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A State Space Approach for Piecewise-Linear Recurrent Neural Networks
  for Reconstructing Nonlinear Dynamics from Neural Measurements

A State Space Approach for Piecewise-Linear Recurrent Neural Networks for Reconstructing Nonlinear Dynamics from Neural Measurements

23 December 2016
Daniel Durstewitz
ArXiv (abs)PDFHTML

Papers citing "A State Space Approach for Piecewise-Linear Recurrent Neural Networks for Reconstructing Nonlinear Dynamics from Neural Measurements"

2 / 2 papers shown
Title
Inferring stochastic low-rank recurrent neural networks from neural data
Inferring stochastic low-rank recurrent neural networks from neural data
Matthijs Pals
A Erdem Sağtekin
Felix Pei
Manuel Gloeckler
Jakob H Macke
501
7
0
24 Jun 2024
An overview of gradient descent optimization algorithms
An overview of gradient descent optimization algorithms
Sebastian Ruder
ODL
204
6,188
0
15 Sep 2016
1