# Hierarchical Temporal Representation in Linear Reservoir Computing

arXiv: https://arxiv.org/abs/1705.05782 · HTML: https://mlsanity.com/paper/1705.05782/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2017.

## Built on by (most cited first, 1 of 1)

- [Reservoir Computing with Magnetic Thin Films](https://mlsanity.com/paper/2101.12700/) (2021)

## Abstract (arXiv)

Recently, studies on deep Reservoir Computing (RC) highlighted the role of layering in deep recurrent neural networks (RNNs). In this paper, the use of linear recurrent units allows us to bring more evidence on the intrinsic hierarchical temporal representation in deep RNNs through frequency analysis applied to the state signals. The potentiality of our approach is assessed on the class of Multiple Superimposed Oscillator tasks. Furthermore, our investigation provides useful insights to open a discussion on the main aspects that characterize the deep learning framework in the temporal domain.