NIT: an open-source tool for information theoretic analysis of neural population data

Information theory provides a popular and principled framework for the analysis of neural data. It allows to uncover in an assumption-free way how neurons encode and transmit information, capturing both linear and non-linear coding mechanisms and including the information carried by interactions of any order. To facilitate its application, here we present Neuroscience Information Toolbox (NIT), a new toolbox for the accurate information theoretical analysis of neural data. NIT contains widely used tools such as limited sampling bias corrections and discretization of neural probabilities for the calculation of stimulus coding in low-dimensional representation of neural activity (e.g. Local Field Potentials or the activity of small neural population).Importantly, it adds a range of recent tools for quantifying information encoding by large populations of neurons or brain areas, for the directed transmission of information between neurons or areas, and for the calculation of Partial Information Decompositions to quantify the behavioral relevance of neural information and the synergy and redundancy among neurons and brain areas. Further, because information theoretic algorithms have been previously validated mainly with electrophysiological recordings, here we used realistic simulations and analysis of real data to study how to optimally apply information theory to the analysis of two-photon calcium imaging data, which are particularly challenging due to their lower signal-to-noise and temporal resolution. We also included algorithms (based on parametric and non-parametric copulas) to compute robustly information specifically with analog signals such as calcium traces. We provide indications on how to best process calcium imaging traces and to apply NIT depending on the type of calcium indicator, imaging frame rate and firing rate levels. In sum, NIT provides a toolbox for the comprehensive and effective information theoretic analysis of all kinds of neural data, including calcium imaging. ### Competing Interest Statement The authors have declared no competing interest.