A Versatile Hub Model For Efficient Information Propagation And Feature Selection
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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.
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References · 8
- 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,898
- 2018Emergence of grid-like representations by training recurrent neural networks to perform spatial localizationcited by 52
- 2017Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networkscited by 29
- 2020Artificial neural networks for neuroscientists: A primercited by 26
- 2016Recurrent Network Models Of Sequence Generation And Memorycited by 21
- 2017Optimal modularity and memory capacity of neural reservoirscited by 8
- 2014A Comparative Study of Reservoir Computing for Temporal Signal Processingcited by 6
- 2018A mechanistic model of connector hubs, modularity, and cognitioncited by 2
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