7 citations · 12 across the 6 of their papers we have counts for
6 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…
A Variational Latent Equilibrium for Learning in Neuronal Circuits
Simon Brandt, Paul Haider, Walter Senn +2
Brains remain unrivaled in their ability to recognize and generate complex spatiotemporal patterns. While AI is able to reproduce some of these capabilities, deep learning algorith…
Real-time processing of analog signals on accelerated neuromorphic hardware
Yannik Stradmann, Johannes Schemmel, Mihai A. Petrovici +1
Sensory processing with neuromorphic systems is typically done by using either event-based sensors or translating input signals to spikes before presenting them to the neuromorphic…
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…
Brain-Inspired Hardware for Artificial Intelligence: Accelerated Learning in a Physical-Model Spiking Neural Network
Timo C. Wunderlich, Akos F. Kungl, Eric Müller +2
Future developments in artificial intelligence will profit from the existence of novel, non-traditional substrates for brain-inspired computing. Neuromorphic computers aim to provi…
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…