, Volume 16, Issue 1, pp 15–30 | Cite as

SpiCoDyn: A Toolbox for the Analysis of Neuronal Network Dynamics and Connectivity from Multi-Site Spike Signal Recordings

  • Vito Paolo Pastore
  • Aleksandar Godjoski
  • Sergio Martinoia
  • Paolo Massobrio
Software Original Article


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.


Neuronal networks Multi-electrode arrays Connectivity Transfer entropy Spiking and bursting activity Multi-threading 

Supplementary material

12021_2017_9343_MOESM1_ESM.pdf (3.4 mb)
ESM 1 (PDF 3456 kb)


  1. Bal-Price, A. K., Hogberg, H. T., Buzanska, L., Lenas, P., van Vliet, E., & Hartung, T. (2010). In vitro developmental neurotoxicity (DNT) testing: relevant models and endpoints. Neurotoxicology, 31(5), 545–554. Scholar
  2. Barabasi, A.-L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286, 509–512.CrossRefPubMedGoogle Scholar
  3. Bennett, K., & Robertson, J. (2011). The impact of the data archiving file format on scientific computing and performance of image processing algorithms in MATLAB using large HDF5 and XML multimodal and hyperspectral data sets, MATLAB - A ubiquitous tool for the practical engineer. In C. Ionescu (Ed.), InTech. Available from:
  4. Berdondini, L., Imfeld, K., Maccione, A., Tedesco, M., Neukom, S., Koudelka-Hep, M., et al. (2009). Active pixel sensor array for high spatio-temporal resolution electrophysiological recordings from single cell to large scale neuronal networks. Lab on a Chip, 9, 2644–2651.CrossRefPubMedGoogle Scholar
  5. Bologna, L. L., Pasquale, V., Garofalo, M., Gandolfo, M., Baljon, P. L., Maccione, A., et al. (2010). Investigating neuronal activity by SPYCODE multi-channel data analyzer. Neural Networks, 23(6), 685–697. Scholar
  6. Buzsaki, G. (2004). Large-scale recording of neuronal ensembles. Nat Neurosci, 7(5), 446–451.CrossRefPubMedGoogle Scholar
  7. Chang, E. F. (2015). Towards large-scale, human-based, mesoscopic neurotechnologies. Neuron, 86(1), 68–78. Scholar
  8. Chiappalone, M., Novellino, A., Vajda, I., Vato, A., Martinoia, S., & van Pelt, J. (2005). Burst detection algorithms for the analysis of spatio-temporal patterns in cortical networks of neurons. Neurocomputing, 65-66, 653–662.CrossRefGoogle Scholar
  9. Chiappalone, M., Bove, M., Vato, A., Tedesco, M., & Martinoia, S. (2006). Dissociated cortical networks show spontaneously correlated activity patterns during in vitro development. Brain Research, 1093, 41–53.CrossRefPubMedGoogle Scholar
  10. Egert, U., Knott, T., Schwarz, C., Nawrot, M., Brandt, A., Rotter, S., et al. (2002). MEA-Tools: an open source toolbox for the analysis of multi-electrode data with MATLAB. Journal of Neuroscience Methods, 117, 33–42.CrossRefPubMedGoogle Scholar
  11. Eichler, M., Dahlhaus, R., & Sandkuhler, J. (2003). Partial correlation analysis for the identification of synaptic connections. Biological Cybernetics, 89, 289–302.CrossRefPubMedGoogle Scholar
  12. Eversmann, B., Jenker, M., Hofmann, F., Paulus, C., Brederlow, R., Holzapfl, B., et al. (2003). A 128 x 128 CMOS biosensor array for extracellular recording of neural activity. IEEE Journal of Solid State Circuits, 38, 2306–2317.CrossRefGoogle Scholar
  13. Eytan, D., Minerbi, A., Ziv, N., & Marom, S. (2004). Dopamine-induced dispersion of correlations between action potentials in networks of cortical neurons. Journal of Neurophysiology, 92(3), 1817–1824.CrossRefPubMedGoogle Scholar
  14. Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27, 861–874.CrossRefGoogle Scholar
  15. Garofalo, M., Nieus, T., Massobrio, P., & Martinoia, S. (2009). Evaluation of the performance of information theory-based methods and cross-correlation to estimate the functional connectivity in cortical networks. PLoS One, 4(8), e6482. Scholar
  16. Grace, A. A., & Bunney, B. S. (1984). The control of firing pattern in nigral dopamine neurons: burst firing. Journal of Neuroscience, 4(11), 2877.PubMedGoogle Scholar
  17. HDFGroup (2013). Hierarchical data format
  18. Hogberg, H. T., Sobanski, T., Novellino, A., Whelan, M., Weiss, D. G., & Bal-Price, A. K. (2011). Application of micro-electrode arrays (MEAs) as an emerging technology for developmental neurotoxicity: Evaluation of domoic acid-induced effects in primary cultures of rat cortical neurons. Neurotoxicology, 32(1), 158–168. Scholar
  19. Humphries, M. D., & Gurney, K. (2008). Network 'Small-World-Ness': a quantitative method for determining canonical network equivalence. PLoS One, 3(4), e0002051.CrossRefPubMedGoogle Scholar
  20. Ito, S., Hansen, M. E., Heiland, R., Lumsdaine, A., Litke, A. M., & Beggs, J. M. (2011). Extending Transfer Entropy Improves Identification of Effective Connectivity in a Spiking Cortical Network Model. PLoS One, 6(11), e27431. Scholar
  21. Izhikevich, E. M. (2003). Simple model of spiking neurons. IEEE Transactions on Neural Networks, 14, 1569–1572.CrossRefPubMedGoogle Scholar
  22. Izhikevich, E. M., Gally, J. A., & Edelman, G. M. (2004). Spike-timing dynamics of neuronal groups. Cerebral Cortex, 14, 933–944.CrossRefPubMedGoogle Scholar
  23. Jones, I. L., Livi, P., Lewandowska, M. K., Fiscella, M., Roscic, B., & Hierlemann, A. (2011). The potential of microelectrode arrays and microelectronics for biomedical research and diagnostics. Analytical Bioanalytical Chemistry, 399(7), 2313–2329. Scholar
  24. Kapucu, F. E., Tanskanen, J., Mikkonen, J., Ylä-Outinen, L., Narkilahti, S., & Hyttinen, J. (2012). Burst analysis tool for developing neuronal networks exhibiting highly varying action potential dynamics. (Methods). Frontiers in Computational Neuroscience, 6(38).
  25. Lungarella, M., Pitti, A., & Kuniyoshi, Y. (2007). Information transfer at multiple scales. Phys Rev E, 76, 0561171–05611710.CrossRefGoogle Scholar
  26. Maccione, A., Gandolfo, M., Massobrio, P., Novellino, A., Martinoia, S., & Chiappalone, M. (2009). A novel algorithm for precise identification of spikes in extracellularly recorded neuronal signals. Journal of Neuroscience Methods, 177(1), 241–249.CrossRefPubMedGoogle Scholar
  27. Maccione, A., Gandolfo, M., Zordan, S., Amin, H., Di Marco, S., Nieus, T., et al. (2015). Microelectronics, bioinformatics and neurocomputation for massive neuronal recordings in brain circuits with large scale multielectrode array probes. Brain Research Bulletin, 119(Part B), 118–126. Scholar
  28. Mahmud, M., & Vassanelli, S. (2016). Processing and analysis of multichannel extracellular neuronal signals: State-of-the-Art and Challenges. (Review). Frontiers in Neuroscience, 10, 248. Scholar
  29. Mahmud, M., Pulizzi, R., Vasilaki, E., & Giugliano, M. (2014). QSpike tools: a generic framework for parallel batch preprocessing of extracellular neuronal signals recorded by substrate microelectrode arrays. Frontiers in Neuroinformatics, 8, 26. Scholar
  30. Makarov, V. A., Panetsos, F., & de Feo, O. (2005). A method for determining neural connectivity and inferring the underlying network dynamics using extracellular spike recordings. Journal of Neuroscience Methods, 144(2), 265–279. Scholar
  31. Massobrio, P., Tessadori, J., Chiappalone, M., & Ghirardi, M. (2015). In vitro studies of neuronal networks and synaptic plasticity in invertebrates and in mammals using Multi-Electrode Arrays (MEAs). Neural Plasticity, 2015.
  32. Mazzoni, A., Broccard, F. D., Garcia-Perez, E., Bonifazi, P., Ruaro, M. E., & Torre, V. (2007). On the dynamics of the spontaneous activity in neuronal networks. PLoS One, 2(5), e439. Scholar
  33. Meier, R., Egert, U., Aertsen, A., & Nawrot, M. P. (2008). FIND-a unified framework for neuronal data analysis. Neural Networks, 21, 1085–1093.CrossRefPubMedGoogle Scholar
  34. Muller, J., Ballini, M., Livi, P., Chen, Y., Radivojevic, M., Shadmani, A., et al. (2015). High-resolution CMOS MEA platform to study neurons at subcellular, cellular, and network levels. Lab on a Chip, 15(13), 2767–2780. Scholar
  35. Newman, M. E. J., Moore, C., & Watts, D. J. (2000). Mean-field solution of the small-world network model. Physical Review Letters, 84, 3201–3204.CrossRefPubMedGoogle Scholar
  36. Overbey, L. A., & Todd, M. D. (2009). Dynamic system change detection using a modification of the transfer entropy. Journal of Sound and Vibration, 322(1–2), 438–453. Scholar
  37. Pasquale, V., Martinoia, S., & Chiappalone, M. (2009). A self-adapting approach for the detection of bursts and network bursts in neuronal cultures. Journal of Computational Neuroscience, 29(1–2), 213–229. Scholar
  38. Pastore, V. P., Poli, D., Godjoski, A., Martinoia, S., & Massobrio, P. (2016). ToolConnect: a functional connectivity toolbox for in vitro networks. Frontiers in Neuroinformatics, 10(13).
  39. Poli, D., Pastore, V. P., & Massobrio, P. (2015). Functional connectivity in in vitro neuronal assemblies. Frontiers in Neural Circuits, 9(57).
  40. Poli, D., Pastore, V. P., Martinoia, S., & Massobrio, P. (2016). From functional to structural connectivity using partial correlation in neuronal assemblies. Journal of Neural Engineering, 13(2), 026023.CrossRefPubMedGoogle Scholar
  41. Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. (Research Support, Non-U.S. Gov't). NeuroImage, 52(3), 1059–1069. Scholar
  42. Schreiber, T. (2000). Measuring Information Transfer. Physical Review Letters, 85(2), 461–464.CrossRefPubMedGoogle Scholar
  43. Schröder, S., Cecchetto, C., Keil, S., Mahmud, M., Brose, E., Özgü, D., et al. (2015). CMOS-compatible purely capacitive interfaces for high-density in-vivo recording from neural tissue. In 2015 IEEE Biomedical Circuits and Systems Conference (BioCAS), 22–24 Oct. 2015 (pp. 1–4).
  44. Shulyzki, R., Abdelhalim, K., Bagheri, A., Salam, M. T., Florez, C. M., Velazquez, J. L. P., et al. (2015). 320-Channel Active Probe for High-Resolution Neuromonitoring and Responsive Neurostimulation. IEEE Transactions on Biomedical Circuits and Systems, 9(1), 34–49. Scholar
  45. Song, S., Miller, K. D., & Abbott, L. F. (2000). Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nature Neuroscience, 3(9), 919–926.CrossRefPubMedGoogle Scholar
  46. Tam, D. C. (2002). An alternate burst analysis for detecting intra-burst firings based on inter-burst periods. Neurocomputing, 44-46, 1155–1159.CrossRefGoogle Scholar
  47. Van Bussel, F., Kriener, B., & Timme, M. (2011). Inferring synaptic connectivity from spatio-temporal spike patterns. (Original Research). Frontiers in Computational Neuroscience, 5, 3. Scholar
  48. van den Heuvel, M. P., & Sporns, O. (2013). Network hubs in the human brain. Trends Cognitive Science, 17(12), 683–696. Scholar
  49. Vassanelli, S. (2014). Multielectrode and Multitransistor Arrays for In Vivo Recording. In M. De Vittorio, L. Martiradonna, & J. Assad (Eds.), Nanotechnology and Neuroscience: Nano-electronic, Photonic and Mechanical Neuronal Interfacing (pp. 239–267). New York: Springer New York.CrossRefGoogle Scholar
  50. Vato, A., Bonzano, L., Chiappalone, M., Cicero, S., Morabito, F., Novellino, A., et al. (2004). Spike manager: a new tool for spontaneous and evoked neuronal networks activity characterization. Neurocomputing, 58(60), 1153–1161.CrossRefGoogle Scholar
  51. Viswam, V., Dragas, J., Muller, J., & Hierlemann, A. (2016). Multi-functional microelectrode array system featuring 59,760 electrodes, 2048 electrophysiology channels, impedance and neurotransmitter measurement units. In IEEE International Solid-State Circuits Conference, San Francisco (US), 2016 (pp 394–396): IEEE. doi:
  52. Wagenaar, D., DeMarse, T. B., & Potter, S. M. (2005). MeaBench: A toolset for multi-electrode data acquisition and on-line analysis. In 2nd International IEEE EMBS Conference on Neural Engineering, Arlington, 16-19 March 2005: IEEE. doi:
  53. Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of 'small-world' networks. Nature, 393, 440–442.CrossRefPubMedGoogle Scholar
  54. Yeung, M. K. S., Tegnér, J., & Collins, J. J. (2002). Reverse engineering gene networks using singular value decomposition and robust regression. Proceedings National Academy Science, 99(9), 6163–6168. Scholar
  55. Yuste, R. (2015). From the neuron doctrine to neural networks. Nature Review Neuroscience, 16, 487–497.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of GenovaGenovaItaly
  2. 2.Brain GmbHWädenswilSwitzerland
  3. 3.Institute of Biophysics, National Research Council (CNR)GenovaItaly

Personalised recommendations