# Exploring Spiking Neural Networks for Binary Classification in Multivariate Time Series at the Edge

arXiv: https://arxiv.org/abs/2510.20997 · HTML: https://mlsanity.com/paper/2510.20997/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 42 references that are in the map.
- First posted on arXiv 23 Oct 2025.

## Builds on (most cited first, 9 of 9)

- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models](https://mlsanity.com/paper/2410.05229/) (2024)
- [A Survey of Neuromorphic Computing and Neural Networks in Hardware](https://mlsanity.com/paper/1705.06963/) (2017)
- [A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasoners](https://mlsanity.com/paper/2406.11050/) (2024)
- [Event-Based Backpropagation can compute Exact Gradients for Spiking Neural Networks](https://mlsanity.com/paper/2009.08378/) (2020)
- [Neuro-memristive Circuits for Edge Computing: A review](https://mlsanity.com/paper/1807.00962/) (2018)
- [Unsupervised Anomaly Detection in Stream Data with Online Evolving Spiking Neural Networks](https://mlsanity.com/paper/1912.08785/) (2019)
- [Ensembles of Spiking Neural Networks](https://mlsanity.com/paper/2010.14619/) (2020)
- [Automatic Seizure Prediction using CNN and LSTM](https://mlsanity.com/paper/2211.02679/) (2022)

## Built on by (most cited first, 1 of 1)

- [Event-Native Symbolic-Temporal Spike Encoding Framework for Heterogeneous Cyber Streams](https://mlsanity.com/paper/2609.15772/) (2026)

## Abstract (arXiv)

We present a general framework for training spiking neural networks (SNNs) to perform binary classification on multivariate time series, with a focus on step-wise prediction and high precision at low false alarm rates. The approach uses the Evolutionary Optimization of Neuromorphic Systems (EONS) algorithm to evolve sparse, stateful SNNs by jointly optimizing their architectures and parameters. Inputs are encoded into spike trains, and predictions are made by thresholding a single output neuron's spike counts. We also incorporate simple voting ensemble methods to improve performance and robustness. To evaluate the framework, we apply it with application-specific optimizations to the task of detecting low signal-to-noise ratio radioactive sources in gamma-ray spectral data. The resulting SNNs, with as few as 49 neurons and 66 synapses, achieve a 51.8% true positive rate (TPR) at a false alarm rate of 1/hr, outperforming PCA (42.7%) and deep learning (49.8%) baselines. A three-model any-vote ensemble increases TPR to 67.1% at the same false alarm rate. Hardware deployment on the microCaspian neuromorphic platform demonstrates 2mW power consumption and 20.2ms inference latency. We also demonstrate generalizability by applying the same framework, without domain-specific modification, to seizure detection in EEG recordings. An ensemble achieves 95% TPR with a 16% false positive rate, comparable to recent deep learning approaches with significant reduction in parameter count.