Nori778/InVitro-InVivo-SpikeTrain-Generation

In Vitro–In Vivo Spike-Train Generation Benchmark

This Hugging Face Dataset page provides a public entry point for the benchmark framework associated with the following paper:

Shimono, M. (2026). In Vitro to In Vivo: Bidirectional and High-Precision Generation of In Vitro and In Vivo Neuronal Spike Data. Algorithms, 19(4), 305. https://doi.org/10.3390/a19040305

Hugging Face Paper Page / arXiv preprint:

https://arxiv.org/abs/2503.20841

Overview

This project introduces a benchmark framework for bidirectional neural-domain transfer between unpaired in vitro and in vivo multineuronal spike trains.

The central question is whether spontaneous population activity recorded in vitro can be transformed into in vivo-like neural activity, and whether in vivo activity can likewise be transformed into in vitro-like activity.

Because independent in vitro and in vivo recordings usually do not contain one-to-one matched neurons, the task is formulated as time-resolved neural-domain transfer between unpaired sparse binary spike-train sequences.

Key Features

• Bidirectional in vitro-to-in-vivo and in vivo-to-in-vitro spike-train generation
• Sparse binary multineuronal time-series modeling
• 1-ms binned 128-unit spike-train representation
• Autoregressive Transformer model
• Dice loss for extreme spike-event sparsity
• ROC-AUC, Precision–Recall curves, and PR-AUC / average precision evaluation
• Benchmark concept for generative neuroscience and neural-domain transfer

Code

The analysis code is available here:

https://github.com/ShimonoMLab/GenerativeNeurosci_ML-TrDic

Data

The dataset associated with the paper is available via Mendeley Data:

https://doi.org/10.17632/kf65cvmtbz.1

Recommended Citation

If you use the code, dataset structure, benchmark concept, preprocessing procedure, evaluation procedure, or any modified version of the repository, please cite:

Shimono, M. (2026). In Vitro to In Vivo: Bidirectional and High-Precision Generation of In Vitro and In Vivo Neuronal Spike Data. Algorithms, 19(4), 305. https://doi.org/10.3390/a19040305

Preprint:

Shimono, M. (2025). In vitro 2 In vivo: Bidirectional and High-Precision Generation of In Vitro and In Vivo Neuronal Spike Data. arXiv:2503.20841. https://arxiv.org/abs/2503.20841

Suggested Citation Sentence

Shimono introduced a Transformer + Dice loss framework for bidirectional neural-domain transfer between unpaired in vitro and in vivo multineuronal spike trains.

Keywords

generative neuroscience; computational neuroscience; NeuroAI; spike-train generation; sparse neural event prediction; Transformer; Dice loss; in vitro; in vivo; neural-domain transfer; translational neuroscience