activity
20102018
most citedSix networks on a universal neuromorphic computing substrate

189 citations · 289 across the 3 of their papers we have counts for

collaborators

6 papers

cs.NE2018

Accelerated physical emulation of Bayesian inference in spiking neural networks

Akos F. Kungl, Sebastian Schmitt, Johann Klähn +21

The massively parallel nature of biological information processing plays an important role for its superiority to human-engineered computing devices. In particular, it may hold the…

q-bio.NC2018

A Mixed-Signal Structured AdEx Neuron for Accelerated Neuromorphic Cores

Syed Ahmed Aamir, Paul Müller, Gerd Kiene +5

Here we describe a multi-compartment neuron circuit based on the Adaptive-Exponential I&F (AdEx) model, developed for the second-generation BrainScaleS hardware. Based on an existi…

q-bio.NC2018

An Accelerated LIF Neuronal Network Array for a Large Scale Mixed-Signal Neuromorphic Architecture

Syed Ahmed Aamir, Yannik Stradmann, Paul Müller +5

We present an array of leaky integrate-and-fire (LIF) neuron circuits designed for the second-generation BrainScaleS mixed-signal 65-nm CMOS neuromorphic hardware. The neuronal arr…

cs.NE20176 cited

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…

q-bio.NC2012189 cited

Six networks on a universal neuromorphic computing substrate

Thomas Pfeil, Andreas Grübl, Sebastian Jeltsch +7

In this study, we present a highly configurable neuromorphic computing substrate and use it for emulating several types of neural networks. At the heart of this system lies a mixed…

q-bio.NC201094 cited

A Comprehensive Workflow for General-Purpose Neural Modeling with Highly Configurable Neuromorphic Hardware Systems

Daniel Brüderle, Mihai A. Petrovici, Bernhard Vogginger +31

In this paper we present a methodological framework that meets novel requirements emerging from upcoming types of accelerated and highly configurable neuromorphic hardware systems.…