Opening the black box: low-dimensional dynamics in high-dimensional recurrent neural networks

D Sussillo, O Barak - Neural computation, 2013 - direct.mit.edu
Recurrent neural networks (RNNs) are useful tools for learning nonlinear relationships
between time-varying inputs and outputs with complex temporal dependencies. Recently …
被引用次数：530 相关文章
[PDF] technion.ac.il

[PDF][PDF] Opening the Black Box: Low-dimensional dynamics in high-dimensional recurrent neural networks

D Sussillo, O Barak - barak.net.technion.ac.il
Recurrent neural networks (RNNs) are useful tools for learning nonlinear relationships
between time-varying inputs and outputs with complex temporal dependencies. Recently …

Opening the black box: Low-dimensional dynamics in high-dimensional recurrent neural networks

D Sussillo, O Barak - Neural Computation, 2013 - dl.acm.org
Recurrent neural networks RNNs are useful tools for learning nonlinear relationships
between time-varying inputs and outputs with complex temporal dependencies. Recently …
[PDF] fsu.edu

[PDF][PDF] Opening the Black Box: Low-Dimensional Dynamics in High-Dimensional Recurrent Neural Networks

D Sussillo, O Barak - math.fsu.edu
Recurrent neural networks (RNNs) are useful tools for learning nonlinear relationships
between time-varying inputs and outputs with complex temporal dependencies. Recently …
[PDF] github.io

[PDF][PDF] Opening the Black Box: Low-Dimensional Dynamics in High-Dimensional Recurrent Neural Networks

D Sussillo, O Barak - compneurojc.github.io
Recurrent neural networks (RNNs) are useful tools for learning nonlinear relationships
between time-varying inputs and outputs with complex temporal dependencies. Recently …

Opening the black box: low-dimensional dynamics in high-dimensional recurrent neural networks

D Sussillo, O Barak - Neural computation, 2013 - pubmed.ncbi.nlm.nih.gov
Recurrent neural networks (RNNs) are useful tools for learning nonlinear relationships
between time-varying inputs and outputs with complex temporal dependencies. Recently …

Opening the Black Box: Low-Dimensional Dynamics in High-Dimensional Recurrent Neural Networks

D Sussillo, O Barak - Neural Computation, 2013 - ieeexplore.ieee.org
Recurrent neural networks (RNNs) are useful tools for learning nonlinear relationships
between time-varying inputs and outputs with complex temporal dependencies. Recently …
[PDF] psu.edu

[PDF][PDF] Opening the Black Box: Low-dimensional dynamics in high-dimensional recurrent neural networks

D Sussillo, O Barak - Citeseer
Recurrent neural networks (RNNs) are useful tools for learning nonlinear relationships
between time-varying inputs and outputs with complex temporal dependencies. Recently …
[PDF] archive.org

[PDF][PDF] Opening the Black Box: Low-dimensional dynamics in high-dimensional recurrent neural networks

D Sussillo, O Barak - scholar.archive.org
Recurrent neural networks (RNNs) are useful tools for learning nonlinear relationships
between time-varying inputs and outputs with complex temporal dependencies. Recently …

Opening the Black Box: Low-Dimensional Dynamics in High-Dimensional Recurrent Neural Networks

D Sussillo, O Barak - Neural Computation, 2013 - cir.nii.ac.jp
抄録< jats: p> Recurrent neural networks (RNNs) are useful tools for learning nonlinear
relationships between time-varying inputs and outputs with complex temporal …