Universality and individuality in neural dynamics across large populations of recurrent networks

19 July 2019

Papers citing "Universality and individuality in neural dynamics across large populations of recurrent networks"

22 / 22 papers shown

Title
Meta-Dynamical State Space Models for Integrative Neural Data Analysis Ayesha Vermani Josue Nassar Hyungju Jeon Matthew Dowling Il Memming Park 31 1 0 07 Oct 2024
Formation of Representations in Neural Networks Liu Ziyin Isaac Chuang Tomer Galanti T. Poggio 37 4 0 03 Oct 2024
Back to the Continuous Attractor Ábel Ságodi Guillermo Martín-Sánchez Piotr Sokól Il Memming Park 27 2 0 31 Jul 2024
When predict can also explain: few-shot prediction to select better neural latents Kabir Dabholkar Omri Barak BDL 55 0 0 23 May 2024
From Data-Fitting to Discovery: Interpreting the Neural Dynamics of Motor Control through Reinforcement Learning Eugene R. Rush Kaushik Jayaram J. Humbert 18 1 0 18 May 2023
How good are variational autoencoders at transfer learning? Lisa Bonheme M. Grzes OOD DRL 23 2 0 21 Apr 2023
Analyzing Populations of Neural Networks via Dynamical Model Embedding Jordan S. Cotler Kai Sheng Tai Felipe Hernández Blake Elias David Sussillo 9 4 0 27 Feb 2023
Sources of Richness and Ineffability for Phenomenally Conscious States Xu Ji Eric Elmoznino George Deane Axel Constant G. Dumas Guillaume Lajoie Jonathan Simon Yoshua Bengio 26 11 0 13 Feb 2023
Representational dissimilarity metric spaces for stochastic neural networks Lyndon Duong Jingyang Zhou Josue Nassar Jules Berman Jeroen Olieslagers Alex H. Williams 24 19 0 21 Nov 2022
Tractable Dendritic RNNs for Reconstructing Nonlinear Dynamical Systems Manuela Brenner Florian Hess Jonas M. Mikhaeil Leonard Bereska Zahra Monfared Po-Chen Kuo Daniel Durstewitz AI4CE 37 29 0 06 Jul 2022
How do Variational Autoencoders Learn? Insights from Representational Similarity Lisa Bonheme M. Grzes CoGe SSL DRL 27 10 0 17 May 2022
On the Origins of the Block Structure Phenomenon in Neural Network Representations Thao Nguyen M. Raghu Simon Kornblith 25 14 0 15 Feb 2022
Testing the Tools of Systems Neuroscience on Artificial Neural Networks Grace W. Lindsay 16 4 0 14 Feb 2022
Gaussian RBF Centered Kernel Alignment (CKA) in the Large Bandwidth Limit S. A. Alvarez 11 7 0 17 Dec 2021
Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems Jimmy T.H. Smith Scott W. Linderman David Sussillo 10 28 0 01 Nov 2021
Do Vision Transformers See Like Convolutional Neural Networks? M. Raghu Thomas Unterthiner Simon Kornblith Chiyuan Zhang Alexey Dosovitskiy ViT 61 924 0 19 Aug 2021
Reverse engineering learned optimizers reveals known and novel mechanisms Niru Maheswaranathan David Sussillo Luke Metz Ruoxi Sun Jascha Narain Sohl-Dickstein 16 21 0 04 Nov 2020
Meta-trained agents implement Bayes-optimal agents Vladimir Mikulik Grégoire Delétang Tom McGrath Tim Genewein Miljan Martic Shane Legg Pedro A. Ortega OOD FedML 35 40 0 21 Oct 2020
Unfolding recurrence by Green's functions for optimized reservoir computing Sandra Nestler Christian Keup David Dahmen M. Gilson Holger Rauhut M. Helias 11 4 0 13 Oct 2020
How recurrent networks implement contextual processing in sentiment analysis Niru Maheswaranathan David Sussillo 22 22 0 17 Apr 2020
From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction Hidenori Tanaka Aran Nayebi Niru Maheswaranathan Lane T. McIntosh S. Baccus Surya Ganguli FAtt 14 61 0 12 Dec 2019
Neural Architecture Search with Reinforcement Learning Barret Zoph Quoc V. Le 271 5,329 0 05 Nov 2016