| --- |
| 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 |
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| This Hugging Face Dataset page provides a public entry point for the benchmark framework associated with the following paper: |
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| **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** |
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| Hugging Face Paper Page / arXiv preprint: |
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| https://arxiv.org/abs/2503.20841 |
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| ## Overview |
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| This project introduces a benchmark framework for bidirectional neural-domain transfer between unpaired in vitro and in vivo multineuronal spike trains. |
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| 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. |
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| 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. |
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| ## Key Features |
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| - 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 |
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| ## Code |
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| The analysis code is available here: |
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| https://github.com/ShimonoMLab/GenerativeNeurosci_ML-TrDic |
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| ## Data |
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| The dataset associated with the paper is available via Mendeley Data: |
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| https://doi.org/10.17632/kf65cvmtbz.1 |
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| ## Recommended Citation |
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| 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** |
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| Preprint: |
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| **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** |
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| ## Suggested Citation Sentence |
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| Shimono introduced a Transformer + Dice loss framework for bidirectional neural-domain transfer between unpaired in vitro and in vivo multineuronal spike trains. |
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| ## Keywords |
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| generative neuroscience; computational neuroscience; NeuroAI; spike-train generation; sparse neural event prediction; Transformer; Dice loss; in vitro; in vivo; neural-domain transfer; translational neuroscience |
