In parallel to the tremendous technical progress in data acquisition (e.g. large number of simultaneous electrode recordings), there is a growing need for new computational tools to analyze and interpret the resulting large data flow from experiments and simulations. Indeed, progressing from single-neuron to network physiology implies a non-linear increase in complexity of data analysis. While there is undeniable progress in novel analysis methods, implementations are difficult to reproduce based on literature or are hidden in (ill-documented, in-house) software collections.

We are developing FIND to address the urgent need of an unified, well-documented interface, following suggested best development practices [1], to various analysis tools. FIND - stands for Finding Information in Neuronal Data and will be shared to the community as an open-source analysis toolbox for electrophysiological recordings and network simulation environments.This platform-independent, MATLAB based - toolbox can be used to analyze neurophysiological data from single- and multiple-electrode recordings by providing a set of standard and more advanced analysis and visualization methods.

Building on experience in design and application of such methods (e.g. the Meatoolbox , see [2]) we will also incorporate other open source toolboxes (e.g. http://neuroanalysis.org/toolkit/, an information theory based toolbox). To enable the incorporation of new algorithms - a weakness of most commercial toolboxes - FIND will be open source, providing the possibility to extend the collection of algorithms and data formats with new ones. We expect that this will facilitate the development and distribution of new techniques among the scientific community.