Johannes Bill - Publications

Affiliations: 
Harvard Medical School, Boston, MA, United States 
Area:
Computational neuroscience, computational cognitive science, neuromorphic engineering

18 high-probability publications. We are testing a new system for linking publications to authors. You can help! If you notice any inaccuracies, please sign in and mark papers as correct or incorrect matches. If you identify any major omissions or other inaccuracies in the publication list, please let us know.

Year Citation  Score
2023 Noel JP, Bill J, Ding H, Vastola J, DeAngelis GC, Angelaki DE, Drugowitsch J. Causal inference during closed-loop navigation: parsing of self- and object-motion. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences. 378: 20220344. PMID 37545300 DOI: 10.1098/rstb.2022.0344  0.678
2023 Noel JP, Bill J, Ding H, Vastola J, DeAngelis GC, Angelaki DE, Drugowitsch J. Causal inference during closed-loop navigation: parsing of self- and object-motion. Biorxiv : the Preprint Server For Biology. PMID 36778376 DOI: 10.1101/2023.01.27.525974  0.677
2022 Bill J, Gershman SJ, Drugowitsch J. Visual motion perception as online hierarchical inference. Nature Communications. 13: 7403. PMID 36456546 DOI: 10.1038/s41467-022-34805-5  0.733
2021 Yang S, Bill J, Drugowitsch J, Gershman SJ. Human visual motion perception shows hallmarks of Bayesian structural inference. Scientific Reports. 11: 3714. PMID 33580096 DOI: 10.1038/s41598-021-82175-7  0.721
2020 Bill J, Pailian H, Gershman SJ, Drugowitsch J. Hierarchical structure is employed by humans during visual motion perception. Proceedings of the National Academy of Sciences of the United States of America. 117: 24581-24589. PMID 32938799 DOI: 10.1073/Pnas.2008961117  0.723
2019 Bill J, Pailian H, Gershman SJ, Drugowitsch J. Hierarchical motion structure is employed by humans during visual perception Journal of Vision. 19: 282. DOI: 10.1167/19.10.282  0.7
2016 Petrovici MA, Bill J, Bytschok I, Schemmel J, Meier K. Stochastic inference with spiking neurons in the high-conductance state. Physical Review. E. 94: 042312. PMID 27841474 DOI: 10.1103/Physreve.94.042312  0.447
2016 Serb A, Bill J, Khiat A, Berdan R, Legenstein R, Prodromakis T. Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses. Nature Communications. 7: 12611. PMID 27681181 DOI: 10.1038/Ncomms12611  0.603
2016 Sharpee TO, Destexhe A, Kawato M, Sekulić V, Skinner FK, Wójcik DK, Chintaluri C, Cserpán D, Somogyvári Z, Kim JK, Kilpatrick ZP, Bennett MR, Josić K, Elices I, Arroyo D, ... ... Bill J, et al. 25th Annual Computational Neuroscience Meeting: CNS-2016 Bmc Neuroscience. 17: 54. PMID 27534393 DOI: 10.1186/S12868-016-0283-6  0.342
2015 Bill J, Buesing L, Habenschuss S, Nessler B, Maass W, Legenstein R. Distributed Bayesian Computation and Self-Organized Learning in Sheets of Spiking Neurons with Local Lateral Inhibition. Plos One. 10: e0134356. PMID 26284370 DOI: 10.1371/Journal.Pone.0134356  0.639
2015 Probst D, Petrovici MA, Bytschok I, Bill J, Pecevski D, Schemmel J, Meier K. Probabilistic inference in discrete spaces can be implemented into networks of LIF neurons. Frontiers in Computational Neuroscience. 9: 13. PMID 25729361 DOI: 10.3389/Fncom.2015.00013  0.49
2015 Probst D, Petrovici M, Bytschok I, Bill J, Pecevski D, Schemmel J, Meier K. Probabilistic inference in discrete spaces can be implemented into networks of LIF neurons Frontiers in Computational Neuroscience. 9. DOI: 10.3389/fncom.2015.00013  0.49
2015 Jordan J, Tetzlaff T, Petrovici M, Breitwieser O, Bytschok I, Bill J, Schemmel J, Meier K, Diesmann M. Deterministic neural networks as sources of uncorrelated noise for probabilistic computations Bmc Neuroscience. 16: P62. DOI: 10.1186/1471-2202-16-S1-P62  0.422
2015 Petrovici MA, Bytschok I, Bill J, Schemmel J, Meier K. The high-conductance state enables neural sampling in networks of LIF neurons Bmc Neuroscience. 16: 1-2. DOI: 10.1186/1471-2202-16-S1-O2  0.451
2014 Bill J, Legenstein R. A compound memristive synapse model for statistical learning through STDP in spiking neural networks. Frontiers in Neuroscience. 8: 412. PMID 25565943 DOI: 10.3389/fnins.2014.00412  0.606
2010 Bill J, Schuch K, Brüderle D, Schemmel J, Maass W, Meier K. Compensating Inhomogeneities of Neuromorphic VLSI Devices Via Short-Term Synaptic Plasticity. Frontiers in Computational Neuroscience. 4: 129. PMID 21031027 DOI: 10.3389/Fncom.2010.00129  0.465
2010 Brüderle D, Bill J, Kaplan B, Kremkow J, Meier K, Müller E, Schemmel J. Simulator-like exploration of cortical network architectures with a mixed-signal VLSI system Iscas 2010 - 2010 Ieee International Symposium On Circuits and Systems: Nano-Bio Circuit Fabrics and Systems. 2784-2787. DOI: 10.1109/ISCAS.2010.5537005  0.394
2010 Brüderle D, Bill J, Kaplan B, Kremkow J, Meier K, Müller E, Schemmel J. Live demonstration: Simulator-like exploration of cortical network architectures with a mixed-signal VLSI system Iscas 2010 - 2010 Ieee International Symposium On Circuits and Systems: Nano-Bio Circuit Fabrics and Systems. 2783. DOI: 10.1109/ISCAS.2010.5537004  0.396
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