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20102020
most citedSix networks on a universal neuromorphic computing substrate

189 citations · 717 across the 10 of their papers we have counts for

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6 papers · 1 filter

q-bio.NC2018★ 54 cited

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★ 124 cited

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…

q-bio.NC2017★ 21 cited

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…

q-bio.NC2014★ 55 cited

Characterization and Compensation of Network-Level Anomalies in Mixed-Signal Neuromorphic Modeling Platforms

Mihai A. Petrovici, Bernhard Vogginger, Paul Müller +9

Advancing the size and complexity of neural network models leads to an ever increasing demand for computational resources for their simulation. Neuromorphic devices offer a number…

q-bio.NC2012★ 189 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.NC2010★ 94 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.…