Search Results for author: Klaus Obermayer

Found 15 papers, 6 papers with code

Applications of optimal nonlinear control to a whole-brain network of FitzHugh-Nagumo oscillators

1 code implementation17 Feb 2021 Teresa Chouzouris, Nicolas Roth, Caglar Cakan, Klaus Obermayer

Optimal control inputs to nodes are determined by minimizing a cost functional that penalizes the deviations from a desired network dynamic, the control energy, and spatially non-sparse control inputs.

Spatiotemporal patterns of adaptation-induced slow oscillations in a whole-brain model of slow-wave sleep

2 code implementations30 Nov 2020 Caglar Cakan, Cristiana Dimulescu, Liliia Khakimova, Daniela Obst, Agnes Flöel, Klaus Obermayer

We address the mechanism of how SOs emerge and recruit large parts of the brain using a whole-brain model constructed from empirical connectivity data in which SOs are induced independently in each brain area by a local adaptation mechanism.


Training Generative Networks with general Optimal Transport distances

1 code implementation1 Oct 2019 Vaios Laschos, Jan Tinapp, Klaus Obermayer

We propose a new algorithm that uses an auxiliary neural network to express the potential of the optimal transport map between two data distributions.

Biophysically grounded mean-field models of neural populations under electrical stimulation

2 code implementations3 Jun 2019 Caglar Cakan, Klaus Obermayer

We evaluate a reduced mean-field model of excitatory and inhibitory adaptive exponential integrate-and-fire (AdEx) neurons which can be used to efficiently study the effects of electrical stimulation on large neural populations.

Joining Sound Event Detection and Localization Through Spatial Segregation

1 code implementation29 Mar 2019 Ivo Trowitzsch, Christopher Schymura, Dorothea Kolossa, Klaus Obermayer

This work presents an approach that robustly binds localization with the detection of sound events in a binaural robotic system.

Sound Audio and Speech Processing

Non-Deterministic Policy Improvement Stabilizes Approximated Reinforcement Learning

no code implementations22 Dec 2016 Wendelin Böhmer, Rong Guo, Klaus Obermayer

This paper investigates a type of instability that is linked to the greedy policy improvement in approximated reinforcement learning.


Regression with Linear Factored Functions

no code implementations19 Dec 2014 Wendelin Böhmer, Klaus Obermayer

Many applications that use empirically estimated functions face a curse of dimensionality, because the integrals over most function classes must be approximated by sampling.

Gaussian Processes reinforcement-learning

Risk-sensitive Reinforcement Learning

no code implementations8 Nov 2013 Yun Shen, Michael J. Tobia, Tobias Sommer, Klaus Obermayer

We derive a family of risk-sensitive reinforcement learning methods for agents, who face sequential decision-making tasks in uncertain environments.

Decision Making Q-Learning +1

Risk-sensitive Markov control processes

no code implementations28 Oct 2011 Yun Shen, Wilhelm Stannat, Klaus Obermayer

We introduce a general framework for measuring risk in the context of Markov control processes with risk maps on general Borel spaces that generalize known concepts of risk measures in mathematical finance, operations research and behavioral economics.

Correlation Coefficients are Insufficient for Analyzing Spike Count Dependencies

no code implementations NeurIPS 2009 Arno Onken, Steffen Grünewälder, Klaus Obermayer

The linear correlation coefficient is typically used to characterize and analyze dependencies of neural spike counts.

Modeling Short-term Noise Dependence of Spike Counts in Macaque Prefrontal Cortex

no code implementations NeurIPS 2008 Arno Onken, Steffen Grünewälder, Matthias Munk, Klaus Obermayer

Furthermore, copulas place a wide range of dependence structures at the disposal and can be used to analyze higher order interactions.

Dependence of Orientation Tuning on Recurrent Excitation and Inhibition in a Network Model of V1

no code implementations NeurIPS 2008 Klaus Wimmer, Marcel Stimberg, Robert Martin, Lars Schwabe, Jorge Mariño, James Schummers, David C. Lyon, Mriganka Sur, Klaus Obermayer

A quantitative analysis shows that the data provides strong evidence for a network model in which the afferent input is dominated by strong, balanced contributions of recurrent excitation and inhibition.

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