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Gradient-based learning drives robust representations in recurrent neural networks by balancing compression and expansion., , , , and . Nat. Mach. Intell., 4 (6): 564-573 (2022)A simple connection from loss flatness to compressed representations in neural networks., , and . CoRR, (2023)A scale-dependent measure of system dimensionality., , , , and . Patterns, 3 (8): 100555 (2022)Dimensionality in recurrent spiking networks: Global trends in activity and local origins in connectivity., , , and . PLoS Comput. Biol., (2019)Dimensionality compression and expansion in Deep Neural Networks., , , , , and . CoRR, (2019)Memory States and Transitions between Them in Attractor Neural Networks., , and . Neural Comput., 29 (10): 2684-2711 (2017)Single Circuit in V1 Capable of Switching Contexts During Movement Using an Inhibitory Population as a Switch., , , , and . Neural Comput., 34 (3): 541-594 (2022)Untangling network information flow.. Nat. Comput. Sci., 2 (8): 475-476 (2022)Neural Network Model of Memory Retrieval., , , and . Frontiers Comput. Neurosci., (2015)Autoencoder networks extract latent variables and encode these variables in their connectomes., , , , and . Neural Networks, (2021)