Search Results for author:
Found 17 papers, 6 papers with code
Dependence of Orientation Tuning on Recurrent Excitation and Inhibition in a Network Model of V1
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.
Modeling Short-term Noise Dependence of Spike Counts in Macaque Prefrontal Cortex
Furthermore, copulas place a wide range of dependence structures at the disposal and can be used to analyze higher order interactions.
Correlation Coefficients are Insufficient for Analyzing Spike Count Dependencies
The linear correlation coefficient is typically used to characterize and analyze dependencies of neural spike counts.
Risk-sensitive Markov control processes
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.
Risk-sensitive Reinforcement Learning
We derive a family of risk-sensitive reinforcement learning methods for agents, who face sequential decision-making tasks in uncertain environments.
Regression with Linear Factored Functions
Many applications that use empirically estimated functions face a curse of dimensionality, because the integrals over most function classes must be approximated by sampling.
Non-Deterministic Policy Improvement Stabilizes Approximated Reinforcement Learning
This paper investigates a type of instability that is linked to the greedy policy improvement in approximated reinforcement learning.
The NIGENS General Sound Events Database
Computational auditory scene analysis is gaining interest in the last years.
Joining Sound Event Detection and Localization Through Spatial Segregation
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
Biophysically grounded mean-field models of neural populations under electrical stimulation
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.
Training Generative Networks with general Optimal Transport distances
We propose a new algorithm that uses an auxiliary neural network to express the potential of the optimal transport map between two data distributions.
Spatiotemporal patterns of adaptation-induced slow oscillations in a whole-brain model of slow-wave sleep
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.
Applications of optimal nonlinear control to a whole-brain network of FitzHugh-Nagumo oscillators
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.
Exploring the Similarity of Representations in Model-Agnostic Meta-Learning
Besides their tremendous success in these tasks, it has still not been fully revealed yet, why it works so well.
A modular framework for object-based saccadic decisions in dynamic scenes
We model this active scene exploration as a sequential decision making process.
Similarity of Pre-trained and Fine-tuned Representations
In transfer learning, only the last part of the networks - the so-called head - is often fine-tuned.
Transfer Learning for Segmentation Problems: Choose the Right Encoder and Skip the Decoder
In this work, we investigate the impact of transfer learning for segmentation problems, being pixel-wise classification problems that can be tackled with encoder-decoder architectures.
