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Found 4 papers, 2 papers with code
Optimizing the energy consumption of spiking neural networks for neuromorphic applications
We demonstrate first that quantization-aware training of CNNs leads to better accuracy in SNNs.
Adversarial Attacks on Spiking Convolutional Neural Networks for Event-based Vision
Event-based dynamic vision sensors provide very sparse output in the form of spikes, which makes them suitable for low-power applications.
Bio-Inspired, Task-Free Continual Learning through Activity Regularization
We evaluate the new sparse-recurrent version of DFC on the split-MNIST computer vision benchmark and show that only the combination of sparsity and intra-layer recurrent connections improves CL performance with respect to standard backpropagation.
Forward Learning with Top-Down Feedback: Empirical and Analytical Characterization
"Forward-only" algorithms, which train neural networks while avoiding a backward pass, have recently gained attention as a way of solving the biologically unrealistic aspects of backpropagation.
