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Found 7 papers, 3 papers with code
sVAD: A Robust, Low-Power, and Light-Weight Voice Activity Detection with Spiking Neural Networks
Spiking Neural Networks (SNNs) are known to be biologically plausible and power-efficient.
LC-TTFS: Towards Lossless Network Conversion for Spiking Neural Networks with TTFS Coding
With TTFS coding, we can achieve up to orders of magnitude saving in computation over ANN and other rate-based SNNs.
TC-LIF: A Two-Compartment Spiking Neuron Model for Long-Term Sequential Modelling
The identification of sensory cues associated with potential opportunities and dangers is frequently complicated by unrelated events that separate useful cues by long delays.
Long Short-term Memory with Two-Compartment Spiking Neuron
The identification of sensory cues associated with potential opportunities and dangers is frequently complicated by unrelated events that separate useful cues by long delays.
A Hybrid Neural Coding Approach for Pattern Recognition with Spiking Neural Networks
As an initial exploration in this direction, we propose a hybrid neural coding and learning framework, which encompasses a neural coding zoo with diverse neural coding schemes discovered in neuroscience.
Training Spiking Neural Networks with Local Tandem Learning
The LTL rule follows the teacher-student learning approach by mimicking the intermediate feature representations of a pre-trained ANN.
Deep Spiking Neural Network with Spike Count based Learning Rule
Deep spiking neural networks (SNNs) support asynchronous event-driven computation, massive parallelism and demonstrate great potential to improve the energy efficiency of its synchronous analog counterpart.
