I’m a theoretical neuroscientist turned neuromorphic hardware and embedded machine learning developer, working as a PostDoc with Prof. Gert Cauwenberghs in the Integrated Systems Neuroengineering lab at UC San Diego. Previously, I worked as a Chief Scientist at the Fraunhofer Institute for Integrated Circuits (IIS) in Germany. I received my PhD on the topic of “Neural mechanisms of information processing and transmission” 「1」 from the Institute of Cognitive Science at the University of Osnabrück. You can find a human-readable summary of my thesis here.

My primary research interest, then and now, has been to understand and model how neurons communicate so efficiently, and how computation emerges from this collective process. This is a broad question, so I dabble in machine learning, mathematics (primarily stochastic processes and probabilistic modeling), engineering and neuroscience, and I hope to bring the results of this work to fruition in real-world applications through neuromorphic hardware.

Johannes Leugering Research Scholar ISN Lab, UCSD

Experience.

Computational Neuroscience & Neuromorphic ComputingComp. Neuro. & Neuromorphics

  • PhD (German: Dr.rer.nat., summa cum laude) with thesis topic “Neural mechanisms of information processing and transmission” 「1」 and journal publications.
  • Book chapter on the topic Computational Elements of Circuits 「2」 in the book The Neocortex by the Ernst-StrĂĽngmann Forum.
  • Concepts & Architectures Expert for Neuromorphic Computing at the Embedded AI group Fraunhofer IIS
  • Bachelor’s & Master’s degree in Cognitive Science from Universität OsnabrĂĽck

Probabilistic Modeling

  • Worked on stochastic processes to model the interaction of intrinsic and homeostatic plasticity in neurons 「3」.
  • Worked with Olivera Stojanovic and the Robert Koch Institute on a spatio-temporal model of epidemic diseases using MCMC sampling 「4」.
  • Worked with Olivera Stojanovic and the Remote Sensing group on the prediction of Leaf Area Index from reflectance spectra using hierarchical Bayesian MCMC sampling models 「5」.

Machine Learning

  • Worked on (delay coupled) reservoir computing, neuromorphic hardware, and geospatial forecasting models with kernel methods & deep learning.
  • Developed and taught a lecture-series on Ensemble Methods for Machine Learning with Olivera Stojanovic
  • AI & ML were my focus topics of Bachelor & Master studies in Cognitive Science
  • Participated in the Deep Rain Project, which aims to predict precipitation at high spatial resolution from low-resolution data using Deep Learning techniques.

Scientific Programming

  • Developed & published code for various scientific­/numeric programming, data analysis & visualizations projects
  • Primary language: julia
  • Advanced knowledge also in:
    Python, Matlab, C++ and received formal training in SystemC
  • Most research code is published on my github page.

Science Communication

  • Wrote academic papers, magazine articles, patent applications and a book chapter; gave presentations at several conferences & fairs (see references).
  • Developed and taught a lecture-series on Ensemble Methods for Machine Learning with Olivera Stojanovic
  • Developed and taught a block-course on wearable electronics with Kristoffer Appel.
  • Developed and taught a block-course on decoding neural activity with Clemens Korndörfer.
  • Supervized >20 Bachelor’s and Master’s theses

Electronics

  • Ongoing Mixed-Signal VLSI design projects at ISN Lab.
  • Close cooperation with experienced hardware developers for professional work on Neuromorphic Hardware at Fraunhofer IIS.
  • Development of TRAUMSCHREIBER v1「6」, an open-source open-hardware bio-signal­/poly­somno­graphy acquisition device.

Documents.

Talks & Events.

  • Wednesday, 9. November '22, 19:00-20:00 @ Heinz Nixdorf Museum, Paderborn

    Talk:  Vom Bienenhirn zum Chip – Die Natur als Vorbild fĂĽr Computerchips von morgen (🇩🇪)

  • Friday, 1. April '22 @ NICE Conference 2022

    Talk:  Modeling and analyzing neuromorphic SNNs as discrete event systems (🇪🇺, )

    A brief presentation of my work on modeling and analyzing neuromorphic SNNs as discrete event systems (or Time Petri Nets, to be precise) at the Neuro-inspired Computational Elements Workshop 2022.

  • Monday, 18. October '21 @ Numenta

    Talk:  Neural computation with dendritic plateau potentials (🇪🇺, )

    We talk about our work on neural computation with dendritic plateau potentials. Johannes first frames the problem of sequence processing and makes the case that a neural model based on active dendrites and dendritic plateau potentials would help solve the problem. Pascal then explains their recent work on the computations in a neural model with segmented dendrites and one with stochastic synapses. He concludes the presentation by discussing the implications of this model.

  • Thursday, 18. March '21 @ NICE Conference 2021

    Talk:  Making spiking neurons more succinct with multi-compartment models (🇪🇺, )

    Spiking neurons consume energy for each spike they emit. Reducing the firing rate of each neuron — without sacrificing relevant information content — is therefore a critical constraint for energy efficient networks of spiking neurons in biology and neuromorphic hardware alike. The inherent complexity of biological neurons provides a possible mechanism to realize a good trade-off between these two conflicting objectives: multi-compartment neuron models can become selective to highly specific input patterns, and can thus produce informative yet sparse spiking codes. I’ll present a model of this mechanism and discuss its potential utility for spiking neural networks and neuromorphic hardware. This talk won the best presentation award.

  • Saturday, 3. September '16 @ MRMCD 2016, Darmstadt

    Talk:  Traumschreiber (🇩🇪, )

Publications.

  1. R. Bansen et al., “Future Computing: Overview of Technological Landscape,” Bitkom e.V., Albrechtstr. 10 \textbar 10117 Berlin, 2023. URL
  2. J. Leugering, P. Nieters, and G. Pipa, “Dendritic plateau potentials can process spike sequences across multiple time-scales,” Frontiers in Cognition, 2023. URL
  1. O. Stojanović, B. Siegmann, T. Jarmer, G. Pipa, and J. Leugering, “Bayesian hierarchical models can infer interpretable predictions of leaf area index from heterogeneous datasets,” Frontiers in Environmental Science, 2022. URL
  1. J. Leugering, “Neural mechanisms of information processing and transmission,” PhD thesis, Universität Osnabrück, Bibliothek der Universität Osnabrück, 2021.
  1. J. Leugering, “Making Spiking Neurons More Succinct with Multi-Compartment Models,” in Proceedings of the Neuro-Inspired Computational Elements Workshop, 2020, pp. 1–6. URL
  2. J. Leugering, “Neuromorphe Hardware,” DESIGN&ELEKTRONIK, no. 7/2020. pp. 41–47, Feb-2020. URL
  3. J. Leugering, “A Visit to the Neuromorphic Zoo,” in Embedded World Conference 2020 – Proceedings, 2020.
  1. J. Leugering, P. Nieters, and G. Pipa, “Computational Elements of Circuits,” in The Neocortex, vol. 27, W. Singer, T. J. Sejnowski, and P. Rakic, Eds. The MIT Press, 2019, pp. 195–209.
  2. J. Leugering, P. Nieters, and G. Pipa, “Neuromorpher Musterdetektor und neuromorphe Schaltkreisanordnung hiermit,” 2019.
  3. F. Meyer zu Driehausen, R. Busche, J. Leugering, and G. Pipa, “Bistable Perception in Conceptor Networks,” in Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions, 2019, pp. 24–34. DOI
  4. P. Nieters, J. Leugering, and G. Pipa, “Neuromorphic Adaptive Filters for Event Detection, Trained with a Gradient Free Online Learning Rule,” 2019, p. 1.
  5. M. Schultz et al., “DeepRain - Improved Local-Scale Prediction of Precipitation through Deep Learning,” 2019, vol. 21, p. 13625. URL
  6. O. Stojanović, J. Leugering, G. Pipa, S. Ghozzi, and A. Ullrich, “A Bayesian Monte Carlo Approach for Predicting the Spread of Infectious Diseases,” PLOS ONE, vol. 14, no. 12, p. e0225838, Dec. 2019. URL
  1. J. Leugering and G. Pipa, “A Unifying Framework of Synaptic and Intrinsic Plasticity in Neural Populations,” Neural Computation, vol. 30, no. 4, pp. 945–986, Jan. 2018. URL
  1. P. Nieters, J. Leugering, and G. Pipa, “Neuromorphic Computation in Multi-Delay Coupled Models,” IBM Journal of Research and Development, vol. 61, no. 2/3, pp. 8:7–8:9, Mar. 2017. URL
  1. K. Appel, J. Leugering, and G. Pipa, “’Traumschreiber’: Measuring and Manipulating Human Sleep with a Portable High-Quality but Low-Cost Polysomnographic System,” in Journal of Sleep Research, 2016, vol. 25, pp. 158–158.
  1. J. Leugering, “Adaptation of Neuronal Activation Functions to Arbitrary Distributions of In- and Output.” Bibliothek der Universität Osnabrück, 2015. .pdf