Keynote Plenary

In my talk I will first present results from a map model of primary visual cortex, where we analysed how much evidence recent single unit recordings from cat area 17 provide for a particular cortical "operating point". Using a Bayesian analysis we find, that the experimental data most strongly support a regime where the local cortical network provides dominant excitatory and inhibitory recurrent inputs (compared to the feedforward drive). Most interestingly, the data supports an operating regime which is close to the border to instability. Hence it is conceivable, that modulatory effects like visual attention may briefly shift the operating point into these regimes, leading to an increased sensitivity of cortical responses to visual inputs.

Secondly, I will talk about new ways to quantify spike count correlations among populations of neurons. I will use copulas to construct discrete multivariate distributions that are appropriate to model spike count distributison across several neurons. With copulas it is possible to use arbitrary marginal distributions such as Poisson or negative binomial that are better suited for modeling neuron noise distributions than the most often applied normal approximation. Copulas place a wide range of dependence structures at the disposal and can thus be used to quantify higher order interactions. I will apply this framework to multi-tetrode data recorded from macaque prefrontal cortex, where standard noise models fail to accurately describe the measured spike-count distribution.

Finally, I will discuss results of developmental perturbations imposed on the visual system of adolescent cats through retinal lesions. Using a computational model of visual cortical responses, I will show that the lesion induced changes of neuronal response properties are consistent with spike timing-dependent plasticity (STDP) learning rules. STDP causes visual cortical receptive fields to converge by creating a competition between neurons for the control of spike timing within the network. The spatial scale of this competition appears to depend on the balance of excitation and inhibition and and can in principle be controlled by synaptic scaling type mechanisms. This reveals a novel way by which the capacity of cortical learning rules to transfer response properties between neurons can be effectively switched on and off.

Klaus Obermayer was born in Ludwigsburg, Germany, in 1961. He received the Diplom degree in physics in 1987 from the University of Stuttgart, Germany, and the Dr. rer. nat. degree in 1992 from the Department of Physics, Technical University of Munich, Germany.

From 1992 and 1993 he was a postdoctoral fellow at the Rockefeller University, New York, and the Salk Institute for Biological Studies, La Jolla, USA. From 1994 to 1995 he was member of the Technische Fakultaet, University of Bielefeld, Germany. He became associate professor in 1995 and full professor in 2001 at the Department of Electrical Engineering and Computer Science of the Berlin University of Technology, Germany. He is head of the Neural Information Processing Group and member of the steering committee of the Bernstein Center for Computational Neuroscience Berlin. He is also member of the governing board of the International Neural Network Society and the Board of Directors of the Organisation for Computational Neuroscience. From 1999-2003 he was one of the directors of the European Advanced Course of Computational Neuroscience. His current areas of research are computational neuroscience, artificial neural networks and machine learning with focus on pattern recognition applications, and the analysis of neural data. He co-authored more than 200 scientific publications.