# Guided Self-Organization of Input-Driven Recurrent Neural Networks

@article{Obst2013GuidedSO, title={Guided Self-Organization of Input-Driven Recurrent Neural Networks}, author={Oliver Obst and Joschka Boedecker}, journal={ArXiv}, year={2013}, volume={abs/1309.1524} }

To understand the world around us, our brains solve a variety of tasks. One of the crucial functions of a brain is to make predictions of what will happen next, or in the near future. This ability helps us to anticipate upcoming events and plan our reactions to them in advance. To make these predictions, past information needs to be stored, transformed or used otherwise. How exactly the brain achieves this information processing is far from clear and under heavy investigation. To guide this…

## 12 Citations

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

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The hypothesis that fixed points, both stable and unstable, and the linearized dynamics around them, can reveal crucial aspects of how RNNs implement their computations is explored.

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This paper takes advantage of a recently developed nonparametric estimator of the Fisher information matrix and provides a method to determine the critical region of echo state networks (ESNs), a particular class of recurrent networks.

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The expansive research field of computational intelligence combines various nature-inspired computational methodologies and draws on rigorous quantitative approaches across computer science, mathematics, physics, and life sciences, and some of its research topics are traditional to computational intelligence.

### Critical echo state network dynamics by means of Fisher information maximization

- Computer Science2017 International Joint Conference on Neural Networks (IJCNN)
- 2017

This paper shows how to identify optimal ESN hyperparameters by relying only on the Fisher information matrix (FIM) estimated from the activations of hidden neurons, and adopts a recently proposed non-parametric FIM estimator.

### University of Birmingham Dynamical systems as temporal feature spaces

- Computer Science
- 2020

A framework for rigorous analysis of feature representations imposed by dynamic kernels and it is demonstrated that for dynamic kernel associated with cycle reservoir topology, the kernel richness undergoes a phase transition close to the edge of stability.

### Dynamical Systems as Temporal Feature Spaces

- Computer ScienceJ. Mach. Learn. Res.
- 2020

A framework for rigorous analysis of feature representations imposed by dynamic kernels and it is demonstrated that for dynamic kernel associated with cycle reservoir topology, the kernel richness undergoes a phase transition close to the edge of stability.

### Input-Anticipating Critical Reservoirs Show Power Law Forgetting of Unexpected Input Events

- PhysicsNeural Computation
- 2015

This letter investigates under which circumstances echo state networks can show a power law forgetting, which means traces of earlier events can be found in the reservoir for very long time spans.

### L G ] 2 4 D ec 2 01 9 Dynamical Systems as Temporal Feature Spaces

- Computer Science
- 2019

This work quantifies richness of feature representations imposed by dynamic kernels and demonstrates that for dynamic kernel associated with cycle reservoir topology, the kernel richness undergoes a phase transition close to the edge of stability.

### Asymptotic Fisher memory of randomized linear symmetric Echo State Networks

- MathematicsNeurocomputing
- 2018

### University of Birmingham Asymptotic Fisher Memory of Randomized Linear Symmetric Echo State Networks

- Mathematics
- 2018

We study asymptotic properties of Fisher memory of linear Echo State Networks with randomized symmetric state space coupling. In particular, two reservoir constructions are considered: (1) More…

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