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Arno Onken

Post Doc
Postdoc

Former colleague

Research Line

Neural Computation

Contacts

Center for Neuroscience and Cognitive Systems Palazzo Fedrigotti Corso Bettini 31

About

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.

Projects

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.

Selected Publications

PEER-REVIEWED PUBLICATIONS

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.

 

ABSTRACTS

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, M. H. Munk, and K. Obermayer (2010). A non-stationary copula-based spike count model. Computational and Systems Neuroscience 2010, Salt Lake City, UT, 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.

Awards

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

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I numeri di IIT

L’Istituto Italiano di Tecnologia (IIT) è una fondazione di diritto privato - cfr. determinazione Corte dei Conti 23/2015 “IIT è una fondazione da inquadrare fra gli organismi di diritto pubblico con la scelta di un modello di organizzazione di diritto privato per rispondere all’esigenza di assicurare procedure più snelle nella selezione non solo nell’ambito nazionale dei collaboratori, scienziati e ricercatori ”.

IIT è sotto la vigilanza del Ministero dell'Istruzione, dell'Università e della Ricerca e del Ministero dell'Economia e delle Finanze ed è stato istituito con la Legge 326/2003. La Fondazione ha l'obiettivo di promuovere l'eccellenza nella ricerca di base e in quella applicata e di favorire lo sviluppo del sistema economico nazionale. La costruzione dei laboratori iniziata nel 2006 si è conclusa nel 2009.

Lo staff complessivo di IIT conta circa 1440 persone. L’area scientifica è rappresentata da circa l’85% del personale. Il 45% dei ricercatori proviene dall’estero: di questi, il 29% è costituito da stranieri provenienti da oltre 50 Paesi e il 16% da italiani rientrati. Oggi il personale scientifico è composto da circa 60 principal investigators, circa 110 ricercatori e tecnologi di staff, circa 350 post doc, circa 500 studenti di dottorato e borsisti, circa 130 tecnici. Oltre 330 posti su 1400 creati su fondi esterni. Età media 34 anni. 41% donne / 59 % uomini.

Nel 2015 IIT ha ricevuto finanziamenti pubblici per circa 96 milioni di euro (80% del budget), conseguendo fondi esterni per 22 milioni di euro (20% budget) provenienti da 18 progetti europei17 finanziamenti da istituzioni nazionali e internazionali, circa 60 progetti industriali

La produzione di IIT ad oggi vanta circa 6990 pubblicazioni, oltre 130 finanziamenti Europei e 11 ERC, più di 350 domande di brevetto attive, oltre 12 start up costituite e altrettante in fase di lancio. Dal 2009 l’attività scientifica è stata ulteriormente rafforzata con la creazione di dieci centri di ricerca nel territorio nazionale (a Torino, Milano, Trento, Parma, Roma, Pisa, Napoli, Lecce, Ferrara) e internazionale (MIT ed Harvard negli USA) che, unitamente al Laboratorio Centrale di Genova, sviluppano i programmi di ricerca del piano scientifico 2015-2017.

IIT: the numbers

Istituto Italiano di Tecnologia (IIT) is a public research institute that adopts the organizational model of a private law foundation. IIT is overseen by Ministero dell'Istruzione, dell'Università e della Ricerca and Ministero dell'Economia e delle Finanze (the Italian Ministries of Education, Economy and Finance).  The Institute was set up according to Italian law 326/2003 with the objective of promoting excellence in basic and applied research andfostering Italy’s economic development. Construction of the Laboratories started in 2006 and finished in 2009.

IIT has an overall staff of about 1,440 people. The scientific staff covers about 85% of the total. Out of 45% of researchers coming from abroad 29% are foreigners coming from more than 50 countries and 16% are returned Italians. The scientific staff currently consists of approximately 60 Principal Investigators110 researchers and technologists350 post-docs and 500 PhD students and grant holders and 130 technicians. External funding has allowed the creation of more than 330 positions . The average age is 34 and the gender balance proportion  is 41% female against 59% male.

In 2015 IIT received 96 million euros in public funding (accounting for 80% of its budget) and obtained 22 million euros in external funding (accounting for 20% of its budget). External funding comes from 18 European Projects, other 17 national and international competitive projects and approximately 60 industrial projects.

So far IIT accounts for: about 6990 publications, more than 130 European grants and 11 ERC grants, more than 350 patents or patent applications12 up start-ups and as many  which are about to be launched. The Institute’s scientific activity has been further strengthened since 2009 with the establishment of 11 research nodes throughout Italy (Torino, Milano, Trento, Parma, Roma, Pisa, Napoli, Lecce, Ferrara) and abroad (MIT and Harvard University, USA), which, along with the Genoa-based Central Lab, implement the research programs included in the 2015-2017 Strategic Plan.