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.
You can reach out to me at: [firstname] [dot] [lastname] [at] [tugraz] [dot] [at]
Publications
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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 -
Spike-based agents for Multi-armed bandits (Master's thesis 2018, Information and Computer Engineering)
Advisor: Prof. Wolfgang Maass
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Gradient-based optimization of AMEA parameters (Bachelor's thesis 2018, Physics)
Advisor: Prof. Wolfgang von der Linden
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Automated Security Proofs for Symmetric Ciphers (Bachelor's thesis 2016, Telematik)
Advisor: Maria Eichlseder
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* equal contribution and team work