[R] Spatially embedded recurrent neural networks reveal widespread links between structural and functional neuroscience findings

**Paper**: [https://www.nature.com/articles/s42256-023-00748-9](https://www.nature.com/articles/s42256-023-00748-9)

**Project page and related work**: [https://www.jachterberg.com/seRNN](https://www.jachterberg.com/sernn)

**Code (1)**: [https://codeocean.com/capsule/2879348/tree/v2](https://codeocean.com/capsule/2879348/tree/v2)

**Code (2)**: [https://github.com/8erberg/spatially-embedded-RNN](https://github.com/8erberg/spatially-embedded-rnn)

**Pre-print version on bioRxiv**: [https://www.biorxiv.org/content/10.1101/2022.11.17.516914v1](https://www.biorxiv.org/content/10.1101/2022.11.17.516914v1)

**University of Cambridge press release**: [https://www.cam.ac.uk/research/news/ai-system-self-organises-to-develop-features-of-brains-of-complex-organisms](https://www.cam.ac.uk/research/news/ai-system-self-organises-to-develop-features-of-brains-of-complex-organisms)

**Abstract**:

>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**). …

abstract basic brain costs embedded inferences information learn limitations machinelearning network networks neural network observe processes processing recurrent neural network space