About

I am currently a researcher at the Institute of Theoretical Computer Science, University of Technology Graz. Under supervision of Professor Wolfgang Maass, I design and investigate algorithms to advance learning mechanism in brain-inspired artificial neural networks. In particular, my interests include topics of Computational Neuroscience, Machine- and Deep Learning.

In addition, I am advising Trever and AnyConcept in questions concerning Machine Learning.

[CV]

You can reach out to me at: [firstname] [dot] [lastname] [at] [tugraz] [dot] [at]

Publications

  • Visualizing a joint future of neuroscience and neuromorphic engineering
    Zenke F, Bohté S M, Clopath C, Comşa I M, Göltz J, Maass W, Masquelier T, Naud R, Neftci E O, Petrovici M A, Scherr F, Goodman D F
    Neuron 2021

  • Learning-to-learn for neuromorphic hardware
    Scherr F, Maass W
    IOP Publishing 2021 | Draft of contribution to Roadmap on Neuromorphic Computing and Engineering

  • Revisiting the role of synaptic plasticity and network dynamics for fast learning in spiking neural networks
    Subramoney A, Bellec G, Scherr F, Legenstein R, Maass W
    bioRxiv 2021 | under review

  • A solution to the learning dilemma for recurrent networks of spiking neurons
    Bellec G*, Scherr F*, Subramoney A, Hajek E, Salaj D, Legenstein R, Maass W
    Nature Communications 2020

  • One-shot learning with spiking neural networks
    Scherr F, Stöckl C, Maass W
    bioRxiv 2020 | under review

  • Eligibility Traces provide a data-inspired alternative to backpropagation through time
    Bellec G*, Scherr F*, Hajek E, Salaj D, Subramoney A, Legenstein R, Maass W
    NeurIPS 2019 | Real neurons and hidden units workshop

  • Slow processes of neurons enable a biologically plausible approximation to policy gradient
    Subramoney A*, Bellec G*, Scherr F*, Hajek E, Salaj D, Legenstein R, Maass W
    NeurIPS 2019 | Biological and artificial RL workshop

  • Reservoirs learn to learn
    Subramoney A, Scherr F, Maass W
    arXiv 2019

  • Neuromorphic Hardware learns to learn
    Bohnstingl T*, Scherr F*, Pehle C, Meier K, Maass W
    Frontiers in Neuroscience | Neuromorphic Engineering 2019

  • Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets
    Bellec G*, Scherr F*, Hajek E, Salaj D, Legenstein R, Maass W
    arXiv 2019

  • * equal contribution and team work


  • Spike-based agents for Multi-armed bandits (Master's thesis 2018, Information and Computer Engineering)
    Advisor: Prof. Wolfgang Maass
    [PDF]

  • Gradient-based optimization of AMEA parameters (Bachelor's thesis 2018, Physics)
    Advisor: Prof. Wolfgang von der Linden
    [PDF]

  • Automated Security Proofs for Symmetric Ciphers (Bachelor's thesis 2016, Telematik)
    Advisor: Maria Eichlseder
    [PDF]