# STDP-based spiking deep convolutional neural networks for object recognition

arXiv: https://arxiv.org/abs/1611.01421 · HTML: https://mlsanity.com/paper/1611.01421/

## Key facts

- Cited by 24 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 4 Nov 2016.

## Built on by (most cited first, 24 of 24)

- [SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence](https://mlsanity.com/paper/2310.16620/) (2023)
- [Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks](https://mlsanity.com/paper/2103.12593/) (2021)
- [Constructing Deep Spiking Neural Networks from Artificial Neural Networks with Knowledge Distillation](https://mlsanity.com/paper/2304.05627/) (2023)
- [Backpropagation with Biologically Plausible Spatio-Temporal Adjustment For Training Deep Spiking Neural Networks](https://mlsanity.com/paper/2110.08858/) (2021)
- [Hybrid SNN-ANN: Energy-Efficient Classification and Object Detection for Event-Based Vision](https://mlsanity.com/paper/2112.03423/) (2021)
- [High-performance deep spiking neural networks with 0.3 spikes per neuron](https://mlsanity.com/paper/2306.08744/) (2023)
- [Characterization of Generalizability of Spike Timing Dependent Plasticity trained Spiking Neural Networks](https://mlsanity.com/paper/2105.14677/) (2021)
- [Linear Leaky-Integrate-and-Fire Neuron Model Based Spiking Neural Networks and Its Mapping Relationship to Deep Neural Networks](https://mlsanity.com/paper/2207.04889/) (2022)
- [Spiking SiamFC++: Deep Spiking Neural Network for Object Tracking](https://mlsanity.com/paper/2209.12010/) (2022)
- [SPIDE: A Purely Spike-based Method for Training Feedback Spiking Neural Networks](https://mlsanity.com/paper/2302.00232/) (2023)
- [In-Sensor & Neuromorphic Computing are all you need for Energy Efficient Computer Vision](https://mlsanity.com/paper/2212.10881/) (2022)
- [Paired Competing Neurons Improving STDP Supervised Local Learning In Spiking Neural Networks](https://mlsanity.com/paper/2308.02194/) (2023)
- [SpikePropamine: Differentiable Plasticity in Spiking Neural Networks](https://mlsanity.com/paper/2106.02681/) (2021)
- [Efficient Training of Spiking Neural Networks with Temporally-Truncated Local Backpropagation through Time](https://mlsanity.com/paper/2201.07210/) (2021)
- [An STDP-Based Supervised Learning Algorithm for Spiking Neural Networks](https://mlsanity.com/paper/2203.03379/) (2022)
- [Voltage-Dependent Synaptic Plasticity (VDSP): Unsupervised probabilistic Hebbian plasticity rule based on neurons membrane potential](https://mlsanity.com/paper/2203.11022/) (2022)
- [Models Developed for Spiking Neural Networks](https://mlsanity.com/paper/2212.04377/) (2022)
- [Meta-Learning in Spiking Neural Networks with Reward-Modulated STDP](https://mlsanity.com/paper/2306.04410/) (2023)
- [Advanced Efficient Strategy for Detection of Dark Objects Based on Spiking Network with Multi-Box Detection](https://mlsanity.com/paper/2310.06370/) (2023)
- [Fractional-order spike-timing-dependent gradient descent for multi-layer spiking neural networks](https://mlsanity.com/paper/2410.15293/) (2024)
- [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)
- [SNN-Based Online Learning of Concepts and Action Laws in an Open World](https://mlsanity.com/paper/2411.12308/) (2024)
- [Hardware-Aware Fine-Tuning of Spiking Q-Networks on the SpiNNaker2 Neuromorphic Platform](https://mlsanity.com/paper/2507.23562/) (2025)
- [From Lightweight CNNs to SpikeNets: Benchmarking Accuracy-Energy Tradeoffs with Pruned Spiking SqueezeNet](https://mlsanity.com/paper/2602.09717/) (2026)

## Abstract (arXiv)

Previous studies have shown that spike-timing-dependent plasticity (STDP) can be used in spiking neural networks (SNN) to extract visual features of low or intermediate complexity in an unsupervised manner. These studies, however, used relatively shallow architectures, and only one layer was trainable. Another line of research has demonstrated - using rate-based neural networks trained with back-propagation - that having many layers increases the recognition robustness, an approach known as deep learning. We thus designed a deep SNN, comprising several convolutional (trainable with STDP) and pooling layers. We used a temporal coding scheme where the most strongly activated neurons fire first, and less activated neurons fire later or not at all. The network was exposed to natural images. Thanks to STDP, neurons progressively learned features corresponding to prototypical patterns that were both salient and frequent. Only a few tens of examples per category were required and no label was needed. After learning, the complexity of the extracted features increased along the hierarchy, from edge detectors in the first layer to object prototypes in the last layer. Coding was very sparse, with only a few thousands spikes per image, and in some cases the object category could be reasonably well inferred from the activity of a single higher-order neuron. More generally, the activity of a few hundreds of such neurons contained robust category information, as demonstrated using a classifier on Caltech 101, ETH-80, and MNIST databases. We also demonstrate the superiority of STDP over other unsupervised techniques such as random crops (HMAX) or auto-encoders. Taken together, our results suggest that the combination of STDP with latency coding may be a key to understanding the way that the primate visual system learns, its remarkable processing speed and its low energy consumption.