Johannes Bill

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Karlheinz MeierRuprecht-Karls-Universität HeidelbergVerified email at kip.uni-heidelberg.de
Johannes SchemmelHeidelberg UniversityVerified email at kip.uni-heidelberg.de
Mihai A. PetroviciGroup Leader, NeuroTMA Lab, University of Bern / Heidelberg UniversityVerified email at unibe.ch
Bernhard NesslerSCCH GmbH & Johannes Kepler University Linz, AustriaVerified email at scch.at
Wolfgang MaassProfessor of Computer Science, Graz University of TechnologyVerified email at igi.tugraz.at
Lars BuesingGoogle DeepMindVerified email at google.com
Robert LegensteinInstitute for Theoretical Computer Science, Graz University of TechnologyVerified email at igi.tugraz.at
Themis ProdromakisRegius Chair of Engineering, Centre for Electronics Frontiers, University of EdinburghVerified email at ed.ac.uk
Radu BerdanKioxiaVerified email at kioxia.com
Alex SerbReader, University of EdinburghVerified email at ed.ac.uk
Samuel GershmanProfessor, Harvard UniversityVerified email at fas.harvard.edu
Oliver BreitwieserDr. rer. nat., Heidelberg UniversityVerified email at kip.uni-heidelberg.de
Dejan PecevskiAI/ML Department, Jumio Corp., Vienna, AustriaVerified email at jumio.com
Jan DrugowitschAssociate Professor, Harvard Medical SchoolVerified email at hms.harvard.edu
Bernhard KaplanTerakiVerified email at teraki.com
Jens KremkowNeuroscience Research Center, Charité - Universitätsmedizin Berlin, Berlin, GermanyVerified email at charite.de
Hrag PailianHarvard UniversityVerified email at fas.harvard.edu
Luziwei LengCentral Research Institute, Huawei 2012 LaboratoriesVerified email at huawei.com
Thomas PfeilBosch Center for Artificial IntelligenceVerified email at mailbox.org
Igor LesanovskyUniversität TübingenVerified email at uni-tuebingen.de

Johannes Bill

Harvard Medical School

Verified email at hms.harvard.edu - Homepage

Computational Neuroscience Machine Learning Neuromorphic Engineering


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Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons L Buesing, J Bill, B Nessler, W Maass PLoS computational biology 7 (11), e1002211, 2011	421	2011
Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses A Serb, J Bill, A Khiat, R Berdan, R Legenstein, T Prodromakis Nature communications 7 (1), 1-9, 2016	286	2016
A compound memristive synapse model for statistical learning through STDP in spiking neural networks J Bill, R Legenstein Frontiers in neuroscience 8, 412, 2014	108	2014
Stochastic inference with spiking neurons in the high-conductance state MA Petrovici, J Bill, I Bytschok, J Schemmel, K Meier Physical Review E 94 (4), 042312, 2016	65	2016
Stochastic inference with deterministic spiking neurons MA Petrovici, J Bill, I Bytschok, J Schemmel, K Meier arXiv preprint arXiv:1311.3211, 2013	43	2013
Homeostatic plasticity in Bayesian spiking networks as Expectation Maximization with posterior constraints S Habenschuss, J Bill, B Nessler Advances in neural information processing systems 25, 2012	35	2012
Probabilistic Inference in Discrete Spaces Can Be Implemented into Networks of LIF Neurons D Probst, MA Petrovici, I Bytschok, J Bill, D Pecevski, J Schemmel, ... Frontiers in Computational Neuroscience 9, 2015	33	2015
Compensating inhomogeneities of neuromorphic VLSI devices via short-term synaptic plasticity J Bill, K Schuch, D Brüderle, J Schemmel, W Maass, K Meier Frontiers in computational neuroscience 4, 129, 2010	29	2010
Distributed bayesian computation and self-organized learning in sheets of spiking neurons with local lateral inhibition J Bill, L Buesing, S Habenschuss, B Nessler, W Maass, R Legenstein PloS one 10 (8), e0134356, 2015	20	2015
Pattern representation and recognition with accelerated analog neuromorphic systems MA Petrovici, S Schmitt, J Klähn, D Stöckel, A Schroeder, G Bellec, J Bill, ... 2017 IEEE International Symposium on Circuits and Systems (ISCAS), 1-4, 2017	18	2017
Simulator-like exploration of cortical network architectures with a mixed-signal VLSI system D Brüderle, J Bill, B Kaplan, J Kremkow, K Meier, E Müller, J Schemmel 2010 IEEE International Symposium on Circuits and Systems (ISCAS), 2784-8787, 2010	16	2010
25th annual computational neuroscience meeting: CNS-2016 TO Sharpee, A Destexhe, M Kawato, V Sekulić, FK Skinner, DK Wójcik, ... BMC neuroscience 17 (1), 1-112, 2016	15	2016
Hierarchical structure is employed by humans during visual motion perception J Bill, H Pailian, SJ Gershman, J Drugowitsch Proceedings of the National Academy of Sciences 117 (39), 24581-24589, 2020	13	2020
The high-conductance state enables neural sampling in networks of LIF neurons MA Petrovici, I Bytschok, J Bill, J Schemmel, K Meier BMC Neuroscience 16 (1), 1-2, 2015	12	2015
Self-stabilizing network architectures on a neuromorphic hardware system J Bill	12	2008
Resonant quantum dynamics of neutral spin-1 particles in a magnetic guide J Bill, MI Trappe, I Lesanovsky, P Schmelcher Physical Review A 73 (5), 053609, 2006	8	2006
Spiking neural networks as superior generative and discriminative models L Leng, MA Petrovici, R Martel, I Bytschok, O Breitwieser, J Bill, ... Cosyne Abstracts, Salt Lake City USA, 2016	6	2016
Human visual motion perception shows hallmarks of Bayesian structural inference S Yang, J Bill, J Drugowitsch, SJ Gershman Scientific reports 11 (1), 1-14, 2021	5	2021
Fast sampling with neuromorphic hardware MA Petrovici, D Stöckel, I Bytschok, J Bill, T Pfeil, J Schemmel, K Meier arXiv preprint arXiv:1311.3211, 2013	4	2013
Neural Networks as Sources of uncorrelated Noise for functional neuralSystems J Jordan, I Bytschok, T Tetzlaff, T Pfeil, O Breitwieser, J Bill, M Diesmann, ... INM Retreat 2014, 2014	2	2014

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