116 citations · 626 across the 37 of their papers we have counts for
5 papers · 1 filter
Non-Deterministic Policy Improvement Stabilizes Approximated Reinforcement Learning
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. We show empirically that non-deterministic pol…
Low-dimensional spike rate models derived from networks of adaptive integrate-and-fire neurons: Comparison and implementation
Moritz Augustin, Josef Ladenbauer, Fabian Baumann +1
The spiking activity of single neurons can be well described by a nonlinear integrate-and-fire model that includes somatic adaptation. When exposed to fluctuating inputs sparsely c…
Approximate Inference for Time-varying Interactions and Macroscopic Dynamics of Neural Populations
Christian Donner, Klaus Obermayer, Hideaki Shimazaki
The models in statistical physics such as an Ising model offer a convenient way to characterize stationary activity of neural populations. Such stationary activity of neurons may b…
Extending integrate-and-fire model neurons to account for the effects of weak electric fields and input filtering mediated by the dendrite
Florian Aspart, Josef Ladenbauer, Klaus Obermayer
How extracellular electric fields, as generated endogenously or through transcranial brain stimulation, affect the dynamics of large neuronal populations is of great interest but n…
A Fenchel-Moreau-Rockafellar type theorem on the Kantorovich-Wasserstein space with Applications in Partially Observable Markov Decision Processes
Vaios Laschos, Klaus Obermayer, Yun Shen +1
By using the fact that the space of all probability measures with finite support can be somehow completed in two different fashions, one generating the Arens-Eells space and anothe…