26 citations · 110 across the 13 of their papers we have counts for
15 papers
Neuromorphic architectures as numerical solvers for computational neuroscience
Jakob Jordan, Ole Richter, Congyang Li +2
Neuromorphic computing is closely associated with spiking neuronal networks. However, an alternative class of so-called "rate-based" models arising from computational neuroscience…
Learning beyond sensations: how dreams organize neuronal representations
Nicolas Deperrois, Mihai A. Petrovici, Walter Senn +1
Semantic representations in higher sensory cortices form the basis for robust, yet flexible behavior. These representations are acquired over the course of development in an unsupe…
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…
A Modular Workflow for Performance Benchmarking of Neuronal Network Simulations
Jasper Albers, Jari Pronold, Anno Christopher Kurth +9
Modern computational neuroscience strives to develop complex network models to explain dynamics and function of brains in health and disease. This process goes hand in hand with ad…
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.…
Routing brain traffic through the von Neumann bottleneck: Efficient cache usage in spiking neural network simulation code on general purpose computers
Jari Pronold, Jakob Jordan, Brian J. N. Wylie +3
Simulation is a third pillar next to experiment and theory in the study of complex dynamic systems such as biological neural networks. Contemporary brain-scale networks correspond…
Routing brain traffic through the von Neumann bottleneck: Parallel sorting and refactoring
Jari Pronold, Jakob Jordan, Brian J. N. Wylie +3
Generic simulation code for spiking neuronal networks spends the major part of time in the phase where spikes have arrived at a compute node and need to be delivered to their targe…
Learning cortical representations through perturbed and adversarial dreaming
Nicolas Deperrois, Mihai A. Petrovici, Walter Senn +1
Humans and other animals learn to extract general concepts from sensory experience without extensive teaching. This ability is thought to be facilitated by offline states like slee…
Conductance-based dendrites perform Bayes-optimal cue integration
Jakob Jordan, João Sacramento, Willem A. M. Wybo +2
A fundamental function of cortical circuits is the integration of information from different sources to form a reliable basis for behavior. While animals behave as if they optimall…
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…
Deterministic networks for probabilistic computing
Jakob Jordan, Mihai A. Petrovici, Oliver Breitwieser +4
Neural-network models of high-level brain functions such as memory recall and reasoning often rely on the presence of stochasticity. The majority of these models assumes that each…
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…
The effect of heterogeneity on decorrelation mechanisms in spiking neural networks: a neuromorphic-hardware study
Thomas Pfeil, Jakob Jordan, Tom Tetzlaff +4
High-level brain function such as memory, classification or reasoning can be realized by means of recurrent networks of simplified model neurons. Analog neuromorphic hardware const…