189 citations · 1.4k across the 34 of their papers we have counts for
8 papers · 1 filter
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…
Spiking neurons with short-term synaptic plasticity form superior generative networks
Luziwei Leng, Roman Martel, Oliver Breitwieser +5
Spiking networks that perform probabilistic inference have been proposed both as models of cortical computation and as candidates for solving problems in machine learning. However,…
Spike-based probabilistic inference with correlated noise
Ilja Bytschok, Dominik Dold, Johannes Schemmel +2
A steadily increasing body of evidence suggests that the brain performs probabilistic inference to interpret and respond to sensory input and that trial-to-trial variability in neu…
An Accelerated Analog Neuromorphic Hardware System Emulating NMDA- and Calcium-Based Non-Linear Dendrites
Johannes Schemmel, Laura Kriener, Paul Müller +1
This paper presents an extension of the BrainScaleS accelerated analog neuromorphic hardware model. The scalable neuromorphic architecture is extended by the support for multi-comp…
Robustness from structure: Inference with hierarchical spiking networks on analog neuromorphic hardware
Mihai A. Petrovici, Anna Schroeder, Oliver Breitwieser +3
How spiking networks are able to perform probabilistic inference is an intriguing question, not only for understanding information processing in the brain, but also for transferrin…
A neuronal dynamics study on a neuromorphic chip
Wenyuan Li, Igor V. Ovchinnikov, Honglin Chen +7
Neuronal firing activities have attracted a lot of attention since a large population of spatiotemporal patterns in the brain is the basis for adaptive behavior and can also reveal…