Senior Lecturer, School of Computer Science,
   University of Lincoln, Lincoln, U.K.

Faculty, Athens International Masters Program in
Neuroscience, Dept of Biology, University of Athens, Greece

Associate Editor, Cognitive Computation

Associate Editor, Scholarpedia

Email: vcutsuridis AT lincoln DOT ac DOT uk

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I am a computational scientist broadly interested to reverse engineer how the brain and mind work in health and disease in order to understand the circuits and patterns of neural activity that give rise to mental experience and behavior in order to design and develop more efficient intelligent methods and systems for complex data analysis.

As advances in technologies are producing large, complex data sets at an unprecedented rate, novel theoretical and analytical approaches are needed to realize the potential of these rich datasets. Understanding neural circuitry requires an understanding of the algorithms and mechanisms that govern information processing within a circuit and between interacting circuits in the brain as a whole. Informed by rich observations, formalized theoretical frameworks allow researchers to infer general principles of brain function and the algorithms underlying functioning neural circuitry. Theory coupled with mathematical modeling and simulation approaches are needed to identify gaps in knowledge, to drive the systematic collection of the future data (e.g. so that the collected data specifically address the model parameters), and to formulate testable hypotheses of neural circuit mechanisms and how they govern behavioral and cognitive processes. New data analysis methods are needed to detect features in complex data, often spanning multiple modalities and scales, to reveal underlying mechanisms of brain function.

My approach in computational modeling is that a top-down theorist. I identify the problem in its most abstract form, then derive the algorithm that solves this problem, and finally look at how the brain implements the algorithm. Any successful computational model should first be constrained by large amounts of data, before it makes any further theoretical predictions, because otherwise too many plausible alternatives cannot be ruled out. A theory that hopes to link brain to behavior thus needs to discover the computational level on which brain dynamics control behavioral success.

Research aims to develop:
  • Theories, ideas and conceptual frameworks

  • Multiscale models to integrate information across large temporal and spatial scales in the nervous system

  • Intelligent new methods for complex data analysis

  • Intelligent machines with autonomous and creative behavior

Core scientific network: close collaborators, co-authors/editors, co-supervisors, and co-organizers
Jonathan Erichsen, School of Optometry and Vision Sciences, Cardiff University (eye movements)
Matt Dunn, School of Optometry and Vision Sciences, Cardiff University (eye movements)
Joe Tsien, Georgia Regents University (neural decoding)
Nikolaos Smyrnis, Medical School, University of Athens, Greece (eye movements)
Ioannis Evdokimidis, Medical School, University of Athens, Greece (eye movements)
Michael Hasselmo, Boston University (computational neuroscience)
Ahmed Moustafa, Western University of Sydney (computational neuroscience)
Peter Erdi, Kalamazoo College, Michigan (cognitive science)
Gyorgi Kampis, ELTE, Hungary (evol comput)
Vaibhav Diwadkar, University of Pittsburg (fMRI)
Mike Kokkinidis, University of Crete (structural biology)
Imre Vida, Charité - Universitätsmedizin Berlin (neural circuits)
George Kostpoulos, University of Patras (cellular neurophysiology)