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20102021
most citedNeuromorphic Hardware In The Loop: Training a Deep Spiking Network on the BrainScaleS Wafer-Scale System

126 citations · 481 across the 19 of their papers we have counts for

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Showing 2014Show all

7 papers · 1 filter

cs.ET2014

Switched-Capacitor Realization of Presynaptic Short-Term-Plasticity and Stop-Learning Synapses in 28 nm CMOS

Marko Noack, Johannes Partzsch, Christian Mayr +5

Synaptic dynamics, such as long- and short-term plasticity, play an important role in the complexity and biological realism achievable when running neural networks on a neuromorphi…

cs.ET2014★ 1 cited

A Biological-Realtime Neuromorphic System in 28 nm CMOS using Low-Leakage Switched Capacitor Circuits

Christian Mayr, Johannes Partzsch, Marko Noack +5

A switched-capacitor (SC) neuromorphic system for closed-loop neural coupling in 28 nm CMOS is presented, occupying 600 um by 600 um. It offers 128 input channels (i.e. presynaptic…

q-bio.NC2014

OTA based 200 GΩ resistance on 700 μm2 in 180 nm CMOS for neuromorphic applications

Christian Mayr, Michael Schultz, Marko Noack +3

Generating an exponential decay function with a time constant on the order of hundreds of milliseconds is a mainstay for neuromorphic circuits. Usually, either subthreshold circuit…

cs.CV2014★ 2 cited

Gabor-like Image Filtering using a Neural Microcircuit

C. Mayr, A. Heittmann, R. Schüffny

In this letter, we present an implementation of a neural microcircuit for image processing employing Hebbian-adaptive learning. The neuronal circuit utilizes only excitatory synaps…

cs.CV2014

Neighborhood Rank Order Coding for Robust Texture Analysis and Feature Extraction

C. Mayr, R. Schüffny

Research into the visual cortex and general neural information processing has led to various attempts to integrate pulse computation schemes in image analysis systems. Of interest…

cs.ET2014

Applying Spiking Neural Nets to Noise Shaping

C. Mayr, R. Schüffny

-In recent years, there has been an increased focus on the mechanics of information transmission in spiking neural networks. Especially the Noise Shaping properties of these networ…