synaptic delays (D=41) achieves perfect F1=1.0 recall of 16 arbitrary spike patterns on a synthetic benchmark by representing them as chains of overlapping spiking motifs.