Sebastiano Curreli

MINT is a toolbox for analyzing multivariate neural information coding and transmission using information theory and dimensionality reduction, applied to simulated and real neural data.

The paper develops bias correction methods for Partial Information Decomposition (PID) in neural data, improving estimates of synergy and redundancy in mouse brain regions.

ASTRA is a deep learning tool for automated segmentation of astrocytic calcium signals in two-photon imaging, outperforming existing methods and enabling large-scale analysis.

This study shows that astrocytic calcium signals in the hippocampus encode spatial information complementary to neurons, enhancing spatial position decoding.