Name
Abstract | ||
|---|---|---|
Nowadays, the use of in vitro reduced models of neuronal networks to investigate the interplay between structural-functional connectivity and the emerging collective dynamics is a widely accepted approach. In this respect, a relevant advance for this kind of studies has been given by the recent introduction of high-density large-scale Micro-Electrode Arrays (MEAs) which have favored the mapping of functional connections and the recordings of the neuronal electrical activity. Although, several toolboxes have been implemented to characterize network dynamics and derive functional links, no specifically dedicated software for the management of huge amount of data and direct estimation of functional connectivity maps has been developed. TOOLCONNECT offers the implementation of up to date algorithms and a user-friendly Graphical User Interface (GUI) to analyze recorded data from large scale networks. It has been specifically conceived as a computationally efficient open-source software tailored to infer functional connectivity by analyzing the spike trains acquired from in vitro networks coupled to MEAs. In the current version, TOOLCONNECT implements correlation- (cross-correlation, partial-correlation) and information theory (joint entropy, transfer entropy) based core algorithms, as well as useful and practical add-ons to visualize functional connectivity graphs and extract some topological features. In this work, we present the software, its main features and capabilities together with some demonstrative applications on hippocampal recordings. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.3389/fninf.2016.00013 | FRONTIERS IN NEUROINFORMATICS |
Keywords | Field | DocType |
functional connectivity,in vitro,micro-electrode arrays,multi-threading,windows form application,neural networks,correlation algorithms,information theory algorithms | Information theory,Multithreading,Transfer entropy,Network dynamics,Computer science,Theoretical computer science,Software,Graphical user interface,Artificial intelligence,Joint entropy,Artificial neural network,Machine learning | Journal |
Volume | Citations | PageRank |
10 | 2 | 0.45 |
References | Authors | |
10 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vito Paolo Pastore | 1 | 5 | 1.66 |
| Daniele Poli | 2 | 2 | 0.45 |
| Aleksandar Godjoski | 3 | 5 | 1.66 |
| S Martinoia | 4 | 158 | 20.68 |
| Paolo Massobrio | 5 | 22 | 5.21 |