20 citations · 48 across the 5 of their papers we have counts for
5 papers
Optimal Population Codes for Control and Estimation
Alex Susemihl, Ron Meir, Manfred Opper
Agents acting in the natural world aim at selecting appropriate actions based on noisy and partial sensory observations. Many behaviors leading to decision mak- ing and action sele…
Temporal Autoencoding Improves Generative Models of Time Series
Chris Häusler, Alex Susemihl, Martin P Nawrot +1
Restricted Boltzmann Machines (RBMs) are generative models which can learn useful representations from samples of a dataset in an unsupervised fashion. They have been widely employ…
Moral foundations in an interacting neural networks society
Renato Vicente, Alex Susemihl, João Pedro Jericó +1
The moral foundations theory supports that people, across cultures, tend to consider a small number of dimensions when classifying issues on a moral basis. The data also show that…
Temporal Autoencoding Restricted Boltzmann Machine
Chris Häusler, Alex Susemihl
Much work has been done refining and characterizing the receptive fields learned by deep learning algorithms. A lot of this work has focused on the development of Gabor-like filter…
Dynamic State Estimation Based on Poisson Spike Trains: Towards a Theory of Optimal Encoding
Alex Susemihl, Ron Meir, Manfred Opper
Neurons in the nervous system convey information to higher brain regions by the generation of spike trains. An important question in the field of computational neuroscience is how…