Search Results for author: Jakob Jordan

Found 12 papers, 6 papers with code

Backpropagation through space, time, and the brain

no code implementations25 Mar 2024 Benjamin Ellenberger, Paul Haider, Jakob Jordan, Kevin Max, Ismael Jaras, Laura Kriener, Federico Benitez, Mihai A. Petrovici

In particular, GLE exploits the ability of biological neurons to phase-shift their output rate with respect to their membrane potential, which is essential in both directions of information propagation.

Learning beyond sensations: how dreams organize neuronal representations

no code implementations3 Aug 2023 Nicolas Deperrois, Mihai A. Petrovici, Walter Senn, Jakob Jordan

However, brains are known to generate virtual experiences, such as during imagination and dreaming, that go beyond previously experienced inputs.

Contrastive Learning

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

Conductance-based dendrites perform Bayes-optimal cue integration

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

We propose a novel, Bayesian view on the dynamics of conductance-based neurons and synapses which suggests that they are naturally equipped to optimally perform information integration.

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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