# SSI-GAN: Semi-Supervised Swin-Inspired Generative Adversarial Networks for Neuronal Spike Classification

arXiv: https://arxiv.org/abs/2601.00189 · HTML: https://mlsanity.com/paper/2601.00189/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 51 references that are in the map.
- First posted on arXiv 1 Jan 2026.

## Builds on (most cited first, 8 of 8)

- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition](https://mlsanity.com/paper/2010.10504/) (2020)
- [Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark](https://mlsanity.com/paper/1806.01427/) (2018)
- [SpikeDeep-Classifier: A deep-learning based fully automatic offline spike sorting algorithm](https://mlsanity.com/paper/1912.10749/) (2019)
- [Automated detection of Zika and dengue in Aedes aegypti using neural spiking analysis](https://mlsanity.com/paper/2312.08654/) (2023)
- [Functional Classification of Spiking Signal Data Using Artificial Intelligence Techniques: A Review](https://mlsanity.com/paper/2409.17516/) (2024)
- [Reducing Labeling Costs in Sentiment Analysis via Semi-Supervised Learning](https://mlsanity.com/paper/2410.11355/) (2024)

## Abstract (arXiv)

Mosquitos are the main transmissive agents of arboviral diseases. Manual classification of their neuronal spike patterns is very labor-intensive and expensive. Most available deep learning solutions require fully labeled spike datasets and highly preprocessed neuronal signals. This reduces the feasibility of mass adoption in actual field scenarios. To address the scarcity of labeled data problems, we propose a new Generative Adversarial Network (GAN) architecture that we call the Semi-supervised Swin-Inspired GAN (SSI-GAN). The Swin-inspired, shifted-window discriminator, together with a transformer-based generator, is used to classify neuronal spike trains and, consequently, detect viral neurotropism. We use a multi-head self-attention model in a flat, window-based transformer discriminator that learns to capture sparser high-frequency spike features. Using just 1 to 3% labeled data, SSI-GAN was trained with more than 15 million spike samples collected at five-time post-infection and recording classification into Zika-infected, dengue-infected, or uninfected categories. Hyperparameters were optimized using the Bayesian Optuna framework, and performance for robustness was validated under fivefold Monte Carlo cross-validation. SSI-GAN reached 99.93% classification accuracy on the third day post-infection with only 3% labeled data. It maintained high accuracy across all stages of infection with just 1% supervision. This shows a 97-99% reduction in manual labeling effort relative to standard supervised approaches at the same performance level. The shifted-window transformer design proposed here beat all baselines by a wide margin and set new best marks in spike-based neuronal infection classification.