# BP-STDP: Approximating Backpropagation using Spike Timing Dependent Plasticity

arXiv: https://arxiv.org/abs/1711.04214 · HTML: https://mlsanity.com/paper/1711.04214/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2017.

## Built on by (most cited first, 23 of 23)

- [Optimal ANN-SNN Conversion for Fast and Accurate Inference in Deep Spiking Neural Networks](https://mlsanity.com/paper/2105.11654/) (2021)
- [1000x Faster Camera and Machine Vision with Ordinary Devices](https://mlsanity.com/paper/2201.09302/) (2022)
- [Bottom-up and top-down approaches for the design of neuromorphic processing systems: Tradeoffs and synergies between natural and artificial intelligence](https://mlsanity.com/paper/2106.01288/) (2021)
- [The Backpropagation Algorithm Implemented on Spiking Neuromorphic Hardware](https://mlsanity.com/paper/2106.07030/) (2021)
- [Loss shaping enhances exact gradient learning with Eventprop in spiking neural networks](https://mlsanity.com/paper/2212.01232/) (2022)
- [Spiking-GAN: A Spiking Generative Adversarial Network Using Time-To-First-Spike Coding](https://mlsanity.com/paper/2106.15420/) (2021)
- [Spiking Neural Networks and Bio-Inspired Supervised Deep Learning: A Survey](https://mlsanity.com/paper/2307.16235/) (2023)
- [S-TLLR: STDP-inspired Temporal Local Learning Rule for Spiking Neural Networks](https://mlsanity.com/paper/2306.15220/) (2023)
- [Paired Competing Neurons Improving STDP Supervised Local Learning In Spiking Neural Networks](https://mlsanity.com/paper/2308.02194/) (2023)
- [An optimised deep spiking neural network architecture without gradients](https://mlsanity.com/paper/2109.12813/) (2021)
- [Neuronal Competition Groups with Supervised STDP for Spike-Based Classification](https://mlsanity.com/paper/2410.17066/) (2024)
- [Efficient Training of Spiking Neural Networks with Temporally-Truncated Local Backpropagation through Time](https://mlsanity.com/paper/2201.07210/) (2021)
- [BioGrad: Biologically Plausible Gradient-Based Learning for Spiking Neural Networks](https://mlsanity.com/paper/2110.14092/) (2021)
- [Highly Efficient SNNs for High-speed Object Detection](https://mlsanity.com/paper/2309.15883/) (2023)
- [A survey on learning models of spiking neural membrane systems and spiking neural networks](https://mlsanity.com/paper/2403.18609/) (2024)
- [Training Spiking Neural Networks via Augmented Direct Feedback Alignment](https://mlsanity.com/paper/2409.07776/) (2024)
- [A brain basis of dynamical intelligence for AI and computational neuroscience](https://mlsanity.com/paper/2105.07284/) (2021)
- [Spiking neural networks trained via proxy](https://mlsanity.com/paper/2109.13208/) (2021)
- [Fractional-order spike-timing-dependent gradient descent for multi-layer spiking neural networks](https://mlsanity.com/paper/2410.15293/) (2024)
- [Linear Constraints Learning for Spiking Neurons](https://mlsanity.com/paper/2103.12564/) (2021)
- [A temporally and spatially local spike-based backpropagation algorithm to enable training in hardware](https://mlsanity.com/paper/2207.09755/) (2022)
- [Desire Backpropagation: A Lightweight Training Algorithm for Multi-Layer Spiking Neural Networks based on Spike-Timing-Dependent Plasticity](https://mlsanity.com/paper/2211.05412/) (2022)
- [Energy-Efficient Eimeria Parasite Detection Using a Two-Stage Spiking Neural Network Architecture](https://mlsanity.com/paper/2601.00806/) (2025)

## Abstract (arXiv)

The problem of training spiking neural networks (SNNs) is a necessary precondition to understanding computations within the brain, a field still in its infancy. Previous work has shown that supervised learning in multi-layer SNNs enables bio-inspired networks to recognize patterns of stimuli through hierarchical feature acquisition. Although gradient descent has shown impressive performance in multi-layer (and deep) SNNs, it is generally not considered biologically plausible and is also computationally expensive. This paper proposes a novel supervised learning approach based on an event-based spike-timing-dependent plasticity (STDP) rule embedded in a network of integrate-and-fire (IF) neurons. The proposed temporally local learning rule follows the backpropagation weight change updates applied at each time step. This approach enjoys benefits of both accurate gradient descent and temporally local, efficient STDP. Thus, this method is able to address some open questions regarding accurate and efficient computations that occur in the brain. The experimental results on the XOR problem, the Iris data, and the MNIST dataset demonstrate that the proposed SNN performs as successfully as the traditional NNs. Our approach also compares favorably with the state-of-the-art multi-layer SNNs.