This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Whittington, J. C., McCaffary, D., Bakermans, J. J., & Behrens, T. E. How to build a cognitive map. Nature Neuroscience, 1-16. (2022).WhittingtonJ. C.McCaffaryD.BakermansJ. J.BehrensT. E.How to build a cognitive map. Nature Neuroscience, 1-16. (2022).Search in Google Scholar
Farzanfar, D., Spiers, H. J., Moscovitch, M., & Rosenbaum, From cognitive maps to spatial schemas. Nature Reviews Neuroscience, R. S. (2022).FarzanfarD.SpiersH. J.MoscovitchM.Rosenbaum, From cognitive maps to spatial schemas. Nature Reviews Neuroscience, R. S. (2022).Search in Google Scholar
Rueckemann, J. W., Sosa, M., Giocomo, L. M., & Buffalo, E. A. (2021). The grid code for ordered experience. Nature Reviews Neuroscience, 22(10), 637-649.RueckemannJ. W.SosaM.GiocomoL. M.BuffaloE. A. (2021). The grid code for ordered experience. Nature Reviews Neuroscience, 22(10), 637-649.Search in Google Scholar
Andrew Szot and Alex Clegg et al. Habitat 2.0: Training Home Assistants to Rearrangetheir Habitat, Advances in Neural Information Processing Systems, 2021.SzotAndrewCleggAlexHabitat 2.0: Training Home Assistants to Rearrangetheir Habitat, Advances in Neural Information Processing Systems, 2021.Search in Google Scholar
Foster, D., Morris, R., Dayan, P. et al. A model of hippocampally dependent navigation, using the temporal difference learning rule. Hippocampus 10, 1–16 (2021).FosterD.MorrisR.DayanP.A model of hippocampally dependent navigation, using the temporal difference learning rule. Hippocampus10, 1–16 (2021).Search in Google Scholar
The Tolman-Eichenbaum Machine: Unifying Spaceand Relational Memory through Generalization in the Hippocampal Formation. Whittington et al., 2020, Cell 183, 1249–1263.Whittington, 2020The Tolman-Eichenbaum Machine: Unifying Spaceand Relational Memory through Generalization in the Hippocampal Formation., Cell183, 1249–1263.Search in Google Scholar
Everett M, Chen Y F, How J P. Collision avoidance in pedestrian-rich environments with deep reinforcement learning[J]. IEEE Access, 2021, 9: 10357-10377.EverettMChenY FHowJ P.Collision avoidance in pedestrian-rich environments with deep reinforcement learning[J]. IEEE Access, 2021, 9: 10357-10377.Search in Google Scholar
Shanshan Qin, Shiva Farashahi, David Lipshutz, Coordinated drift of receptive fields in Hebbian/anti-Hebbian network models during noisy representation learning, Nature Neuroscience, pages339–349 (2023)QinShanshanFarashahiShivaLipshutzDavid, Coordinated drift of receptive fields in Hebbian/anti-Hebbian network models during noisy representation learning, Nature Neuroscience, pages339–349 (2023)Search in Google Scholar
Laura Cantini, Hope4Genes: a Hopfield-like class prediction algorithm for transcriptomic data, Scientific Reports, 337 (2019)CantiniLaura, Hope4Genes: a Hopfield-like class prediction algorithm for transcriptomic data, Scientific Reports, 337 (2019)Search in Google Scholar
Zhila Agharezaei, Reza Firouzi, Samira Hassanzadeh, Computer-aided diagnosis of keratoconus through VAE-augmented images using deep learning, Scientific Reports, 20586 (2023).AgharezaeiZhilaFirouziRezaHassanzadehSamira, Computer-aided diagnosis of keratoconus through VAE-augmented images using deep learning, Scientific Reports, 20586 (2023).Search in Google Scholar