| Predictive learning as a network mechanism for extracting low-dimensional latent space representations S Recanatesi, M Farrell, G Lajoie, S Deneve, M Rigotti, E Shea-Brown Nature communications 12 (1), 1-13, 2021 | 94* | 2021 |
| Gradient-based learning drives robust representations in recurrent neural networks by balancing compression and expansion M Farrell, S Recanatesi, T Moore, G Lajoie, E Shea-Brown Nature Machine Intelligence 4 (6), 564-573, 2022 | 84* | 2022 |
| Dimensionality compression and expansion in deep neural networks S Recanatesi, M Farrell, M Advani, T Moore, G Lajoie, E Shea-Brown arXiv preprint arXiv:1906.00443, 2019 | 82 | 2019 |
| Dimensionality in recurrent spiking networks: Global trends in activity and local origins in connectivity S Recanatesi, GK Ocker, MA Buice, E Shea-Brown PLoS computational biology 15 (7), e1006446, 2019 | 78 | 2019 |
| Metastable attractors explain the variable timing of stable behavioral action sequences S Recanatesi, U Pereira-Obilinovic, M Murakami, Z Mainen, L Mazzucato Neuron 110 (1), 139-153. e9, 2022 | 67 | 2022 |
| Neural network model of memory retrieval S Recanatesi, M Katkov, S Romani, M Tsodyks Frontiers in computational neuroscience 9, 149, 2015 | 55 | 2015 |
| Strong coupling and local control of dimensionality across brain areas D Dahmen, S Recanatesi, GK Ocker, X Jia, M Helias, E Shea-Brown Biorxiv, 2020.11. 02.365072, 2020 | 33 | 2020 |
| A scale-dependent measure of system dimensionality S Recanatesi, S Bradde, V Balasubramanian, NA Steinmetz, ... Patterns 3 (8), 2022 | 24 | 2022 |
| Memory states and transitions between them in attractor neural networks S Recanatesi, M Katkov, M Tsodyks Neural computation 29 (10), 2684-2711, 2017 | 18 | 2017 |
| Emergence of hierarchical organization in memory for random material M Naim, M Katkov, S Recanatesi, M Tsodyks Scientific reports 9 (1), 10448, 2019 | 16 | 2019 |
| From lazy to rich to exclusive task representations in neural networks and neural codes M Farrell, S Recanatesi, E Shea-Brown Current opinion in neurobiology 83, 102780, 2023 | 11 | 2023 |
| Autoencoder networks extract latent variables and encode these variables in their connectomes M Farrell, S Recanatesi, RC Reid, S Mihalas, E Shea-Brown Neural Networks 141, 330-343, 2021 | 9 | 2021 |
| Signatures of low-dimensional neural predictive manifolds S Recanatesi, M Farrell, G Lajoie, S Deneve, M Rigotti, E Shea-Brown Cosyne Abstracts, 2019 | 3 | 2019 |
| A non-Hebbian code for episodic memory R Pang, S Recanatesi Science Advances 11 (8), eado4112, 2025 | 1 | 2025 |
| Single Circuit in V1 Capable of Switching Contexts During Movement Using an Inhibitory Population as a Switch D Voina, S Recanatesi, B Hu, E Shea-Brown, S Mihalas Neural Computation 34 (3), 541-594, 2022 | 1 | 2022 |
| j/ψ→ µµ sidebands subtraction S Recanatesi, S Bolognesi, M De Mattia Universita degli studi di Pisa Summer Student CERN 2010, 2010 | 1 | 2010 |
| Identifying the impact of local connectivity patterns on dynamics in excitatory-inhibitory networks Y Shao, D Dahmen, S Recanatesi, E Shea-Brown, S Ostojic arXiv preprint arXiv:2411.06802, 2024 | | 2024 |
| Learning to Embed Distributions via Maximum Kernel Entropy O Kachaiev, S Recanatesi arXiv preprint arXiv:2408.00549, 2024 | | 2024 |
| A simple connection from loss flatness to compressed representations in neural networks S Chen, S Recanatesi, E Shea-Brown arXiv preprint arXiv:2310.01770, 2023 | | 2023 |
| Untangling network information flow S Recanatesi Nature Computational Science 2 (8), 475-476, 2022 | | 2022 |
Eric Shea-BrownApplied Mathematics, University of WashingtonPatvirtintas el. paštas uw.edu