Intelligent-Computing-Lab-Yale / Exploring-Temporal-Information-Dynamics-in-Spiking-Neural-NetworksLinks
PyTorch Implementation of Exploring Temporal Information Dynamics in Spiking Neural Networks (AAAI23)
☆28Updated 2 years ago
Alternatives and similar repositories for Exploring-Temporal-Information-Dynamics-in-Spiking-Neural-Networks
Users that are interested in Exploring-Temporal-Information-Dynamics-in-Spiking-Neural-Networks are comparing it to the libraries listed below
Sorting:
- Pytorch implementation of Neuromorphic Data Augmentation for SNN, Accepted to ECCV 2022.☆40Updated 2 years ago
- GLIF: A Unified Gated Leaky Integrate-and-Fire Neuron for Spiking Neural Networks, NeurIPS 2022 Poster☆51Updated 2 years ago
- [ICASSP2022] RATE CODING OR DIRECT CODING: WHICH ONE IS BETTER FOR ACCURATE, ROBUST, and ENERGY-EFFICIENT SPIKING NEURAL NETWORKS☆19Updated last year
- Membrane Potential Batch Normalization for Spiking Neural Networks☆19Updated last year
- Create a new backward path for more accurate SNN gradients.☆16Updated 10 months ago
- Pytorch implementation of SEENN (Spiking Early Exit Neural Networks) (NeurIPS 2023)☆15Updated 7 months ago
- Neural Architecture Search for Spiking Neural Networks, ECCV2022☆67Updated 2 years ago
- Official code of "AutoSNN: Towards Energy-Efficient Spiking Neural Networks," ICML22☆18Updated 3 years ago
- codes of the paper Rate Gradient Approximation Attack Threats Deep Spiking Neural Networks (CVPR 2023)☆17Updated 10 months ago
- Advancing Spiking Neural Networks towards Deep Residual Learning☆52Updated 2 months ago
- [ICCV2023] Towards Memory- and Time-Efficient Backpropagation for Training Spiking Neural Networks☆42Updated last year
- ☆29Updated last year
- ☆54Updated 2 years ago
- ☆50Updated last year
- [NeurIPS 2022] Online Training Through Time for Spiking Neural Networks☆62Updated last year
- Exploring Lottery Ticket Hypothesis in Sparse Spiking Neural Networks (ECCV2022, oral presentation)☆34Updated 2 years ago
- ☆53Updated last year
- Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networks