Spatially embedded recurrent neural networks reveal widespread links between structural and functional neuroscience findings | Nature Machine Intelligence

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Advertisement Nature Machine Intelligence volume  5,  pages 1369–1381 (2023)Cite this article 27k Accesses 2 Citations 342 Altmetric Metrics details A preprint version of the article is available at bioRxiv. Brain networks exist within the confines of resource limitations. As a result, a brain network must overcome the metabolic costs of growing and sustaining the network within its physical space, while simultaneously implementing its required information processing. Here, to observe the effect of these processes, we introduce the spatially embedded recurrent neural network (seRNN). seRNNs learn basic task-related infere

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