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Stochastic Multi-Scale Modelling for the Analysis of Closed-Loop Interactions among Brain Networks

Total Cost €


EC-Contrib. €






Project "STOMMAC" data sheet

The following table provides information about the project.


Organization address
address: VIA MOREGO 30
city: GENOVA
postcode: 16163

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Italy [IT]
 Project website
 Total cost 168˙277 €
 EC max contribution 168˙277 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2014
 Funding Scheme MSCA-IF-EF-ST
 Starting year 2015
 Duration (year-month-day) from 2015-07-01   to  2017-06-30


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 


 Project objective

Brain functions likely emerge from the concerted, context-dependent operations of its microscopic and macroscopic networks. Therefore, the organization and operational principles of such complex systems may be best investigated by using multi-modal approaches, including concurrent measurements of neural activity on multiple spatiotemporal scales. Performing and interpreting such multi-scale measures, though, presents enormous challenges for both experimental and mathematical neuroscientists. Existing analysis methods, however, make limited use of newly acquired concurrent multi-scale information. To dramatically advance analysis methods for these data, I propose a novel multi-scale model that bridges the gap between single cell statistics and neural mass signals. The model describes mixed discrete statistics, covering single cell and small population spike trains, as well as continuous statistics, such as those describing mesoscopic and macroscopic measures of mass neural activity in neuroimaging experiments. These elements are combined by means of copulas, describing multivariate interactions within and between scales of activity. I will extend this method to the level of efficient applicability and, in the course of a secondment with Prof. Logothetis at the Max Planck Institute in Tübingen, exploit the technique to solve current problems of multi-scale analysis of interactions between cortical and subcortical brain areas. The new mathematical approach will lead to a set of new tools that I will disseminate in an Open Source format for maximal impact, and will be widely applicable to multi-modal recordings in animals and humans, in both research and clinical settings. My own application of this method to multi-modal datasets will establish a deeper general understanding of the mechanisms of large-scale communication among brain areas, with particular emphasis on the principles of communication among networks involved in the formation of declarative memory.


year authors and title journal last update
List of publications.
2016 Onken, Arno; Panzeri, Stefano
Concurrent Analysis of Neural Activity at Multiple Scales Using Mixed Vine Copulas
published pages: , ISSN: , DOI: 10.12751/nncn.bc2016.0213
Bernstein Conference 2016 1 2019-07-24
2016 Arno Onken, Jian K. Liu, P. P. Chamanthi R. Karunasekara, Ioannis Delis, Tim Gollisch, Stefano Panzeri
Using Matrix and Tensor Factorizations for the Single-Trial Analysis of Population Spike Trains
published pages: e1005189, ISSN: 1553-7358, DOI: 10.1371/journal.pcbi.1005189
PLOS Computational Biology 12/11 2019-07-24
2016 Onken, Arno; Panzeri, Stefano
Mixed vine copulas as joint models of spike counts and local field potentials
published pages: 1325--1333, ISSN: , DOI: 10.5281/zenodo.584120
Advances in Neural Information Processing Systems 29 2019-07-24
2016 Ioannis Delis, Arno Onken, Philippe G. Schyns, Stefano Panzeri, Marios G. Philiastides
Space-by-time decomposition for single-trial decoding of M/EEG activity
published pages: 504-515, ISSN: 1053-8119, DOI: 10.1016/j.neuroimage.2016.03.043
NeuroImage 133 2019-07-24
2016 Manuel Molano-Mazon; Arno Onken; Liu, Jian K.; Tim Gollisch; Houman Safaai; Stefano Panzeri
Information Jitter Derivative Method: A Novel Approach to the Analysis of Multiplexed Neural Codes
published pages: , ISSN: , DOI: 10.5281/zenodo.814804
2 2019-07-24
2016 Molano-Mazón, Manuel; Onken, Arno; Safaai, Houman; Panzeri, Stefano
Temporal Information Demultiplexing: A Novel Approach to the Analysis of Multiplexed Neural Codes
published pages: , ISSN: , DOI: 10.12751/nncn.bc2016.0136
Bernstein Conference 2016 6 2019-07-24
2017 Vito De Feo, Fabio Boi, Houman Safaai, Arno Onken, Stefano Panzeri, Alessandro Vato
State-Dependent Decoding Algorithms Improve the Performance of a Bidirectional BMI in Anesthetized Rats
published pages: , ISSN: 1662-453X, DOI: 10.3389/fnins.2017.00269
Frontiers in Neuroscience 11 2019-07-24
2017 Manuel Molano-Mazon; Arno Onken; Liu, Jian K.; Tim Gollisch; Houman Safaai; Stefano Panzeri
Information Jitter Derivative Method: A Novel Approach to the Analysis of Multiplexed Neural Codes
published pages: , ISSN: , DOI: 10.5281/zenodo.814832
1 2019-07-24
2017 Jian K. Liu, Helene M. Schreyer, Arno Onken, Fernando Rozenblit, Mohammad H. Khani, Vidhyasankar Krishnamoorthy, Stefano Panzeri, Tim Gollisch
Inference of neuronal functional circuitry with spike-triggered non-negative matrix factorization
published pages: , ISSN: 2041-1723, DOI: 10.1038/s41467-017-00156-9
Nature Communications 8/1 2019-07-24
2015 Molano-Mazon, Manuel; Onken, Arno; Safaai, Houman; Panzeri, Stefano
Analysis of Multiplexed Neural Codes Using the Laplacian Pyramid Decomposition
published pages: , ISSN: , DOI: 10.12751/nncn.bc2015.0077
Bernstein Conference 2015 8 2019-07-24

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