7 citations · 9 across the 4 of their papers we have counts for
6 papers
DELAUNAY: a dataset of abstract art for psychophysical and machine learning research
Camille Gontier, Jakob Jordan, Mihai A. Petrovici
Image datasets are commonly used in psychophysical experiments and in machine learning research. Most publicly available datasets are comprised of images of realistic and natural o…
Latent Equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons
Paul Haider, Benjamin Ellenberger, Laura Kriener +3
The response time of physical computational elements is finite, and neurons are no exception. In hierarchical models of cortical networks each layer thus introduces a response lag.…
Evolving Neuronal Plasticity Rules using Cartesian Genetic Programming
Henrik D. Mettler, Maximilian Schmidt, Walter Senn +2
We formulate the search for phenomenological models of synaptic plasticity as an optimization problem. We employ Cartesian genetic programming to evolve biologically plausible huma…
Conductance-based dendrites perform reliability-weighted opinion pooling
Jakob Jordan, João Sacramento, Mihai A. Petrovici +1
Cue integration, the combination of different sources of information to reduce uncertainty, is a fundamental computational principle of brain function. Starting from a normative mo…
Evolving to learn: discovering interpretable plasticity rules for spiking networks
Jakob Jordan, Maximilian Schmidt, Walter Senn +1
Continuous adaptation allows survival in an ever-changing world. Adjustments in the synaptic coupling strength between neurons are essential for this capability, setting us apart f…
Closing the loop between neural network simulators and the OpenAI Gym
Jakob Jordan, Philipp Weidel, Abigail Morrison
Since the enormous breakthroughs in machine learning over the last decade, functional neural network models are of growing interest for many researchers in the field of computation…