# Spiking Neural Networks -- Part II: Detecting Spatio-Temporal Patterns

arXiv: https://arxiv.org/abs/2010.14217 · HTML: https://mlsanity.com/paper/2010.14217/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2020.

## Built on by (most cited first, 6 of 6)

- [Exploiting Noise as a Resource for Computation and Learning in Spiking Neural Networks](https://mlsanity.com/paper/2305.16044/) (2023)
- [Neuromorphic Wireless Cognition: Event-Driven Semantic Communications for Remote Inference](https://mlsanity.com/paper/2206.06047/) (2022)
- [Knowing When to Stop: Delay-Adaptive Spiking Neural Network Classifiers with Reliability Guarantees](https://mlsanity.com/paper/2305.11322/) (2023)
- [Energy-Efficient On-Board Radio Resource Management for Satellite Communications via Neuromorphic Computing](https://mlsanity.com/paper/2308.11152/) (2023)
- [NeuroMoCo: A Neuromorphic Momentum Contrast Learning Method for Spiking Neural Networks](https://mlsanity.com/paper/2406.06305/) (2024)
- [Inferring Ingrained Remote Information in AC Power Flows Using Neuromorphic Modality Regime](https://mlsanity.com/paper/2407.14883/) (2024)

## Abstract (arXiv)

Inspired by the operation of biological brains, Spiking Neural Networks (SNNs) have the unique ability to detect information encoded in spatio-temporal patterns of spiking signals. Examples of data types requiring spatio-temporal processing include logs of time stamps, e.g., of tweets, and outputs of neural prostheses and neuromorphic sensors. In this paper, the second of a series of three review papers on SNNs, we first review models and training algorithms for the dominant approach that considers SNNs as a Recurrent Neural Network (RNN) and adapt learning rules based on backpropagation through time to the requirements of SNNs. In order to tackle the non-differentiability of the spiking mechanism, state-of-the-art solutions use surrogate gradients that approximate the threshold activation function with a differentiable function. Then, we describe an alternative approach that relies on probabilistic models for spiking neurons, allowing the derivation of local learning rules via stochastic estimates of the gradient. Finally, experiments are provided for neuromorphic data sets, yielding insights on accuracy and convergence under different SNN models.