# Exploiting Neuron and Synapse Filter Dynamics in Spatial Temporal Learning of Deep Spiking Neural Network

arXiv: https://arxiv.org/abs/2003.02944 · HTML: https://mlsanity.com/paper/2003.02944/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2020.

## Built on by (most cited first, 30 of 37)

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- [Attacking the Spike: On the Transferability and Security of Spiking Neural Networks to Adversarial Examples](https://mlsanity.com/paper/2209.03358/) (2022)
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- [SpGesture: Source-Free Domain-adaptive sEMG-based Gesture Recognition with Jaccard Attentive Spiking Neural Network](https://mlsanity.com/paper/2405.14398/) (2024)
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- [Why Deep Models Often cannot Beat Non-deep Counterparts on Molecular Property Prediction?](https://mlsanity.com/paper/2306.17702/) (2023)
- [Retain, Blend, and Exchange: A Quality-aware Spatial-Stereo Fusion Approach for Event Stream Recognition](https://mlsanity.com/paper/2406.18845/) (2024)
- [Combining Spiking Neural Network and Artificial Neural Network for Enhanced Image Classification](https://mlsanity.com/paper/2102.10592/) (2021)
- [SIT: A Bionic and Non-Linear Neuron for Spiking Neural Network](https://mlsanity.com/paper/2203.16117/) (2022)
- [Can Pre-trained Models Really Learn Better Molecular Representations for AI-aided Drug Discovery?](https://mlsanity.com/paper/2209.07423/) (2022)

## Abstract (arXiv)

The recent discovered spatial-temporal information processing capability of bio-inspired Spiking neural networks (SNN) has enabled some interesting models and applications. However designing large-scale and high-performance model is yet a challenge due to the lack of robust training algorithms. A bio-plausible SNN model with spatial-temporal property is a complex dynamic system. Each synapse and neuron behave as filters capable of preserving temporal information. As such neuron dynamics and filter effects are ignored in existing training algorithms, the SNN downgrades into a memoryless system and loses the ability of temporal signal processing. Furthermore, spike timing plays an important role in information representation, but conventional rate-based spike coding models only consider spike trains statistically, and discard information carried by its temporal structures. To address the above issues, and exploit the temporal dynamics of SNNs, we formulate SNN as a network of infinite impulse response (IIR) filters with neuron nonlinearity. We proposed a training algorithm that is capable to learn spatial-temporal patterns by searching for the optimal synapse filter kernels and weights. The proposed model and training algorithm are applied to construct associative memories and classifiers for synthetic and public datasets including MNIST, NMNIST, DVS 128 etc.; and their accuracy outperforms state-of-art approaches.