Eric Müller

Affiliations:
  • Heidelberg University, Germany (PhD 2014)


According to our database1, Eric Müller authored at least 33 papers between 2009 and 2024.

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Timeline

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Bibliography

2024
jaxsnn: Event-driven Gradient Estimation for Analog Neuromorphic Hardware.
CoRR, 2024

Towards Large-scale Network Emulation on Analog Neuromorphic Hardware.
CoRR, 2024

Emulating insect brains for neuromorphic navigation.
CoRR, 2024

2023
Simulation-based inference for model parameterization on analog neuromorphic hardware.
Neuromorph. Comput. Eng., December, 2023

From clean room to machine room: commissioning of the first-generation BrainScaleS wafer-scale neuromorphic system.
Neuromorph. Comput. Eng., September, 2023

Gradient-based methods for spiking physical systems.
CoRR, 2023

Event-based Backpropagation for Analog Neuromorphic Hardware.
CoRR, 2023

hxtorch.snn: Machine-learning-inspired Spiking Neural Network Modeling on BrainScaleS-2.
Proceedings of the Neuro-Inspired Computational Elements Conference, 2023

2022
Demonstrating Analog Inference on the BrainScaleS-2 Mobile System.
IEEE Open J. Circuits Syst., 2022

The operating system of the neuromorphic BrainScaleS-1 system.
Neurocomputing, 2022

Spiking Neural Network Equalization for IM/DD Optical Communication.
CoRR, 2022

A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware.
CoRR, 2022

The BrainScaleS-2 accelerated neuromorphic system with hybrid plasticity.
CoRR, 2022

Demonstrating BrainScaleS-2 Inter-Chip Pulse-Communication using EXTOLL.
Proceedings of the NICE 2022: Neuro-Inspired Computational Elements Conference, 2022

2021
BrainScaleS Large Scale Spike Communication using Extoll.
CoRR, 2021

2020
Extending BrainScaleS OS for BrainScaleS-2.
CoRR, 2020

Inference with Artificial Neural Networks on Analog Neuromorphic Hardware.
Proceedings of the IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning, 2020

hxtorch: PyTorch for BrainScaleS-2 - Perceptrons on Analog Neuromorphic Hardware.
Proceedings of the IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning, 2020



2019
Brain-Inspired Hardware for Artificial Intelligence: Accelerated Learning in a Physical-Model Spiking Neural Network.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2019: Theoretical Neural Computation, 2019

2018
Code Generation in Computational Neuroscience: A Review of Tools and Techniques.
Frontiers Neuroinformatics, 2018

Demonstrating Advantages of Neuromorphic Computation: A Pilot Study.
CoRR, 2018

Generative models on accelerated neuromorphic hardware.
CoRR, 2018

2017
Neuromorphic Hardware In The Loop: Training a Deep Spiking Network on the BrainScaleS Wafer-Scale System.
CoRR, 2017



2014
Novel operation modes of accelerated neuromorphic hardware.
PhD thesis, 2014

2011
A comprehensive workflow for general-purpose neural modeling with highly configurable neuromorphic hardware systems.
Biol. Cybern., 2011

2010
Simulator-like exploration of cortical network architectures with a mixed-signal VLSI system.
Proceedings of the International Symposium on Circuits and Systems (ISCAS 2010), May 30, 2010

Live demonstration: Simulator-like exploration of cortical network architectures with a mixed-signal VLSI system.
Proceedings of the International Symposium on Circuits and Systems (ISCAS 2010), May 30, 2010

A Software Framework for Mapping Neural Networks to a Wafer-scale Neuromorphic Hardware System.
Proceedings of the Artificial Neural Networks and Intelligent Information Processing, 2010

2009
Establishing a novel modeling tool: a python-based interface for a neuromorphic hardware system.
Frontiers Neuroinformatics, 2009


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