Research map / Architectures and efficiency / Spiking networks
A Plasticity-Centric Approach to Train the Non-Differential Spiking Neural Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| increase number of hidden neurons (ablation) | VPSNN · MNIST test | accuracy | Helped |
| increase iterations of unsupervised equilibrium state learning (6 vs 20) (ablation) | VPSNN · MNIST t-SNE cluster quality | cluster separation (qualitative) | Helped |
Closest papers by meaning
- Learning the Plasticity: Plasticity-Driven Learning Framework in Spiking Neural Networks (NeurIPS 2025 · similarity 0.91)
- Meta-learning families of plasticity rules in recurrent spiking networks using simulation-based inference (NeurIPS 2023 · similarity 0.89)
- Differentiable Spike: Rethinking Gradient-Descent for Training Spiking Neural Networks (NeurIPS 2021 · similarity 0.89)
- Temporal Flexibility in Spiking Neural Networks: Towards Generalization Across Time Steps and Deployment Friendliness (ICLR 2025 · similarity 0.88)
- Rethinking the Membrane Dynamics and Optimization Objectives of Spiking Neural Networks (NeurIPS 2024 · similarity 0.88)
- Autaptic Synaptic Circuit Enhances Spatio-temporal Predictive Learning of Spiking Neural Networks (ICML 2024 · similarity 0.87)
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