Opendata, web and dolomites


Biological neural networks: from structure to function

Total Cost €


EC-Contrib. €






Project "NeuArc2Fun" data sheet

The following table provides information about the project.


Organization address
address: PLACA DE LA MERCE, 10-12
postcode: 8002

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 Spain [ES]
 Project website
 Total cost 158˙121 €
 EC max contribution 158˙121 € (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 2016
 Duration (year-month-day) from 2016-03-01   to  2018-02-28


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSIDAD POMPEU FABRA ES (BARCELONA) coordinator 158˙121.00


 Project objective

The present project lies at the connection between theoretical and experimental neuroscience. It investigates how information is processed in neural networks with feedback, via the firing activity. On the one hand, the past decades have seen a growing interest for the analysis of the functional connectivity, namely how the spiking activity of neural populations is organized spatially and temporally. These activity patterns are hypothesized to form the basis of neural information, i.e., how neurons collectively encode information. On the other hand, experiments have revealed the complex design of the neural circuitry with many levels of organization, from the local connectivity of neurons to broad-scale pathways between cortical areas. NeuArc2Fun aims to develop a recurrent neural network model that bridges these structural and functional levels. The advantage of this model-based approach is the ability to make predictions about the role of each component of the model - in particular, its connectivity - in shaping neural activity. A key issue is to keep a balance between the mathematical tractability and biological realism in the model. To address this trade-off problem, NeuArc2Fun focuses on the mesoscopic level, namely scales at which many interacting neural populations can be simultaneously recorded by current state-of-the-art experimental techniques, such as electrode arrays. In practice, experimental data from the visual cortex will be used to tune and test the network models. In turn, gaining precise knowledge about neural cognitive processing will be applied to design experiments and test new ideas for information coding in networks. To a broader extent, this project will also benefit to applications that involve information decoding and interaction with the brain, e.g., neural prostheses and brain-machine interfaces.


year authors and title journal last update
List of publications.
2017 Matthieu Gilson, Gustavo Deco, Karl J. Friston, Patric Hagmann, Dante Mantini, Viviana Betti, Gian Luca Romani, Maurizio Corbetta
Effective connectivity inferred from fMRI transition dynamics during movie viewing points to a balanced reconfiguration of cortical interactions
published pages: , ISSN: 1053-8119, DOI: 10.1016/j.neuroimage.2017.09.061
NeuroImage 2019-06-13
2018 Matthieu Gilson
Analysis of fMRI data using noise-diffusion network models: a new covariance-coding perspective
published pages: , ISSN: 0340-1200, DOI: 10.1007/s00422-017-0741-y
Biological Cybernetics 2019-06-13
2018 Edmund T. Rolls, Wei Cheng, Matthieu Gilson, Jiang Qiu, Zicheng Hu, Hongtao Ruan, Yu Li, Chu-Chung Huang, Albert C. Yang, Shih-Jen Tsai, Xiaodong Zhang, Kaixiang Zhuang, Ching-Po Lin, Gustavo Deco, Peng Xie, Jianfeng Feng
Effective Connectivity in Depression
published pages: 187-197, ISSN: 2451-9022, DOI: 10.1016/j.bpsc.2017.10.004
Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 3/2 2019-06-13
2017 Katharina Glomb, Adrián Ponce-Alvarez, Matthieu Gilson, Petra Ritter, Gustavo Deco
Resting state networks in empirical and simulated dynamic functional connectivity
published pages: 388-402, ISSN: 1053-8119, DOI: 10.1016/j.neuroimage.2017.07.065
NeuroImage 159 2019-06-13
2018 Mario Senden, Niels Reuter, Martijn P. van den Heuvel, Rainer Goebel, Gustavo Deco, Matthieu Gilson
Task-related effective connectivity reveals that the cortical rich club gates cortex-wide communication
published pages: 1246-1262, ISSN: 1065-9471, DOI: 10.1002/hbm.23913
Human Brain Mapping 39/3 2019-06-13
2017 M. Gilson, A. Tauste Campo, X. Chen, A. Thiele, G. Deco
Nonparametric test for connectivity detection in multivariate autoregressive networks and application to multiunit activity data
published pages: 357-380, ISSN: 2472-1751, DOI: 10.1162/NETN_a_00019
Network Neuroscience 1/4 2019-06-13
2018 Katharina Glomb, Adrián Ponce-Alvarez, Matthieu Gilson, Petra Ritter, Gustavo Deco
Stereotypical modulations in dynamic functional connectivity explained by changes in BOLD variance
published pages: 40-54, ISSN: 1053-8119, DOI: 10.1016/j.neuroimage.2017.12.074
NeuroImage 171 2019-06-13

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The information about "NEUARC2FUN" are provided by the European Opendata Portal: CORDIS opendata.

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