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The Bernstein Center for
Computational Neuroscience (BCCN)


The brain enables us to actively interact with our environment. Speed, fault-tolerance, adaptivity and creativity characterize normal brain function, guaranteeing that we successfully master our daily lives. Dynamics are an outstanding feature of the brain at each level of observation. The BCCN aims to improve our understanding of these dynamics regarding the underlying mechanisms, inter-relations and functional role, and explore the application of new insights and techniques to outstanding questions in biomedicine and neurotechnology.

Research at the BCCN Freiburg focuses on:

  • Analysis and modelling of the dynamic processes associated with brain activity at and across multiple levels, from microscopic to macroscopic scales
  • Understanding the constraints from the anatomical substrate, and the structural and functional changes associated with development, learning and adaptation
  • Biological function in a dynamic setting: real-time decoding and controlling of brain dynamics: adaptive controllers of neural prostheses and humanoid robots





Research Projects and PIs:

Theoretical and methodological foundations of Computational Neuroscience

Computational Neuroscience represents a specific approach to brain research and, like any other branch of neuroscience, is essentially defined by its characteristic spectrum of methods. Specifically, Computational Neuroscience plays a central role during the processes of analyzing and interpreting experimental data, by providing the appropriate tools; developing a model, or a theory, and expressing it in mathematical language. Common goals of all projects in this section are (1) the availability of a thoroughly tested numerical simulation environment to explore hitherto inaccessible phenomena in dynamic networks; (2) a concise model-based view of cooperative dynamics and its adaptivity and plasticity in recurrent networks; (3) data analysis tools to assess and characterize cooperative dynamics in large populations of spiking neurons; (4) to gain insight into the structure-function relation of recurrent dynamic networks.

A1: Stochastic models for neuronal dynamics in recurrent cortical networks
S. Rotter, J. Timmer

A2: Dynamics of population activity and spike synchronization in realistic cortical network models
M. Diesmann, A. Aertsen

A3: Functional plasticity in recurrent neuronal networks
M. Diesmann, M.-O. Gewaltig, S. Rotter

A4: Structure formation and structural plasticity of cortical networks
M. Diesmann, M.-O. Gewaltig, S. Rotter, G. Schneider

A5: Structure-function analysis using statistical relational learning and link mining
L. deRaedt, U. Egert, M. Frotscher, S. Rotter

Experimental foundations

At the BCCN, properties and functional implications of neuronal network dynamics at multiple levels are studied in experiments and measurements on living brain tissue, in close interaction with theoretical work. Common goals of all projects in this section are (1) a common data analysis strategy for dynamic networks observed at different levels using new analysis tools; (2) a concise view of cooperative network dynamics based on appropriate physiological experiments; (3) biological insight into the relation between structural and dynamical parameters of recurrent networks; (4) a better understanding of the differences between in vitro and in vivo networks.

B1: Activity dynamics in defined neuronal networks on micro-patterned substrates
U. Egert, A. Aertsen, J. Rühe

B2: Neuronal interaction of biological and computational neuronal networks in a hybrid circuit
U. Egert, K.-H. Boven, M. Nawrot

B3: Cellular and synaptic mechanisms of hippocampal interneuron network dynamics
P. Jonas, M. Bartos, I. Vida

B4: Spatio-temporal dynamics of neocortical networks in vivo
C. Boucsein, M. P. Nawrot, A. Aertsen

B5: FIND - An open-source analysis toolbox for ensemble spike recordings
K.-H. Boven, A. Aertsen, U. Egert

Applications to biomedicine and new technologies

Upon the availability of promising quantitative methods and models, meaningful biomedical and/or technical applications emerge in a natural way, and they play a central role for the research conducted at the BCCN. In particular, the projects selected for this section explore global dynamical properties of brains in a functional context, and explore the possibilities to interface with the brain in real-time. Common goals of all projects in this section are (1) insight into possible strategies to dynamically and adaptively control network dynamics in real-time; (2) confrontation of models and techniques in neuronal dynamics with real-world” problems in biomedicine and technical systems“; (3) applications of insights from neuronal dynamics to a biomedical or technical setting, such as epilepsy prediction and control, neuronal prostheses and humanoid robots.

C1: Experimentally induced granule cell dispersion as a model of pathological neuronal network dynamics
C. Haas, U. Egert, M. Frotscher

C2: Prediction of epileptic seizures by modelling and analyzing abnormal synchronization in cortical networks
J. Timmer, A. Schulze-Bonhage

C3: Towards controlling pathological network dynamics - Terminating ictal epileptic activity by electrical stimulation of the epileptic focus
A. Schulze-Bonhage, K.-H. Boven, A. Aertsen

C4: A neuronal model of adaptive movement control
C. Mehring, S. Rotter, M.-O. Gewaltig

C5: A brain-machine interface for reaching and grasping based on intracranially implanted electrodes in humans
T. Ball, C. Mehring, M. P. Nawrot


Project closed


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