Gustavo Deco is an Argentinian and Italian professor and scientist. He serves as Research Professor at the Catalan Institution for Research and Advanced Studies and Full Professor (Catedratico) at the Pompeu Fabra University, where he is Director of the Center of Brain and Cognition and head of the Computational Neuroscience Group. In 2001 Deco was awarded the international prize of Siemens "Inventor of the Year" for his contributions in statistical learning, models of visual perception, and fMRI based diagnosis of neuropsychiatric diseases.[citation needed].
Deco has held lecturing posts at Rosario, Frankfurt and Munich and, since 2001, invited lecturer post at the Ludwig-Maximilian-University of Munich. He was associate professor at the Technical University of Munich and honorary professor at the University of Rosario in 1998. From 2001-2009, he was a McDonnell-Pew Visiting Fellow at the Centre for Cognitive Neuroscience at the University of Oxford.
Academic contributions
Deco has made important contributions on a number of topics including computational neuroscience, neuropsychology, psycholinguistics, biological networks, statistical formulation of neural networks, and chaos theory.[1] His most highly cited research focuses on whole-computational modelling of ongoing spontaneous activity in resting-state networks, and thus providing a causal understanding of these important networks in health and disease.[2] Deco is currently investigating these research questions in his advanced ERC grant “The Dynamical and Structural Basis of Human Mind Complexity: Segregation and Integration of Information and Processing in the Brain”.
The large-scale dynamical brain model is able to best fit the empirical resting functional magnetic resonance imaging (fMRI) data when the brain network is critical (i.e., at the border of a dynamical bifurcation point), so that, at that operating point, the system defines a meaningful dynamic repertoire that is inherent to the neuroanatomical connectivity. To determine the dynamical operating point of the system, Deco et al. contrasted the results of the simulated model with the experimental resting functional connectivity (FC) as a function of the control parameter G describing the scaling or global strength of the intercortical coupling. The fit between both the empirical and the simulated FC matrix was measured by the Pearson correlation coefficient. In the same plot, the second bifurcation line obtained below is also shown. The best fit of the empirical data is observed at the brink of the second bifurcation model. (B) Bifurcation diagrams characterizing the stationary states of the brain system as a function of the control parameter G. Deco and colleagues plotted the maximal firing rate activity over all cortical areas for the different possible stable states. They studied 1000 different random initial conditions to identify all possible new stationary states, and also the case where the initial condition was just the spontaneous state, to identify when the spontaneous state loses stability. For small values of the global coupling G, only one stable state exists, namely the spontaneous state characterized by low firing activity in all cortical areas. For a critical value of G, a first bifurcation emerges where at least one new multistable state appears while the spontaneous state is still stable. For even larger values of G, a second bifurcation appears where the spontaneous state becomes unstable. Further information can be found in Deco, Jirsa and McIntosh (2013)
In his research, Deco has used large-scale cortical models where the networks teeter on the edge of instability.[4] In this state, the functional networks are in a low-firing stable state while they are continuously pulled towards multiple other configurations. Small extrinsic perturbations can shape task-related network dynamics, whereas perturbations from intrinsic noise generate excursions reflecting the range of available functional networks. This is particularly advantageous for the efficiency and speed of network mobilization. Thus, the resting state reflects the dynamical capabilities of the brain, which emphasizes the vital interplay of time and space. Ongoing research concentrates on characterizing these functional and structural networks in health and disease with a view to creating a new discipline of computational neuropsychiatry.[5]
Bibliography
Books
G. Deco and D. Obradovic (1996) "An Information-Theoretic Approach to Neural Computing", Springer Verlag, New York.
G. Deco and B. Schürmann (2000) "Information Dynamics: Foundations and Applications", Springer Verlag, New York.
E. Rolls and G. Deco (2001) "Computational Neuroscience of Vision", Oxford University Press, Oxford.
E. Rolls and G. Deco (2010) "The Noisy Brain", Oxford University Press, Oxford.
Articles
↑ Deco G, Tononi G, Boly M, Kringelbach ML, 2015, "Rethinking Segregation and Integration: Contributions of Whole-Brain Modelling", Nature Reviews Neuroscience, 16:430–439.
↑ Deco G, Kringelbach ML, 2016, "Metastability and Coherence: Extending the Communication through Coherence Hypothesis Using A Whole-Brain Computational Perspective", Trends in Neurosciences, 39:125–135.
↑ Deco G, Jirsa VK, McIntosh AR, 2013, "Resting Brains Never Rest: Computational Insights into Potential Cognitive Architectures", Trends in Neurosciences 36:268-274.
↑ Kringelbach ML, McIntosh AR, Ritter P, Jirsa VK, Deco G, 2015, "The Rediscovery of Slowness: Exploring the Timing of Cognition", Trends in Cognitive Sciences, 19:616–628.
↑ Deco G, Kringelbach ML, 2014, "Great Expectations: Using Whole-Brain Computational Connectomics for Understanding Neuropsychiatric Disorders", Neuron, 84(5):892-905.
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