The Emergence of Spontaneous and Evoked Functional Connectivity in a Large-Scale Model of the Brain

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Abstract

We show how functional connectivity (FC) emerges in an anatomically constrained large-scale spiking model of the brain. To gain theoretical insights, we approximate the model, using a mean-field technique that leads to a set of equations for statistical moments, providing a direct analytical link between anatomical structure, neural network dynamics, and FC. We show that FC arises from noise propagation and dynamic slowing down of fluctuations through the anatomically constrained dynamic system. Moreover, the model provides mechanistic explanation for the building up of state-dependent effective FC, under task conditions, through changes of neural dynamics built on the anatomical connectivity.

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Adrián Ponce-Alvarez is a postdoctoral fellow at the Pompeu Fabra University (Barcelona). He studied physics at the University of Paris (France). There, in 2006, he received his master's degree in physics of biological systems. In 2010, he received his PhD degree in neurosciences for his thesis on Probabilistic Models for Studying Variability in Single-Neuron and Neuronal Ensemble Activity, in the University of Mediterranean, Marseille (France).

His research interests include computational neuroscience, physiology, neural networks, stochastic dynamics, probabilistic models, and information theory.

Gustavo Deco is research professor at the Institució Catalana de Recerca i Estudis Avaçats and full professor (cátedratico) at the Pompeu Fabra University (Barcelona) where he is head of the Computational Neuroscience Group and director of the Center of Brain and Cognition. He studied physics at the National University of Rosario (Argentina) where he received his diploma degree in theoretical atomic physics. In 1987, he received his PhD degree in physics for his thesis on Relativistic Atomic Collisions. In 1987, he became a postdoctoral fellow at the University of Bordeaux in France. In the period from 1988 to 1990, he obtained a postdoctoral position of the Alexander von Humboldt Foundation at the University of Giessen in Germany. From 1990 to 2003, he has been with the Neural Computing Section at the Siemens Corporate Research Center in Munich, Germany, where he led the Computational Neuroscience Group. In 1997, he obtained his habilitation (maximal academic degree in Germany) in computer science (Dr. Rer. Nat. Habil.) at the Technical University of Munich for his thesis on Neural Learning. In 2001, he received his PhD in psychology (Dr. phil.) for his thesis on Visual Attention at the Ludwig-Maximilian-University of Munich.

His research interests include computational neuroscience, neuropsychology, psycholinguistics, biological networks, statistical formulation of neural networks, and chaos theory.

He has published 4 books, more than 170 papers in international journals, 260 papers in international conferences, and 30 book chapters. He has also 52 patents in Europe, the United States, Canada, and Japan. Recently, he was awarded with the ‘Advanced ERC’ grant.

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