---
license: cc-by-4.0
tags:
- neuroscience
- computational-neuroscience
- neuroai
- generative-neuroscience
- spike-train
- spike-train-generation
- neural-data
- in-vitro
- in-vivo
- domain-transfer
- neural-domain-transfer
- transformer
- dice-loss
- sparse-time-series
- population-activity
- translational-neuroscience
papers:
- https://arxiv.org/abs/2503.20841
---

# 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