A toolbox for dynamic and connectivity analysis of neuronal spike trains data

Thanks to recent improvements in the neurotechnology, parallel recordings with an ever-increasing number of micro-transducers are now available to monitor the neuronal spiking activity of large-scale neuronal networks. At the same time, continuous improvements are required to develop computationally efficient software for processing and analyzing such huge amounts of data. In this work, we present a new tool named SPICODYN, as a possible solution to efficiently process and analyze big-data coming from in vitro multi-site recordings. By adopting the standardized HDF5 raw input data format it offers independency from the specific acquisition setup. SPICODYN allows performing pre-processing operations (spike detection), full dynamics and functional-effective connectivity analysis on the generated spike trains, and topological characterization related to the estimated connectivity.