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.
Surrogate gradient learning in spiking neural networks.IEEE Signal Processing Magazine, 36(6):51–63
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BMRUs enable analog recurrent neural network hardware via discrete outputs that suppress noise 20-fold, with one-to-one parameter-to-circuit mapping and linear power scaling for recurrence.
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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.
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Hardware-Software Co-Design of Scalable, Energy-Efficient Analog Recurrent Computations
BMRUs enable analog recurrent neural network hardware via discrete outputs that suppress noise 20-fold, with one-to-one parameter-to-circuit mapping and linear power scaling for recurrence.