A software platform for real-time and adaptive neuroscience experiments

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 and Eva A. Naumann ()
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Anne Draelos: Duke University School of Medicine
Matthew D. Loring: Duke University School of Medicine
Maxim Nikitchenko: Duke University School of Medicine
Chaichontat Sriworarat: Duke University School of Medicine
Pranjal Gupta: Duke University
Daniel Y. Sprague: Duke University School of Medicine
Eftychios Pnevmatikakis: Flatiron Institute
Andrea Giovannucci: University of North Carolina at Chapel Hill / North Carolina State University
Tyler Benster: Stanford University
Karl Deisseroth: Stanford University
John M. Pearson: Duke University School of Medicine
Eva A. Naumann: Duke University School of Medicine

Nature Communications, 2025, vol. 16, issue 1, 1-14

Abstract: Abstract 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.

Date: 2025
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DOI: 10.1038/s41467-025-64856-3

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