Curriculum Vitae

Dr. rer. nat. Gordon Pipa

born:

20. September 1974

address:

Ingelheimerstr. 20

 

60529 Frankfurt/M, Germany

 

 

phone:

+ 49 69 967 69 289

fax:

+ 49 69 970 86 654

e-mail

mail@g-pipa.de

 

 

Education and Career:

  • PhD, supervisor Prof. Klaus Obermayer, Department of Neural Information Processing, Berlin
    University of Technology, Berlin, Germany, Grade 1 (very good)
    Thesis title: The Neuronal Code: Development of tools and hypotheses for understanding the role of synchronization of neuronal activity
  • Diploma, Max Planck Institute for Brain Research and the Faculty of Physics, J. W. Goethe University, Frankfurt am Main, Germany, Grade 1 (very good)
  • Diploma-Studies in Physics, RWTH-Aachen University, Aachen, Germany
    specialized on theoretical thermodynamics, stochastic processes, and information and image processing
     

Positions

  • Group leader with Prof. Wolf Singer, Department of Neurophysiology, Max Planck Institute for Brain Research, Frankfurt am Main, Germany
    since
  • Junior fellow (~Junior Professor), Frankfurt Institute for Advanced Studies (FIAS), Frankfurt am Main, Germany
  • Research fellow with Prof. Emery Brown, joint appointment: Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA, and Department of Anesthesia and Critical Care, Massachusetts General Hospital, Boston, MA, USA
  • Research assistant with PD Dr. Sonja Grün, Department of Neurophysiology, Max Planck Institute for Brain Research, Frankfurt am Main, Germany
     

 

Professional Activities

  • Program Chair of Bernstein Conference for Computational Neuroscience, 2009 (BCCN), Frankfurt am Main, Germany
  • Organizer of Trends in Complex Systems - International Workshop on Synchronization and Multiscale Complex Dynamics in the Brain (BSYNC09), 2009, Dresden, Germany
  • Course Director of FIAS Summer School on Theoretical Neuroscience and Complex Systems in 2006, 2007 and 2008, Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany
  • 2006: Guest scientist in Group of Prof. Claudio R. Mirasso at Institute for Cross-Disciplinary Physics and Complex Systems, Palma de Mallorca, Spain
  • 2003: Visiting student in Group of Prof. Larry Abbott at Brandeis University, Waltham, MA, USA
     

Grants

  • PENS Grant by the Federation of European Neuroscience Societies for running the FIAS Summer School on Theoretical Neuroscience and Complex Systems
  • European Grant 04330: GABA ('Global Approach to Brain Activity')
  • Volkswagen Grant for running the FIAS Summer School on Theoretical Neuroscience and Complex Systems 2006
     

Awards and Honors

  • MainCampus Educator Award, scholarship for young faculty by the Stiftung Polytechnische Gesellschaft Frankfurt am Main
  • Scholarship of the German National Academic Foundation, state scholarshipTriple prize winner in the German contest for young scientists (Jugend Forscht)

 

Cooperation’s:

 

Most Important Invited Talks:

  • 2009
    • Trends in Complex Systems - International Workshop on Synchronization and Multiscale Complex Dynamics in the Brain (BSYNC09), Dresden, Germany (upcoming in November)
    • Computational and Systems Neuroscience Meeting (COSYNE), plenary talk and workshop, Salt Lake City, UT, USA
    • Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA, Group of Prof. Ann Graybiel
       
  • 2008
    • German-American Frontiers of Science Symposium (GAFOS), Potsdam, Germany
    • Center for BioDynamics, Boston University, Boston, MA, USA, Group of Prof. Nancy Kopell
    • International Conference on Cognitive and Neural Systems (ICCNS), Boston, MA, USA
    • Computational Neuroscience Meeting (CNS), Portland, OR, USA
    • International Conference on Artificial Neural Networks (ICANN), Prague, Czech Republic
       
  • 2007
    • Center for Neurobiology and Behavior, Columbia University, New York, USA, Group of Prof. Larry Abbott
    • University of Potsdam, Germany, Nonlinear Dynamics Group of Prof. Jürgen Kurths
    • Noise in Life - International Workshop on Stochastic Dynamics in the Neurosciences, Dresden, Germany
    • University of California, San Francisco, CA, USA, Group of Prof. Loren Frank
       
  • 2006
    • The dynamical brain, International Titisee Conference, Titisee, Germany
    • Institute for Cross-Disciplinary Physics and Complex Systems, Palma de Mallorca, Spain
    • Faculty of Electrical Engineering and Computer Science, Berlin University of Technology, Berlin, Germany, Neural Information Processing Group of Prof. Klaus Obermayer
    • Faculty of Biology, Freie Universität Berlin, Germany, Neuroinformatics and Theoretical Neuroscience, Group of PD Dr. Sonja Grün
       
  • 2005
    • Brazilian Conference for Biology (FeSBE), Sao Paulo, Brazil
    • Summer school on Nonlinear Dynamics and Chaos at the Max Planck Institute for Complex Systems, Dresden, Germany
    • Workshop on Data Analysis in Neuroscience, Trinity College, Dublin, Ireland, together with Prof. Emery Brown and Prof. Peter Latham
       
  • 2003
    • Computational Neuroscience Meeting (CNS), Alicante, Spain
    • Woods Hole Course for Neuroinformatics, Woods Hole, MA, USA
    • Society for Neuroscience Meeting (SFN), New Orleans, LA, USA
       
  • 2002
    • Center for Neurodynamics, University of Missouri, St. Louis, MO, USA, Group of Prof. Frank Moss
    • Computational Neuroscience Meeting (CNS), Chicago, IL, USA

 

 

Scientific research program:

My research is focused on understanding how information processing and cognitive phenomena can arise from the collective self-organization of elements interacting across many spatial and temporal scales. In particular I study, first, synchronization of neuronal activity in delay coupled systems, second, information processing in self-organized complex systems in different dynamical states, i.e. self-organized criticality, and third, the use of time series analysis to understand how information flow can take place between neural activity occurring at different spatial and temporal scales. The long term goal of my research is to identify principles that shape neuronal activity and are used to process information in a multi-scale system like the brain.

I firmly believe that understanding the principles of neuronal information processing requires the combination of theoretical, computational and experimental approaches. Therefore my research is multidisciplinary and is composed of two tracks. The first track develops and uses analytical and computational models to identify and understand principles. The second track is data driven and aims to characterize neuronal activity and collective behavior based upon the experimental work of my group and also upon that of international collaborators.

My modeling activities have three main thrusts: first modeling networks and emergent properties, second rhythms and synchronization, and third neuronal computations based on self-organizing systems. In the first thrust I model networks of neurons or neuronal masses assuming that emergent phenomena are inherently important for understanding neuronal dynamics and information processing. In other words, the whole is more than just the sum of simple building blocks. Second, rhythms and synchronization seem to be an omnipresent feature of neuronal activity. Especially the interaction of excitatory and inhibitory sub populations has been demonstrated, by both theory and experiments, to be a crucial element for rhythm generation and synchronization in local populations. Based on this, I focus my synchronization research on establishing concepts and models for synchronization among such local populations that are coupled by large delays. A key element in this research is the use of the network topology to stabilize subsets of synchronization solutions, i.e. zero time lag or near zero time lag. In one of our most recent papers published in PNAS we demonstrate that a certain topology, here a V shape motif that is often found in thalamo-cortical, cortico-cortical, and inside local cortical networks of neurons, can stabilize zero phase synchronization independently of the coupling delay. Third, the neuronal system comprises a large diversity of elements that define various temporal and spatial scales. The self-organization of the system and the resultant dynamics have to cope with or even take advantage of the multi-scale nature and diversity. While in a traditional view such complexity is often seen as noise or an unwanted feature I am interested in principles of neuronal information processing that can take advantage of these properties. Towards this end I identified reservoir computing, originally introduced in the context of echo state or liquid state machines, as a promising concept. Reservoir computing is a universal framework for computation. It uses the dynamics of a complex, maybe random, dynamical system to map features into a high dimensional state, similar to the idea of support vector machines from machine learning. I extended the reservoir computing concept by adding delay coupling and neuronal plasticity to allow for self-organization. Importantly, I do not just aim for describing changes of computational properties, but also for characterizing the underlying principles in terms concepts from physics, such as characterizing the dynamical states regarding criticality, synchronization and consistency in the case of chaotic behavior.

The second research track is data driven and concerns the development and use of new tools for analyzing time series of neuronal data, including spiking activity, local field potentials (LFP), EEG and MEG. The foci of this research track are fourfold. First, detection of synchronous spiking activity, which has over the past two decades turned out to be very difficult. A major complication is how to separate the influences of spike rate and synchronization. My group and I develop tools that allow the identification of synchronous firing and the comparison of the synchronization strength between conditions. Second, to understand neuronal information processing it is important to identify the information flow between different elements, i.e. areas, columns or sub networks of neurons. My research aims to develop and apply tools that allow to identify this information flow, for systems that are both linearly and non-linearly coupled. For MEG, EEG, and LFP data we use directed measures that infer causality based on conditional entropies. To infer the information flow between neurons we use the Point process framework. This is a statistical framework based on the point process likelihood function to relate a neuron's spiking probability to the neuron's own spiking history, concurrent ensemble activity, and extrinsic covariates such as stimuli or behavior. Third, we develop and apply tools to characterize information flow between different spatial and temporal scales, especially between EEG, LFP and spiking activity measured simultaneously. The framework that we are using is also based on the Point process framework. It measures to what degree neuronal mass activity characterized by different components of the LFP or EEG changes the millisecond precise firing of neurons that are part of a network. Fourth, using our tools we can identify undirected and directed coupling to characterize the network for individual states and cognitive processes. To investigate changes of these networks due to dynamic coupling and decoupling we develop and apply tools based on graph theory that allow us to characterize and compare network activity across different experimental conditions.

 

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