PCL+ spiking network learns recurrent connections with delays via STDP to retain recent visual inputs and predict future ones, reproducing cortical sequence learning and filling missing data in gesture recognition.
Learning delays in spiking neu- ral networks using dilated convolutions with learnable spacings
3 Pith papers cite this work. Polarity classification is still indexing.
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An FPGA implementation of a neuromorphic auditory sensor plus graph neural network achieves 87.43% accuracy on Google Speech Commands v2 with sub-35 µs latency and 1.12 W power.
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.
citing papers explorer
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Predictive Coding Light+: learning to predict visual sequences with spike timing-dependent plasticity and synaptic delays
PCL+ spiking network learns recurrent connections with delays via STDP to retain recent visual inputs and predict future ones, reproducing cortical sequence learning and filling missing data in gesture recognition.
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End-to-End Keyword Spotting on FPGA Using Graph Neural Networks with a Neuromorphic Auditory Sensor
An FPGA implementation of a neuromorphic auditory sensor plus graph neural network achieves 87.43% accuracy on Google Speech Commands v2 with sub-35 µs latency and 1.12 W power.
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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.