Etienne Mueller

High-Performance Zebrafish (HPZ)

– A Python and bash toolkit designed to automate recurring brain imaging data tasks on a Slurm-based HPC setup
– End-to-end pipeline that consolidates multiple manual steps for loading, preprocessing, and detecting neurons and spikes in microscopy data into a single automated process, reducing manual intervention and error by a factor of five
– Automated setup for new users to easily work with zebrafish brain imaging data, improving onboarding efficiency

Convert2SNN

– A TensorFlow-based library that converts conventionally trained neural networks with continuous activation functions to spiking neural networks, with minimal to no performance loss, to estimate energy consumption in neuromorphic systems
– Supports key spike encoding techniques, including rate, population, and temporal coding, with the ability to estimate spike counts for efficiency evaluation, reducing the need for extensive hardware simulations during development

For the full list see: Github

Reverse Engineering Neural Connectivity: Mapping Neural Activity Data to Artificial Neural Networks for Synaptic Strength Analysis

E. Mueller, W. Qin, in 8th International Conference on Information Technology (InCIT), Chonburi, Thailand and Kanazawa, Japan, 2024, (accepted).

Neural Oscillations for Energy-Efficient Hardware Implementation of Sparsely Activated Deep Spiking Neural Networks,

E. Mueller, S. Klimaschka, D. Auge, A. Knoll, in Association for the Advancement of Artificial Intelligence (AAAI) Practical DL, Online (Vancouver, Canada), 2022, pp. 1-7.

Exploiting Inhomogeneities of Subthreshold Transistors as Populations of Spiking Neurons

E. Mueller, D. Auge, A. Knoll, in International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), Online (Fuzhou, China), 2022, pp. 1-8.

Spiking Transformer Networks: A Rate Coded Approach for Processing Sequential Data

E. Mueller, V. Studenyak, D. Auge, A. Knoll, in 7th Int. Conference on Systems and Informatics (ICSAI), Online (Jiaxing, China), 2021, pp. 1-5.

Minimizing Inference Time: Optimization Methods for Converted Deep Spiking Neural Networks

E. Mueller, J. Hansjakob, D. Auge, A. Knoll, in International Joint Conference on Neural Networks (IJCNN), Online (Shenzen, China), 2021, pp. 1-8.

A Survey of Encoding Techniques for Signal Processing in Spiking Neural Networks

D. Auge, J. Hille, E. Mueller, A. Knoll, Neural Processing Letters, vol. 53, issue 6, pp. 4693-4710, Dec 2021.

Normalization Hyperparameter Search for Converted Spiking Neural Networks

E. Mueller, D. Auge, A. Knoll, in Bernstein Computational Neuroscience Conference, Online (Berlin, Germany), 2021, P 8.

Hand Gesture Recognition in Range-Doppler Images Using Binary Activated Spiking Neural Networks

D. Auge, J. Hille, E. Mueller, A. Knoll, in IEEE International Conference on Automatic Face and Gesture Recognition, Online (Jodhpur, India), 2021, pp. 1-7.

End-to-end Spiking Neural Network for Speech Recognition Using Resonating Input Neurons

D. Auge, J. Hille, F. Kreutz, E. Mueller, A. Knoll, in 30th International Conference on Artificial Neural Networks (ICANN), Online (Bratislave, Slovakia), 2021, pp. 245-256.

Faster Conversion of Analog to Spiking Neural Networks by Error Centering

E. Mueller, J. Hansjakob, D. Auge, in Bernstein Computational Neuroscience Conference, Online (Berlin, Germany), 2020, P 146.

Hand Gesture Recognition using Hierarchical Temporal Memory on Radar Sequence Data

D. Auge, P. Wenner, E. Mueller, in Bernstein Computational Neuroscience Conference, Online (Berlin, Germany), 2020, P 3.

Resonate-and-Fire Neurons as Frequency Selective Input Encoders for Spiking Neural Networks

Daniel Auge, E. Mueller, Chair of Informatics, TUM, Munich, Technical Report TUM-I2083. 2020

For the full list see: Google Scholar