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   10.64898/2026.06.16.732723

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   10.64898/2026.06.11.730447

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   10.1101/2024.05.21.595210

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   10.1101/2025.05.23.655388

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   10.64898/2026.05.29.728861

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   10.64898/2026.06.28.735097

7. Reinforcement learning for closed-loop optimisation of spatiotemporal stimulation in patterned neuronal networks

   This article has 8 authors:

   1. Benedikt Maurer
   2. Vaiva Vasiliauskaitė
   3. Julian Hengsteler
   4. Gino Cathomen
   5. Tobias Ruff
   6. Cedric Schmid
   7. János Vörös
   8. Stephan J. Ihle

   This article has been curated by 1 group:

   • Curated by eLife

     eLife Assessment

     This study presents an open-source reinforcement learning framework for the real-time, closed-loop optimization of spatiotemporal electrical stimulation in engineered neuronal networks. Using single-spike-resolution activity as continuous feedback, the authors provide solid evidence that their platform can identify stimulation patterns that drive specific activity motifs within a structurally constrained four-node circuit. While this reproducible system offers a valuable and accessible tool for interacting with biological neural networks in an adaptive manner, further validation is needed to determine how well these stimulation strategies generalize to larger, unstructured, or more conventional network architectures.

   Reviewed by eLife

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   10.64898/2026.07.14.738598

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   10.64898/2026.05.29.728767

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    10.64898/2026.04.21.719886