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Learning a Continuous Attractor Neural Network from Real Images.

, , , , , and . ICONIP (4), volume 10637 of Lecture Notes in Computer Science, page 622-631. Springer, (2017)

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Neural Information Processing in Hierarchical Prototypical Networks., , , , , and . ICONIP (3), volume 11303 of Lecture Notes in Computer Science, page 603-611. Springer, (2018)Neural feedback facilitates rough-to-fine information retrieval., , , , , , , and . Neural Networks, (2022)Dynamical information encoding in neural adaptation., , , , , and . EMBC, page 3060-3063. IEEE, (2016)Push-pull Feedback Implements Hierarchical Information Retrieval Efficiently., , , , , , , and . NeurIPS, page 5702-5711. (2019)Dynamic Information Encoding With Dynamic Synapses in Neural Adaptation., , , , and . Frontiers Comput. Neurosci., (2018)Oscillatory Tracking of Continuous Attractor Neural Networks Account for Phase Precession and Procession of Hippocampal Place Cells., , , , , , and . NeurIPS, (2022)Spike Frequency Adaptation Implements Anticipative Tracking in Continuous Attractor Neural Networks., , , and . NIPS, page 505-513. (2014)A brain-inspired computational model for spatio-temporal information processing., , , , , and . Neural Networks, (2021)A Synaptical Story of Persistent Activity with Graded Lifetime in a Neural System., , , and . NIPS, page 352-360. (2014)Learning, Storing, and Disentangling Correlated Patterns in Neural Networks., , , , , , and . ICONIP (3), volume 11303 of Lecture Notes in Computer Science, page 506-514. Springer, (2018)