Arno Onken received the Ph.D. degree in Computational Neuroscience from the Technische Universität Berlin, Germany in 2011. From 2011 to 2013 he worked as a Postdoctoral Fellow at the Laboratory of Cognitive Computational Neuroscience at the University of Geneva, Switzerland. In 2013 he joined the Neural Computation Laboratory of Stefano Panzeri as a Postdoctoral Fellow at the University of Glasgow, UK and later at the Istituto Italiano di Tecnologia, Italy. Since 2015 he works as a Marie-Curie Fellow at the Istituto Italiano di Tecnologia, Italy.
His research interests include the development and application of novel methods based on machine learning and information theory for analyzing neural systems.
Brain functions likely emerge from the concerted, context-dependent operations of its microscopic and macroscopic networks. Therefore, the organization and operational principles of such complex systems may be best investigated by using multi-modal approaches, including concurrent measurements of neural activity on multiple spatiotemporal scales. Performing and interpreting such multi-scale measures, though, presents enormous challenges for both experimental and mathematical neuroscientists. Existing analysis methods make limited use of newly acquired concurrent multi-scale information.
To advance analysis methods for these data, Arno Onken develops novel multi-scale models that bridge the gap between single cell statistics and neural mass signals. The models describe mixed discrete statistics, covering single cell and small population spike trains, as well as continuous statistics, such as those describing mesoscopic and macroscopic measures of mass neural activity in neuroimaging experiments. Application of these methods to multi-modal datasets will establish a deeper general understanding of the mechanisms of large-scale communication among brain areas.
A. Onken and S. Panzeri (2016). Mixed vine copulas as joint models of spike counts and local field potentials. In D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon and R. Garnett, editors, Advances in Neural Information Processing Systems 29 (NIPS 2016), pages 1325–1333.
A. Onken, J. K. Liu, P. P. C. R. Karunasekara, I. Delis, T. Gollisch, and S. Panzeri (2016). Using matrix and tensor factorizations for the single-trial analysis of population spike trains. PLoS Computational Biology 12(11): e1005189.
I. Delis, A. Onken, P. G. Schyns, S. Panzeri, and M. G. Philiastides (2016). Space-by-time decomposition for single-trial decoding of M/EEG activity. NeuroImage 133: 504–515.
A. Onken, P. P. C. R. Karunasekara, C. Kayser, and S. Panzeri (2014). Understanding neural population coding: information-theoretic insights from the auditory system. Advances in Neuroscience, vol. 2014, Article ID 907851.
A. Onken, V. Dragoi, and K. Obermayer (2012). A maximum entropy test for evaluating higher-order correlations in spike counts. PLoS Computational Biology 8(6): e1002539.
A. Onken, S. Grünewälder, M. H. J. Munk, and K. Obermayer (2009). Analyzing short-term noise dependencies of spike-counts in macaque prefrontal cortex using copulas and the flashlight transformation. PLoS Computational Biology 5(11): e1000577.
A. Onken and K. Obermayer (2009). A Frank mixture copula for modeling higher-order correlations of neural spike counts. Journal of Physics: Conference Series 197: 012019.
A. Onken, S. Grünewälder, and K. Obermayer (2009). Correlation coefficients are insufficient for analyzing spike count dependencies. In Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams, and A. Culotta, editors, Advances in Neural Information Processing Systems 22 (NIPS 2009), pages 1383–1391.
A. Onken, S. Grünewälder, M. H. J. Munk, and K. Obermayer (2008). Modeling short-term noise dependence of spike counts in macaque prefrontal cortex. In D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou, editors, Advances in Neural Information Processing Systems 21 (NIPS 2008), pages 1233–1240.
A. Onken and S. Panzeri (2016). Concurrent analysis of neural activity at multiple scales using mixed vine copulas. Bernstein Conference 2016. Berlin, Germany.
M. Molano-Mazon, A. Onken, H. Safaai, and S. Panzeri (2016). A novel approach to the analysis of multiplexed neural codes. Bernstein Conference 2016. Berlin, Germany.
M. Molano-Mazon, A. Onken, H. Safaai, and S. Panzeri (2015). Analysis of multiplexed neural codes using the Laplacian pyramid decomposition. Bernstein Conference 2015. Heidelberg, Germany.
A. Onken, J. K. Liu, I. Delis, S. Panzeri, and T. Gollisch (2014). Detection of substructure in receptive fields of retinal ganglion cells. Bernstein Conference 2014. Göttingen, Germany.
A. Onken, G. C. DeAngelis, D. E. Angelaki, A. Pouget (2013). Near-optimal multisensory integration revisited. Society for Neuroscience Annual Meeting 2013. San Diego, CA, USA.
A. Onken, J. Drugowitsch, I. Kanitscheider, G. C. DeAngelis, D. E. Angelaki, J. M. Beck, A. Pouget (2012). Near optimal multisensory integration with nonlinear probabilistic population codes using divisive normalization. Society for Neuroscience Annual Meeting 2012. New Orleans, LA, USA.
A. Onken, S. Grünewälder, V. Dragoi, and K. Obermayer (2010). A maximum entropy mutual information test for spike counts of V1. Society for Neuroscience Annual Meeting 2010. San Diego, CA, USA.
A. Onken, S. Grünewälder, and K. Obermayer (2009). Extensions to copula based modeling of spike counts. Computational and Systems Neuroscience 2009, Salt Lake City, UT, USA.
A. Onken and K. Obermayer (2008). Modeling spike-count dependence structures with multivariate Poisson distributions. Seventeenth Annual Computational Neuroscience Meeting, Portland, OR, USA.
A. Onken and K. Obermayer (2008). Stimulus-independence of noise correlations is beneficial for short-term population coding in MT. Computational and Systems Neuroscience 2008, Salt Lake City, UT, USA.
A. Onken and K. Obermayer (2007). A multivariate Poisson model with higher order noise correlations for population decoding in MT. BCCN Symposium 2007, Göttingen, Germany.
07/2015 – 06/2017 Marie Skłodowska-Curie Fellowship
07/2011 Sloan Swartz Fellowship for the Sloan Swartz Annual Meeting 2011
05/2007 – 05/2010 Scholarship from the Bernstein Center for Computational Neuroscience