| Title |
SpiCoDyn: A Toolbox for the Analysis of Neuronal Network Dynamics and Connectivity from Multi-Site Spike Signal Recordings
|
|---|---|
| Published in |
Neuroinformatics, October 2017
|
| DOI | 10.1007/s12021-017-9343-z |
| Pubmed ID | |
| Authors |
Vito Paolo Pastore, Aleksandar Godjoski, Sergio Martinoia, Paolo Massobrio |
| Abstract |
We implemented an automated and efficient open-source software for the analysis of multi-site neuronal spike signals. The software package, named SPICODYN, has been developed as a standalone windows GUI application, using C# programming language with Microsoft Visual Studio based on .NET framework 4.5 development environment. Accepted input data formats are HDF5, level 5 MAT and text files, containing recorded or generated time series spike signals data. SPICODYN processes such electrophysiological signals focusing on: spiking and bursting dynamics and functional-effective connectivity analysis. In particular, for inferring network connectivity, a new implementation of the transfer entropy method is presented dealing with multiple time delays (temporal extension) and with multiple binary patterns (high order extension). SPICODYN is specifically tailored to process data coming from different Multi-Electrode Arrays setups, guarantying, in those specific cases, automated processing. The optimized implementation of the Delayed Transfer Entropy and the High-Order Transfer Entropy algorithms, allows performing accurate and rapid analysis on multiple spike trains from thousands of electrodes. |
Mendeley readers
The data shown below were compiled from readership statistics for 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 65 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 12 | 18% |
| Researcher | 11 | 17% |
| Student > Master | 8 | 12% |
| Student > Bachelor | 4 | 6% |
| Student > Doctoral Student | 3 | 5% |
| Other | 10 | 15% |
| Unknown | 17 | 26% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 20 | 31% |
| Engineering | 11 | 17% |
| Agricultural and Biological Sciences | 4 | 6% |
| Mathematics | 2 | 3% |
| Computer Science | 2 | 3% |
| Other | 5 | 8% |
| Unknown | 21 | 32% |