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<p>Current neuroscience research is often limited to testing predetermined hypotheses and post hoc analysis of already collected data. Adaptive experimental designs, in which modeling drives ongoing data collection and selects experimental manipulations, offer a promising alternative. However, such adaptive paradigms require tight integration between software and hardware under real-time constraints. We introduce <i>improv</i>, a software platform for flexible integration of modeling, data collection, analysis pipelines, and live experimental control. We demonstrate both in silico and in vivo how <i>improv</i> enables efficient experimental designs for discovery and validation across various model organisms and data types. We used <i>improv</i> to orchestrate real-time behavioral analyses, rapid functional typing of neural responses via calcium imaging, optimal visual stimulus selection, and model-driven optogenetic photostimulation of visually responsive neurons in the zebrafish brain. Together, these results demonstrate the power of <i>improv</i> to integrate modeling with data collection and experimental control to achieve next-generation adaptive experiments.</p>

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A software platform for real-time and adaptive neuroscience experiments

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月11日

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  • Anne Draelos,
  • Matthew D. Loring,
  • Maxim Nikitchenko,
  • Chaichontat Sriworarat,
  • Pranjal Gupta,
  • Daniel Y. Sprague,
  • Eftychios Pnevmatikakis,
  • Andrea Giovannucci,
  • Tyler Benster,
  • Karl Deisseroth,
  • John M. Pearson,
  • Eva A. Naumann

摘要

Current neuroscience research is often limited to testing predetermined hypotheses and post hoc analysis of already collected data. Adaptive experimental designs, in which modeling drives ongoing data collection and selects experimental manipulations, offer a promising alternative. However, such adaptive paradigms require tight integration between software and hardware under real-time constraints. We introduce improv, a software platform for flexible integration of modeling, data collection, analysis pipelines, and live experimental control. We demonstrate both in silico and in vivo how improv enables efficient experimental designs for discovery and validation across various model organisms and data types. We used improv to orchestrate real-time behavioral analyses, rapid functional typing of neural responses via calcium imaging, optimal visual stimulus selection, and model-driven optogenetic photostimulation of visually responsive neurons in the zebrafish brain. Together, these results demonstrate the power of improv to integrate modeling with data collection and experimental control to achieve next-generation adaptive experiments.