improv: A flexible software platform for adaptive neuroscience experiments

Autor: Maxim Nikitchenko, Daniel Sprague, Eftychios A. Pnevmatikakis, Chaichontat Sriworarat, Andrea Giovannucci, Eva A. Naumann, John M. Pearson, Matthew D. Loring, Anne Draelos
Rok vydání: 2021
Předmět:
Popis: Neuroscientists now routinely record the activity of large numbers of neurons at high temporal and spatial resolution. With these capabilities comes the promise of causally intervening during these recordings by perturbing neurons or changing experimental conditions, requiring tight integration between data acquisition, analysis, and manipulation. Unfortunately, solutions for real-time interventions are rare, difficult to design and implement, and remain largely unused. Here, we introduce improv, a software platform that allows users to flexibly specify and manage adaptive experiments to integrate data collection, preprocessing, visualization, and user-defined analytics. Using improv for streaming data analysis for two photon calcium imaging and behavior we demonstrate how access to online information can be used for automated, integrated experimentation.
Databáze: OpenAIRE