# Backpropagated Neighborhood Aggregation for Accurate Training of Spiking Neural Networks

arXiv: https://arxiv.org/abs/2107.06861 · HTML: https://mlsanity.com/paper/2107.06861/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2021.

## Built on by (most cited first, 14 of 14)

- [Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting](https://mlsanity.com/paper/2202.11946/) (2022)
- [Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representation](https://mlsanity.com/paper/2205.00459/) (2022)
- [GLIF: A Unified Gated Leaky Integrate-and-Fire Neuron for Spiking Neural Networks](https://mlsanity.com/paper/2210.13768/) (2022)
- [SNN2ANN: A Fast and Memory-Efficient Training Framework for Spiking Neural Networks](https://mlsanity.com/paper/2206.09449/) (2022)
- [BioLeaF: A Bio-plausible Learning Framework for Training of Spiking Neural Networks](https://mlsanity.com/paper/2111.13188/) (2021)
- [Direct Training via Backpropagation for Ultra-low Latency Spiking Neural Networks with Multi-threshold](https://mlsanity.com/paper/2112.07426/) (2021)
- [Enhancing SNN-based Spatio-Temporal Learning: A Benchmark Dataset and Cross-Modality Attention Model](https://mlsanity.com/paper/2410.15689/) (2024)
- [Efficient Logit-based Knowledge Distillation of Deep Spiking Neural Networks for Full-Range Timestep Deployment](https://mlsanity.com/paper/2501.15925/) (2025)
- [S3NN: Time Step Reduction of Spiking Surrogate Gradients for Training Energy Efficient Single-Step Spiking Neural Networks](https://mlsanity.com/paper/2201.10879/) (2022)
- [A Computational Framework of Cortical Microcircuits Approximates Sign-concordant Random Backpropagation](https://mlsanity.com/paper/2205.07292/) (2022)
- [DS2TA: Denoising Spiking Transformer with Attenuated Spatiotemporal Attention](https://mlsanity.com/paper/2409.15375/) (2024)
- [Synaptic Dynamics Realize First-order Adaptive Learning and Weight Symmetry](https://mlsanity.com/paper/2212.09440/) (2022)
- [Enhanced Self-Distillation Framework for Efficient Spiking Neural Network Training](https://mlsanity.com/paper/2510.06254/) (2025)
- [A Neuromorphic Trigger for Efficient Audio Event Detection](https://mlsanity.com/paper/2606.17775/) (2026)

## Abstract (arXiv)

While backpropagation (BP) has been applied to spiking neural networks (SNNs) achieving encouraging results, a key challenge involved is to backpropagate a continuous-valued loss over layers of spiking neurons exhibiting discontinuous all-or-none firing activities. Existing methods deal with this difficulty by introducing compromises that come with their own limitations, leading to potential performance degradation. We propose a novel BP-like method, called neighborhood aggregation (NA), which computes accurate error gradients guiding weight updates that may lead to discontinuous modifications of firing activities. NA achieves this goal by aggregating finite differences of the loss over multiple perturbed membrane potential waveforms in the neighborhood of the present membrane potential of each neuron while utilizing a new membrane potential distance function. Our experiments show that the proposed NA algorithm delivers the state-of-the-art performance for SNN training on several datasets.