71 citations · 176 across the 8 of their papers we have counts for
8 papers
Stochasticity from function -- why the Bayesian brain may need no noise
Dominik Dold, Ilja Bytschok, Akos F. Kungl +6
An increasing body of evidence suggests that the trial-to-trial variability of spiking activity in the brain is not mere noise, but rather the reflection of a sampling-based encodi…
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…
Pattern representation and recognition with accelerated analog neuromorphic systems
Mihai A. Petrovici, Sebastian Schmitt, Johann Klähn +33
Despite being originally inspired by the central nervous system, artificial neural networks have diverged from their biological archetypes as they have been remodeled to fit partic…
Stochastic inference with spiking neurons in the high-conductance state
Mihai A. Petrovici, Johannes Bill, Ilja Bytschok +2
The highly variable dynamics of neocortical circuits observed in vivo have been hypothesized to represent a signature of ongoing stochastic inference but stand in apparent contrast…
The high-conductance state enables neural sampling in networks of LIF neurons
Mihai A. Petrovici, Ilja Bytschok, Johannes Bill +2
The apparent stochasticity of in-vivo neural circuits has long been hypothesized to represent a signature of ongoing stochastic inference in the brain. More recently, a theoretical…