# A Versatile Hub Model For Efficient Information Propagation And Feature Selection

arXiv: https://arxiv.org/abs/2307.02398 · HTML: https://mlsanity.com/paper/2307.02398/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 49 references that are in the map.
- First posted on arXiv 5 Jul 2023.

## Builds on (most cited first, 8 of 8)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Emergence of grid-like representations by training recurrent neural networks to perform spatial localization](https://mlsanity.com/paper/1803.07770/) (2018)
- [Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks](https://mlsanity.com/paper/1703.10371/) (2017)
- [Artificial neural networks for neuroscientists: A primer](https://mlsanity.com/paper/2006.01001/) (2020)
- [Recurrent Network Models Of Sequence Generation And Memory](https://mlsanity.com/paper/1603.04687/) (2016)
- [Optimal modularity and memory capacity of neural reservoirs](https://mlsanity.com/paper/1706.06511/) (2017)
- [A Comparative Study of Reservoir Computing for Temporal Signal Processing](https://mlsanity.com/paper/1401.2224/) (2014)
- [A mechanistic model of connector hubs, modularity, and cognition](https://mlsanity.com/paper/1803.08109/) (2018)

## Abstract (arXiv)

Hub structure, characterized by a few highly interconnected nodes surrounded by a larger number of nodes with fewer connections, is a prominent topological feature of biological brains, contributing to efficient information transfer and cognitive processing across various species. In this paper, a mathematical model of hub structure is presented. The proposed method is versatile and can be broadly applied to both computational neuroscience and Recurrent Neural Networks (RNNs) research. We employ the Echo State Network (ESN) as a means to investigate the mechanistic underpinnings of hub structures. Our findings demonstrate a substantial enhancement in performance upon incorporating the hub structure. Through comprehensive mechanistic analyses, we show that the hub structure improves model performance by facilitating efficient information processing and better feature extractions.