Position: It Is Time We Test Neural Computation In Vitro

Frithjof Gressmann, Ashley Chen, Lily Hexuan Xie, Nancy Amato, Lawrence Rauchwerger
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:81393-81409, 2025.

Abstract

Recent advances in bioengineering have enabled the creation of biological neural networks in vitro, significantly reducing the cost, ethical hurdles, and complexity of experimentation with genuine biological neural computation. In this position paper, we argue that this trend offers a unique and timely opportunity to put our understanding of neural computation to the test. By designing artificial neural networks that can interact and control living neural systems, it is becoming possible to validate computational models beyond simulation and gain empirical insights to help unlock more robust and energy-efficient next-generation AI systems. We provide an overview of key technologies, challenges, and principles behind this development and describe strategies and opportunities for novel machine learning research in this emerging field. We also discuss implications and fundamental questions that could be answered as this technology advances, exemplifying the longer-term impact of increasingly sophisticated in vitro neural networks.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-gressmann25a, title = {Position: It Is Time We Test Neural Computation In Vitro}, author = {Gressmann, Frithjof and Chen, Ashley and Xie, Lily Hexuan and Amato, Nancy and Rauchwerger, Lawrence}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {81393--81409}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/gressmann25a/gressmann25a.pdf}, url = {https://proceedings.mlr.press/v267/gressmann25a.html}, abstract = {Recent advances in bioengineering have enabled the creation of biological neural networks in vitro, significantly reducing the cost, ethical hurdles, and complexity of experimentation with genuine biological neural computation. In this position paper, we argue that this trend offers a unique and timely opportunity to put our understanding of neural computation to the test. By designing artificial neural networks that can interact and control living neural systems, it is becoming possible to validate computational models beyond simulation and gain empirical insights to help unlock more robust and energy-efficient next-generation AI systems. We provide an overview of key technologies, challenges, and principles behind this development and describe strategies and opportunities for novel machine learning research in this emerging field. We also discuss implications and fundamental questions that could be answered as this technology advances, exemplifying the longer-term impact of increasingly sophisticated in vitro neural networks.} }
Endnote
%0 Conference Paper %T Position: It Is Time We Test Neural Computation In Vitro %A Frithjof Gressmann %A Ashley Chen %A Lily Hexuan Xie %A Nancy Amato %A Lawrence Rauchwerger %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-gressmann25a %I PMLR %P 81393--81409 %U https://proceedings.mlr.press/v267/gressmann25a.html %V 267 %X Recent advances in bioengineering have enabled the creation of biological neural networks in vitro, significantly reducing the cost, ethical hurdles, and complexity of experimentation with genuine biological neural computation. In this position paper, we argue that this trend offers a unique and timely opportunity to put our understanding of neural computation to the test. By designing artificial neural networks that can interact and control living neural systems, it is becoming possible to validate computational models beyond simulation and gain empirical insights to help unlock more robust and energy-efficient next-generation AI systems. We provide an overview of key technologies, challenges, and principles behind this development and describe strategies and opportunities for novel machine learning research in this emerging field. We also discuss implications and fundamental questions that could be answered as this technology advances, exemplifying the longer-term impact of increasingly sophisticated in vitro neural networks.
APA
Gressmann, F., Chen, A., Xie, L.H., Amato, N. & Rauchwerger, L.. (2025). Position: It Is Time We Test Neural Computation In Vitro. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:81393-81409 Available from https://proceedings.mlr.press/v267/gressmann25a.html.

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