Publications Neuro-inspired Theory, Modeling and Applications

A. Korcsak-Gorzo, M.G. Müller, A. Baumbach, L. Leng, O.J. Breitwieser, S.J. van Albada, W. Senn, K. Meier, R. Legenstein, M. A. Petrovici.
Cortical oscillations support sampling-based computations in spiking neural networks. PLoS Comput Biol 2022 DOI PubMed pdf

arXiv

C. Gontier, J. Jordan, M.A. Petrovici.
DELAUNAY: a dataset of abstract art for psychophysical and machine learning research. arXiv 2022 pdf

GitHub project

N. Deperrois, M.A. Petrovici, W. Senn, and J. Jordan.
Learning cortical representations through perturbed and adversarial dreaming. eLIFE 2022 DOI pdf

arXiv

E. Kreutzer, W. Senn, M.A. Petrovici.
Natural-gradient learning for spiking neurons. eLIFE 2022 DOI pdf

arXiv

S. Czischek, A. Baumbach, S. Billaudelle, B. Cramer, L. Kades, J.M. Pawlowski, M.K. Oberthaler, J. Schemmel, M.A. Petrovici, T. Gasenzer and M. Gärttner.
Spiking neuromorphic chip learns entangled quantum states. SciPost Physics 39, 2022 DOI pdf

arXiv

K. Amunts, M. Axer, L. Bitsch, J. Bjaalie, A. Brovelli, S. Caspers, I. Costantini, E. D'Angelo, G.De Bonis, J. DeFelipe, A. Destexhe, T. Dickscheid, M. Diesmann, S. Eickhoff, A. Engel, J. Fousek, S. Furber, R. Goebel, O. Günterkün, J. Hellgren Kotaleski, C.C. Hilgetag, S.M. Hölter, Y. Ioannidis, V. Jirsa, W. Klijn, J. Kämpfer, T. Lippert, A. Meyer-Lindenberg, M. Migliore, Y. Morel, F. Morin, L. Oden, F. Panagiotaropoulos, P. Stanislao Paolucci, C. Pennartz, S. Petkoski, M.A. Petrovici, P. Ritter, S. Rotter, A. Rowald, S. Ruland, P. Ryvlin, A. Salles, M. V. Sanchez-Vives, J. Schemmel, B. Thirion.
The coming decade of digital brain research - A vision for neuroscience at the intersection of technology and computing. 2022 DOI pdf

L. Kriener, J. Göltz, M. A. Petrovici.
The Yin-Yang dataset. Neuro-Inspired Computational Elements Conference Proceedings 2022 DOI pdf

arXiv

R. Klassert, A. Baumbach, M.A. Petrovici, and M. Gärttner.
Variational learning of quantum ground states on spiking neuromorphic hardware. iScience 2022 DOI pdf

arXiv

A. Baumbach, S. Billaudelle, V. Sabado and M.A. Petrovici.
BrainScaleS: Greater Versatility for Neuromorphic Emulation. ERCIM April, 2021

Article

J. Jordan, M. Schmidt, W. Senn, M.A. Petrovici.
Evolving interpretable plasticity for spiking networks. eLIFE 2021 DOI PubMed pdf

arXiv

H.D. Mettler, M. Schmidt, W. Senn, M.A. Petrovici, J. Jordan.
Evolving Neuronal Plasticity Rules using Cartesian Genetic Programming. arXiv 2021 pdf

J. Göltz, L. Kriener, V. Sabado and M.A. Petrovici.
Fast and Energy-efficient Deep Neuromorphic Learning. ERCIM April, 2021

Article

J. Göltz, L. Kriener, A. Baumbach, S. Billaudelle, O. Breitwieser, B. Cramer, D. Dold, A.F. Kungl, W. Senn, J. Schemmel, K. Meier, M.A. Petrovici.
Fast and energy-efficient neuromorphic deep learning with first-spike times. Nature Machine Intelligence 823–835, 2021 DOI pdf

arXiv

P. Haider, B. Ellenberger, L. Kriener, J. Jordan, W. Senn, M.A. Petrovici.
Latent Equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons. Advances in Neural Information Processing Systems (NeurIPS) 2021 pdf

arXiv

J. Jordan, J. Sacramento, W.A.M. Wybo, M.A. Petrovici, W. Senn.
Learning Bayes-optimal dendritic opinion pooling. arXiv 2021 pdf

C. Zhao, Y.F. Widmer, S. Diegelmann, M.A. Petrovici, S.G. Sprecher, W. Senn.
Predictive olfactory learning in Drosophila. Sci Rep 2021 DOI PubMed pdf

bioRxiv

S. Billaudelle, B. Cramer, M.A. Petrovici, K. Schreiber, D. Kappel, J. Schemmel, K. Meier.
Structural plasticity on an accelerated analog neuromorphic hardware system. Neural Networks 133, 11-20, 2021 DOI pdf

arXiv

H.D. Mettler, V. Sabado, W. Senn, M.A. Petrovici and J. Jordan .
Uncovering Neuronal Learning Principles through Artificial Evolution. ERCIM April, 2021

Article

F. Zenke, S.M. Bohté, C. Clopath, I.M. Comşa, J. Göltz, W. Maass, T. Masquelier, R. Naud, E.O. Neftci, M.A. Petrovici, F. Scherr, and D.F.M. Goodman.
Visualizing a joint future of neuroscience and neuromorphic engineering. Neuron 571-575, 2021 DOI

S. Billaudelle, Y. Stradmann, K. Schreiber, B. Cramer, A. Baumbach, D. Dold, J. Göltz, A.F. Kungl, T.C. Wunderlich, A. Hartel, E. Müller, O. Breitwieser, C. Mauch, M. Kleider, A. Grübl, Da. Stöckel, C. Pehle, A. Heimbrecht, P. Spilger, G. Kiene, V. Karasenko, W. Senn, M.A. Petrovici, J. Schemmel, K. Meier.
Versatile emulation of spiking neural networks on an accelerated neuromorphic substrate. IEEE Xplore 2020 DOI pdf

arXiv

A.F. Kungl, S. Schmitt, J. Klähn, P. Müller, A. Baumbach, D. Dold, A. Kugele, N. Gürtler, E. Müller, C. Koke, M. Kleider, C. Mauch, O. Bretwieser, M. Güttler, D. Husmann, K. Husmann, J. Ilmberger, A. Hartel, V. Karasenko, A. Grübl, J. Schemmel, K. Meier, M.A. Petrovici.
Accelerated physical emulation of Bayesian inference in spiking neural networks. Front. Neuroscience 2019 DOI pdf

arXiv

T. Wunderlich, A.F. Kungl, E. Müller, A. Hartel, Y. Stradmann, S. A. Aamir, A. Grübl, A. Heimbrecht, K. Schreiber, D. Stöckel, C. Pehle, S. Billaudelle, G. Kiene, C. Mauch, J. Schemmel, K. Meier and M.A. Petrovici.
Demonstrating Advantages of Neuromorphic Computation: A Pilot Study. Front. Neuroscience 3:260, 2019 DOI pdf

arXiv

J. Jordan, M.A. Petrovici, O. Breitwieser, J. Schemmel, K. Meier, M. Diesmann & T. Tetzlaff.
Deterministic networks for probabilistic computing. Nature Scientific Reports 9:18303, 2019 DOI pdf

arXiv

D. Dold, A.F. Kungl, J. Sacramento, M.A. Petrovici, K. Schindler, J. Binas, Y. Bengio, W. Senn.
Lagrangian neurodynamics for real-time error-backpropagation across cortical areas. 2019 pdf

D. Dold, I. Bytschok, A.F. Kungl, A. Baumbach, O. Breitwieser, W. Senn, J. Schemmel, K. Meier, M.A. Petrovici.
Stochasticity from function - Why the Bayesian brain may need no noise. Neural Networks 2019 DOI pdf

arXiv

Luziwei Leng, R. Martel, O. Breitwieser, I. Bytschok, W. Senn, J. Schemmel, K. Meier & M.A. Petrovici .
Spiking neurons with short-term synaptic plasticity form superior generative networks. Nature Scientific Reports 8: 10651, 2018 DOI pdf

arXiv

S. Schmitt, J. Klähn, G. Bellec, A. Grübl, M. Güttler, A. Hartel, S. Hartmann, D. Husmann, K. Husmann, S. Jeltsch, V. Karasenko, M. Kleider, C. Koke, A. Kononov, C. Mauch, E. Müller, P. Müller, J. Partzsch, M.A. Petrovici, S. Schiefer, S. Scholze, V. Thanasoulis, B. Vogginger, R. Legenstein, W. Maass, C. Mayr, R. Schüffny, J. Schemmel, K. Meier.
Neuromorphic hardware in the loop: Training a deep spiking network on the BrainScaleS wafer-scale system. Proceedings of 2017 IJCNN online, 2017 DOI pdf

arXiv

M.A. Petrovici, S. Schmitt, J. Klähn, D. Stöckel, A. Schroeder, G. Bellec, J. Bill, O. Breitwieser, I. Bytschok, A. Grübl, M. Güttler, A. Hartel, S. Hartmann, D. Husmann, K. Husmann, S. Jeltsch, V. Karasenko, M. Kleider, C. Koke, A. Kononov, C. Mauch, E. Müller, P. Müller, J. Partzsch, T. Pfeil, S. Schiefer, S. Scholze, A. Subramoney, V. Thanasoulis, B. Vogginger, R. Legenstein, W. Maass, R. Schüffny, C. Mayr, J. Schemmel, K. Meier.
Pattern representation and recognition with accelerated analog neuromorphic systems. Proceedings of 2017 IEEE International Symposium on Circuits and Systems (ISCAS) 2017 DOI pdf

arXiv

M.A. Petrovici, A. Schroeder, O. Breitwieser, A. Grübl, J. Schemmel, K. Meier.
Robustness from structure: Inference with hierarchical spiking networks on analog neuromorphic hardware. Proceedings of 2017 IJCNN online, 2017 DOI pdf

arXiv

M.A. Petrovici.
Form Versus Function: Theory and Models for Neuronal Substrates . 2016 DOI pdf

M.A. Petrovici, J. Bill, I. Bytschok, J. Schemmel, K. Meier.
Stochastic inference with spiking neurons in the high-conductance state. PhysRev 042312, 2016 DOI

arXiv

D. Probst, M.A. Petrovici, I. Bytschok, J. Bill, D. Pecevski, J. Schemmel, K. Meier.
Probabilistic inference in discrete spaces can be implemented into networks of LIF neurons. Front. Comput.Neurosci. 2015 DOI

arXiv

M.A. Petrovici, B. Vogginger, P. Müller, O. Breitwieser, M. Lundqvist, L. Muller, M. Ehrlich, A. Destexhe, A. Lansner, R. Schüffny, J. Schemmel, K. Meier .
Characterization and Compensation of Network-Level Anomalies in Mixed-Signal Neuromorphic Modeling Platforms. PLoS ONE 2014 DOI

arXiv

T. Pfeil, A. Grübl, S. Jeltsch, E. Müller, P. Müller, M.A. Petrovici, M. Schmuker, D. Brüderle, J. Schemmel, K. Meier.
Six networks on a universal neuromorphic computing substrate. Front. Neuroscience 2013 DOI

arXiv

M.A. Petrovici, J. Bill, I. Bytschok, J. Schemmel, K. Meier.
Stochastic inference with deterministic spiking neurons. arXiv 2013 pdf

D. Brüderle, M.A. Petrovici, B. Vogginger, M. Ehrlich, T. Pfeil, S. Millner, A. Grübl, K. Wendt, E. Müller and M.O. Schwartz, K. Meier .
A comprehensive workflow for general-purpose neural modeling with highly configurable neuromorphic hardware systems. Biol. Cybern. 104, pages263–296, 2011 DOI

arXiv

F Carminati, P. Foka, P. Giubellino, A. Morsch, G. Paic, J.-P. Revol, K. Safarík, Y. Schutz, A. Wiedemann, M.A. Petrovici, the ALICE Collaboration.
ALICE: Physics Performance Report, Volume I. J Phys G: Nucl. Part. Phys. 2004