 | Up a level |
Senden, Mario; van Albada, Sacha J.; Pezzulo, Giovanni; Hashim, Ibrahim; Kroner, Alexander; Kurth, Anno C.; Lanillos, Pablo; Narayanan, Vaishnavi; Pennartz, Cyriel; Petrovici, Mihai A.; Steffen, Lea; Weidler, Tonio; Goebel, Rainer (2023). Modular-integrative modeling: a new framework for building brain models that blend biological realism and functional performance. National Science Review, 11(5) Oxford University Press 10.1093/nsr/nwad318
Chakrabarty, Samit; Petrovici, Mihai A. (2023). Is neuromorphic computing disruptive enough to 1) advance our understanding of the brain and 2) make the design and working of (bio)electronic devices efficient and scalable? (In Press). Research Directions: Bioelectronics, 1 Cambridge University Press 10.1017/bel.2023.5
Czischek, Stefanie; Baumbach, Andreas; Billaudelle, Sebastian; Cramer, Benjamin; Kades, Lukas; Pawlowski, Jan M.; Oberthaler, Markus; Schemmel, Johannes; Petrovici, Mihai A.; Gasenzer, Thomas; Gärttner, Martin (2022). Spiking neuromorphic chip learns entangled quantum states. SciPost Physics, 12(1) SciPost Foundation 10.21468/SciPostPhys.12.1.039
Billaudelle, Sebastian; Cramer, Benjamin; Petrovici, Mihai A.; Schreiber, Korbinian; Kappel, David; Schemmel, Johannes; Meier, Karlheinz (2021). Structural plasticity on an accelerated analog neuromorphic hardware system. Neural networks, 133, pp. 11-20. Elsevier 10.1016/j.neunet.2020.09.024
Zenke, Friedemann; Bohté, Sander M.; Clopath, Claudia; Comşa, Iulia M.; Göltz, Julian; Maass, Wolfgang; Masquelier, Timothée; Naud, Richard; Neftci, Emre O.; Petrovici, Mihai A.; Scherr, Franz; Goodman, Dan F.M. (2021). Visualizing a joint future of neuroscience and neuromorphic engineering. Neuron, 109(4), pp. 571-575. Cell Press 10.1016/j.neuron.2021.01.009
Kungl, Akos F.; Schmitt, Sebastian; Klähn, Johann; Müller, Paul; Baumbach, Andreas; Dold, Dominik; Kugele, Alexander; Müller, Eric; Koke, Christoph; Kleider, Mitja; Mauch, Christian; Breitwieser, Oliver; Leng, Luziwei; Gürtler, Nico; Güttler, Maurice; Husmann, Dan; Husmann, Kai; Hartel, Andreas; Karasenko, Vitali; Grübl, Andreas; ... (2019). Accelerated Physical Emulation of Bayesian Inference in Spiking Neural Networks. Frontiers in neuroscience, 13(1201), p. 1201. Frontiers Research Foundation 10.3389/fnins.2019.01201
Wunderlich, Timo; Kungl, Akos F.; Müller, Eric; Hartel, Andreas; Stradmann, Yannik; Aamir, Syed Ahmed; Grübl, Andreas; Heimbrecht, Arthur; Schreiber, Korbinian; Stöckel, David; Pehle, Christian; Billaudelle, Sebastian; Kiene, Gerd; Mauch, Christian; Schemmel, Johannes; Meier, Karlheinz; Petrovici, Mihai A. (2019). Demonstrating Advantages of Neuromorphic Computation: A Pilot Study. Frontiers in neuroscience, 13(260) Frontiers Research Foundation 10.3389/fnins.2019.00260
Jordan, Jakob; Petrovici, Mihai A.; Breitwieser, Oliver; Schemmel, Johannes; Meier, Karlheinz; Diesmann, Markus; Tetzlaff, Tom (2019). Deterministic networks for probabilistic computing. Scientific reports, 9(1), p. 18303. Springer Nature 10.1038/s41598-019-54137-7
Dold, Dominik; Bytschok, Ilja; Kungl, Akos F.; Baumbach, Andreas; Breitwieser, Oliver; Senn, Walter; Schemmel, Johannes; Meier, Karlheinz; Petrovici, Mihai A. (2019). Stochasticity from function — Why the Bayesian brain may need no noise. Neural networks, 119, pp. 200-213. Elsevier 10.1016/j.neunet.2019.08.002
Kriener, Laura; Göltz, Julian; Petrovici, Mihai A. (28 March 2022). The Yin-Yang dataset. In: NICE 2022: 9th Annual Neuro-Inspired Computational Elements Conference. Neuro-Inspired Computational Elements Conference (pp. 107-111). ACM 10.1145/3517343.3517380
Haider, Paul; Ellenberger, Benjamin; Kriener, Laura; Jordan, Jakob; Senn, Walter; Petrovici, Mihai A. (2021). Latent Equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons. Advances in Neural Information Processing Systems (NIPS), 35. MIT Press
