Andrea Giovannucci

This paper introduces RADICaL, a deep learning framework that infers single-trial neural population dynamics from calcium imaging with sub-frame temporal resolution, showing improved accuracy in synthetic and real mouse data.

The paper investigates how the mouse secondary auditory cortex integrates multi-frequency sounds, showing that SOM interneurons broaden the temporal window for harmonic integration.

VolPy provides an automated, scalable pipeline for analyzing voltage imaging datasets, demonstrating strong performance in neuron localization and spike extraction.

improv enables real-time adaptive neuroscience experiments by integrating modeling, data collection, and analysis across organisms and data types.

This paper introduces two online algorithms for real-time analysis of microendoscopic calcium imaging data, improving computational efficiency and enabling closed-loop experiments.

CaImAn, an open-source library, automates motion correction, source extraction, deconvolution and cross-session registration for calcium imaging, detecting neurons near human level (F1 0.75 batch, 0.76 online).

OnACID processes streaming calcium imaging data in real time by combining online dictionary learning with motion correction, source extraction, and activity deconvolution, tracking hundreds of neurons for closed-loop experiments.

NoRMCorre provides an efficient algorithm for non-rigid motion correction in calcium imaging data, achieving real-time performance on streaming data.