NYC Photo
Mária Ercsey-Ravasz

Researcher

Faculty of Physics, Babes-Bolyai University

Romanian Institute of Science and Technology

Cluj-Napoca, Romania

E-mail: ercsey.ravasz@phys.ubbcluj.ro

After obtaining Ph.D. in Physics and Information Technology, I have spent 3 years as a postdoctoral researcher at the University of Notre Dame, IN, USA. I returned to Romania with a Starting Research Grant, and obtained a Marie Curie Fellowship.

Currently I am a research fellow at the Babes-Bolyai University and also at the Romanian Institute of Science and Technology.

Research interests:

1. Physical structure of the brain, the inter-areal cortical network, functional cortical networks. 2. Networks, centrality measures, clustering, biological networks 3. Analog computing, solving optimization problems with continuos-time dynamical systems, turbulent computing and transiently chaotic dynamical systems, cellular neural networks and their applications.

Most recent publications:

- H.R. Noori, J. Schottler, M. Ercsey-Ravasz, A. Cosa-Linan, M. Varga, Z. Toroczkai, R. Spanagel, "A multiscale cerebral neurochemical connectome of the rat brain", PLoS Biology, 2002612 (2017)

- Zs. I. Lazar, I. Papp, L. Varga, F. Jarai-Szabo, D. Deritei, M. Ercsey- Ravasz, “Stochastic graph Voronoi tessellation reveals community structure”, Physical Rveiew E, 95, 022306 (2017).

- Sz. Horvát†, Răzvan Gămănuț†, Mária Ercsey-Ravasz†, Loïc Magrou, Bianca Gămănuț, David C. Van Essen, Andreas Burkhalter, Kenneth Knoblauch, Zoltán Toroczkai, Henry Kennedy,”Spatial embedding and wiring cost constrain the functional layout of cortical networks in rodents and primates”, PLoS Biology, 14, e1002512 (2016). († indicates equal contribution)

- M. Varga, R. Sumi, Z. Toroczkai, M. Ercsey-Ravasz, "Order-to-chaos transition in the hardness of random Boolean satisfiability”, Physical Review E, 93, 052211 (2016).

- D. Deritei, W.C. Aird, M. Ercsey-Ravasz, E. Ravasz Regan, "Principles of dynamical modularity in biological regulatory networks", Scientific Reports, 6, 21957 (2016).

- Y. Ren, M. Ercsey-Ravasz, P. Wang, M.C. Gonzalez, Z. Toroczkai, “Predicting commuter flows in spatial networks using a radiation model based on temporal ranges”, Nature Communications, 5, 5347 (2014).

- D. Deritei, Zs. Lazar, I. Papp, F. Jarai-Szabo, R. Sumi, L. Varga, ER Regan, M. Ercsey-Ravasz, “Community detection by graph Voronoi diagrams”, New Journal of Physics, 16, 063007 (2014).

- R. Sumi, B. Molnar, M. Ercsey-Ravasz, “Robust optimization with transiently chaotic dynamical systems”, European Phyics Letters, 106, 40002 (2014).

- N.T. Markov, M. Ercsey-Ravasz, D.C. Van Essen, K. Knoblauch, Z. Toroczkai, H. Kennedy, "Cortical High-density Counter-stream Architectures", Science, 342, 1238406, 2013 doi:10.1126/science.1238406

- M. Ercsey-Ravasz, N.T. Markov, C. Lamy, D.C. Van Essen, K. Knoblauch, Z. Toroczkai, H. Kennedy, "A predictive network model of cerebral cortical connectivity based on a distance rule", Neuron, 80, 184, 2013 doi:10.1016/j.neuron.2013.07.036

- B. Molnár, M. Ercsey-Ravasz, "Asymmetric Continuous-Time Neural Networks without Local Traps for Solving Constraint Satisfaction Problems". PLoS ONE 8(9): e73400, 2013. doi:10.1371/journal.pone.0073400

- N.T. Markov, M. Ercsey-Ravasz, C. Lamy, A.R. Ribeiro Gomes, L. Magrou, P. Misery, P. Giroud, P. Barone, C. Dehay, Z. Toroczkai, K. Knoblauch, D.C. Van Essen, H. Kennedy. "The role of long-range connections on the specificity of the macaque interareal cortical network" PNAS 110, 5187 (2013), doi:10.1073/pnas.1218972110

- M. Ercsey-Ravasz, Z. Toroczkai, "The Chaos Within Sudoku", Scientific Reports 2, 755 (2012) doi:10.1038/srep00725

- N. T. Markov, M. Ercsey-Ravasz, A.R. Ribiero Gomes, C. Lamy, J. Vezoli, L. Magrou, P. Misery, A. Falchier, R. Quilodran, J. Sallet, M.A. Gariel, R. Gamanut, C. Huissoud, S. Clavagnier, P. Giroud, D. Sappey-Marinier, P. Barone, C. Dehay, Z. Toroczkai, K. Knoblauch, D.C. Van Essen, H. Kennedy. "A weighted and directed interareal connectivity matrix for macaque cerebral cortex" Cerebral Cortex , advance access , Sep. 25 (2012)

- B. Molnár, Z. Toroczkai, M. Ercsey-Ravasz, "Continuous-time Neural Networks Without Local Traps for Solving Boolean Satisfiability", CNNA 2012, Torino, Italy (2012) doi:10.1109/CNNA.2012.6331411

- M. Ercsey-Ravasz, R. Lichtenwalter, N.W. Chawla, Z. Toroczkai, "Range-limited Centrality Measures in Non-weighted and Weighted Complex Networks, Physical Review E 85, 066103 (2012). arxiv:1111.5382

- M. Ercsey-Ravasz, Z. Toroczkai, Z. Lakner, J. Baranyi, "Complexity of the International Agro-Food Trade Network", PLoS ONE 7(5), e37810 (2012). doi:10.1371/journal.pone.0037810

- M. Ercsey-Ravasz, Z. Toroczkai, "Optimization Hardness as Transient Chaos in an Analog Approach to Constraint Satisfaction", Nature Physics 7, 966 (2011) arxiv:1208.0526

- N.T. Markov, P. Misery, A. Falchier, C. Lamy, J. Vezoli, R. Quilodran, P. Giroud, M.A. Gariel, M. Ercsey-Ravasz, L.J. Pilaz, C. Huissoud, P. Barone, C. Dehay, Z. Toroczkai, D.C. Van Essen, H. Kennedy, K. Knoblauch. "Weight consistency specifies regularities of cortical networks." Cerebral Cortex 21 , 1254-1272 (2011). free journal access .

- M. Ercsey-Ravasz, Z. Toroczkai, "Centrality Scaling in Large Networks", Physical Review Letters 105, 038701 (2010) 10.1103/PhysRevLett.105.038701

The heavily connected brain - Cortical high-density counterstream architectures

Background.The cerebral cortex is divisible into many individual areas, each exhibiting distinct connectivity profiles, architecture, and physiological characteristics. Interactions among cortical areas underlie higher sensory, motor, and cognitive functions. Graph theory provides an important framework for understanding network properties of the interareal weighted and directed connectivity matrix reported in recent studies.Density and topology of the cortical graph. Figure (Left) The 66% density of the cortical matrix (black triangle) is considerably greater than in previous reports (colored points) and is inconsistent with a small-world network. (Right) A bow-tie representation of the high-density cortical matrix. The high-efficiency cortical core has defined relations with the cortical periphery in the two fans. Advances. We derive an exponential distance rule that predicts many binary and weighted features of the cortical network, including efficiency of information transfer, the high specificity of long-distance compared to short-distance connections, wire length minimization, and the existence of a highly interconnected cortical core. We propose a bow-tie representation of the cortex, which combines these features with hierarchical processing. Outlook. The exponential distance rule has important implications for understanding scaling properties of the cortex and developing future large-scale dynamic models of the cortex.

- N.T. Markov, M. Ercsey-Ravasz, D.C. Van Essen, K. Knoblauch, Z. Toroczkai, H. Kennedy, "Cortical High-density Counter-stream Architectures", Science, 342, 1238406, 2013 doi:10.1126/science.1238406