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Counting objects with biologically inspired regulatory-feedback networks., , and . IJCNN, page 36-40. IEEE Computer Society, (2009)Input Feedback Networks: Classification and Inference Based on Network Structure., and . AGI, volume 171 of Frontiers in Artificial Intelligence and Applications, page 15-26. IOS Press, (2008)Supervised Generative Reconstruction: An Efficient Way To Flexibly Store and Recognize Patterns. CoRR, (2011)Input shunt networks.. Neurocomputing, (2002)Using non-oscillatory dynamics to disambiguate simultaneous patterns.. IJCNN, page 41-48. IEEE Computer Society, (2009)Evaluating the Contribution of Top-Down Feedback and Post-Learning Reconstruction., and . BICA, volume 233 of Frontiers in Artificial Intelligence and Applications, page 9-15. IOS Press, (2011)What AI, Neuroscience, and Cognitive Science Can Learn from Each Other: An Embedded Perspective.. Cogn. Comput., 16 (5): 2428-2436 (September 2024)A Localist Paradigm for Big Data.. INNS Conference on Big Data, volume 53 of Procedia Computer Science, page 356-364. Elsevier, (2015)Shedding weights: More with less., , and . IJCNN, page 3020-3027. IEEE, (2008)Editorial: What AI and Neuroscience Can Learn from Each Other - Open Problems in Models and Theories., , , , and . Cogn. Comput., 16 (5): 2331-2333 (September 2024)