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Sebastian Gerwinn

Address: Spemannstr. 41
72076 Tübingen
Room number: 1.B.05
Phone: +49 7071 601 1772
E-Mail: sebastian.gerwinn
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Sebastian Gerwinn

Position: PhD Student  Unit: Alumni Schölkopf Alumni Bethge

My research interests are in the area of Bayesian inference and computational neuroscience. In particular, I am interested in a characterization of the relationship between sensory signals and neural responses. The methods which I think are the most promising to tackle this kind of tasks are Bayesian methods.

The applicability of Bayesian methods is often limited by the fact that they are computational prohibitive. My main focus has therefore been to alleviate this problem by developing approximate methods which are then also feasible on a much larger scale and can therefore be applied to realistically sized data.

The main advantage of a Bayesian treatment lies in the explicit representation of the involved uncertainties. Having access to this kind of knowledge enables one to perform further analysis such as experimental design or model selection.

 

 

Stimulus Response Relationship

I have analyzed the relationship between stimuli and neural responses from three different perspectives: (1) the encoding, (2) the decoding and (3) the joint occurrence perspective.


In a first project I investigated the system identification task corresponding to the encoding direction of the stimulus response relationship. I developed an approximate Bayesian inference method which is feasible for models of generalized linear type, one of the most successful and commonly used generative models. As a result, we obtained not only particular point estimates of sets of parameters, but also model based confidence intervals, which in turn we used for feature selection and estimating the functional connectivity within populations of neurons.


Second, I analyzed the relationship from a decoding point of view. Here, using the leaky integrate and fire neuron model, I obtained a simple yet accurate decoding algorithm. Again, using a Bayesian treatment, it is possible to not only decode the most likely stimulus but also assigning to each stimulus the probability that it has caused the observed neural response.


Third, merging both perspectives, I looked at the joint occurrence of stimuli and neural responses. Using commonly used descriptive statistics such as spike-triggered average and spike-triggered covariance, I build a maximum entropy model. This model can then be used as a generative model as well as a decoding model exhibiting the same descriptive statistics as the observed ones, while assuming the least  additional constrains due to the maximum entropy property.

Unüberwachtes Lernen Steuerbarer Filter
Unüberwachtes Lernen Steuerbarer Filter
Obwohl sich die Pixel-Darstellung eines Bildes unter affinen Transformationen wie Translation,     Rotation und Skalierung stark ändert, bleibt der Inhalt des Bildes weitgehend unverändert. Insbesondere, wenn sich die Änderungen eines D-dimensionalen Lichtintensitätsvektors durch eine einparametrige Lie-Gruppe beschreiben lassen, ist es möglich, eine verlustfreie Bildrepräsentation zu finden, bei der eine Komponente dem Transformationsparameter entspricht und die anderen (D-1) Komponenten invariant sind unter der Lie-Gruppentransformation. Um solche Bildrepräsentationen abzuleiten, konstruieren wir geeignete generative Modelle, mit denen Steuerbare Filter auf unüberwachte Weise gelernt werden können. Insbesondere haben wir zeigen können, dass es möglich ist mit einer anti-symmetrischen Variante der Kanonischen Korrelationsanalyse (CCA), eine vollständige Basis für 32x32 Bildausschnitte zu bestimmen, die sich aus rotationsinvarianten Steuerbaren Filtern zusammensetzt.

Bayesian Models for Multi-Electrode Neuronal Spike Recordings

We investigate Bayesian methods to predict responses from multiple retinal and LGN ganglion cells, conditioned on visual stimuli.

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Articles (8):

Häfner RM Person, Gerwinn S Person, Macke JH Person and Bethge M Person (2013) Inferring decoding strategies from choice probabilities in the presence of correlated variability Nature Neuroscience 16(2) 235-242.
Theis L Person, Gerwinn S Person, Sinz F Person and Bethge M Person (2011) In All Likelihood, Deep Belief Is Not Enough Journal of Machine Learning Research 12 3071-3096.
Macke JH Person, Gerwinn S Person, White LW , Kaschube M and Bethge M Person (2011) Gaussian process methods for estimating cortical maps NeuroImage 56(2) 570-581.
Berens P Person, Ecker AS Person, Gerwinn S Person, Tolias AS Person and Bethge M Person (2011) Reassessing optimal neural population codes with neurometric functions Proceedings of the National Academy of Sciences of the United States of America 108(11) 4423-4428.
Gerwinn S Person, Macke JH Person and Bethge M Person (2011) Reconstructing stimuli from the spike-times of leaky integrate and fire neurons Frontiers in Neuroscience 5(1) 1-16.
Gerwinn S Person, Macke J Person and Bethge M Person (2010) Bayesian inference for generalized linear models for spiking neurons Frontiers in Computational Neuroscience 4(12) 1-42.
Gerwinn S Person, Macke JH Person and Bethge M Person (2009) Bayesian population decoding of spiking neurons Frontiers in Computational Neuroscience 3(21) 1-28.
Sinz FH Person, Gerwinn S Person and Bethge M Person (2009) Characterization of the p-Generalized Normal Distribution Journal of Multivariate Analysis 100(5) 817-820.

Conference papers (6):

Gerwinn S Person, Berens P Person and Bethge M Person (2009) A joint maximum-entropy model for binary neural population patterns and continuous signals In: Advances in Neural Information Processing Systems 22, (Ed) Bengio, Y. , D. Schuurmans, J. Lafferty, C. Williams, A. Culotta, 23rd Annual Conference on Neural Information Processing Systems (NIPS 2009), Curran, Red Hook, NY, USA, 620-628.
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Macke JH Person, Gerwinn S Person, Kaschube M , White LE and Bethge M Person (2009) Bayesian estimation of orientation preference maps In: Advances in Neural Information Processing Systems 22, (Ed) Bengio, Y. , D. Schuurmans, J. Lafferty, C. Williams, A. Culotta, 23rd Annual Conference on Neural Information Processing Systems (NIPS 2009), Curran, Red Hook, NY, USA, 1195-1203.
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Berens P Person, Gerwinn S Person, Ecker AS Person and Bethge M Person (2009) Neurometric function analysis of population codes In: Advances in Neural Information Processing Systems 22, (Ed) Bengio, Y. , D. Schuurmans, J. Lafferty, C. Williams, A. Culotta, 23rd Annual Conference on Neural Information Processing Systems (NIPS 2009), Curran, Red Hook, NY, USA, 90-98.
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Gerwinn S Person, Macke J Person, Seeger M Person and Bethge M Person (2008) Bayesian Inference for Spiking Neuron Models with a Sparsity Prior In: Advances in neural information processing systems 20, (Ed) Platt, J. C., D. Koller, Y. Singer, S. Roweis, Twenty-First Annual Conference on Neural Information Processing Systems (NIPS 2007), Curran, Red Hook, NY, USA, 529-536.
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Seeger M Person, Gerwinn S Person and Bethge M Person (2007) Bayesian Inference for Sparse Generalized Linear Models In: ECML 2007, (Ed) Kok, J. N., J. Koronacki, R. Lopez de Mantaras, S. Matwin, D. Mladenic, A. Skowron, 18th European Conference on Machine Learning, Springer, Berlin, Germany, 298-309.
Bethge M Person, Gerwinn S Person and Macke JH Person (2007) Unsupervised learning of a steerable basis for invariant image representations In: Human Vision and Electronic Imaging XII, (Ed) Rogowitz, B. E., SPIE Human Vision and Electronic Imaging Conference 2007, SPIE, Bellingham, WA, USA, 1-12.
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Posters (5):

Theis L Person, Gerwinn S Person, Sinz F Person and Bethge M Person (2010): Likelihood Estimation in Deep Belief Networks, Bernstein Conference on Computational Neuroscience (BCCN 2010), Berlin, Germany, Frontiers in Computational Neuroscience, Conference Abstract: Bernstein Conference on Computational Neuroscience.
Macke J Person, Gerwinn S Person, White L , Kaschube M and Bethge M Person (2009): Bayesian estimation of orientation preference maps, Frontiers in Systems Neuroscience, 2009 Conference Abstracts(Computational and Systems Neuroscience) 1.
Gerwinn S Person, Macke J Person and Bethge M Person (2009): Bayesian Population Decoding of Spiking Neurons, Frontiers in Systems Neuroscience, 2009 Conference Abstracts(Computational and Systems Neuroscience) 1.
Gerwinn S Person, Seeger M Person, Zeck G Person and Bethge M Person (2007): Bayesian Neural System identification: error bars, receptive fields and neural couplings, 31st Göttingen Neurobiology Conference, 31 360.
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Bethge M Person, Macke JH Person, Gerwinn S Person and Zeck G Person (2007): Identifying temporal population codes in the retina using canonical correlation analysis, 31st Göttingen Neurobiology Conference, 31 359.
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Theses (1):

Gerwinn S Person: Bayesian Methods for Neural Data Analysis, Eberhard Karls Universität Tübingen, Germany, (2010). PhD thesis

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