Search Results for author: Jakob Jordan

Found 10 papers, 6 papers with code

DELAUNAY: a dataset of abstract art for psychophysical and machine learning research

1 code implementation28 Jan 2022 Camille Gontier, Jakob Jordan, Mihai A. Petrovici

This dataset provides a middle ground between natural images and artificial patterns and can thus be used in a variety of contexts, for example to investigate the sample efficiency of humans and artificial neural networks.

BIG-bench Machine Learning

Routing brain traffic through the von Neumann bottleneck: Parallel sorting and refactoring

no code implementations23 Sep 2021 Jari Pronold, Jakob Jordan, Brian J. N. Wylie, Itaru Kitayama, Markus Diesmann, Susanne Kunkel

With growing network size a compute node receives spikes from an increasing number of different source neurons until in the limit each synapse on the compute node has a unique source.

Learning cortical representations through perturbed and adversarial dreaming

1 code implementation9 Sep 2021 Nicolas Deperrois, Mihai A. Petrovici, Walter Senn, Jakob Jordan

We support this hypothesis by implementing a cortical architecture inspired by generative adversarial networks (GANs).

Learning Semantic Representations

Learning Bayes-optimal dendritic opinion pooling

no code implementations27 Apr 2021 Jakob Jordan, João Sacramento, Willem A. M. Wybo, Mihai A. Petrovici, Walter Senn

Pooling different opinions and weighting them according to their reliability is conducive to making good decisions.

Evolving Neuronal Plasticity Rules using Cartesian Genetic Programming

no code implementations8 Feb 2021 Henrik D. Mettler, Maximilian Schmidt, Walter Senn, Mihai A. Petrovici, Jakob Jordan

We formulate the search for phenomenological models of synaptic plasticity as an optimization problem.

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