🪢 Lastly, we find that all these findings arise in unison in seRNNs, highlighting that this diverse set of seemingly unrelated brain features can result from a shared underlying optimisation process!

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⚡️ While the connectome of seRNNs is modular & the neurons are spatially organised by their code, we find that seRNNs still show an information-rich mixed selective code, that also is commonly found in frontal cortex. seRNNs achieve this using very efficient neuronal activations!

[seRNN Thread 8/13]

📡 To use our model system to jointly study structural and functional phenomena, we also analyse the code used by neurons in the network. Similar to brains, we find that information communication & processing in seRNNs is spatially structured!

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🕸️ Looking at the network structure more specifically, we see that seRNNs develop a highly modular connectome with strong small-world characteristics. These features are commonly found in biology and thought to guide efficient information processing!

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🧠 Even in the simplest observation we already see that an isolated seRNN develops connection patterns commonly observed in brains – here we see a relationship between connection strength and spatial distance and clustering of connections in the connectome!

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🔧 All our tests of spatially embedded RNNs (we call them seRNNs) are made in comparison with standard L1 regularised models, matched for sparsity. We train a large batch of networks with varying regularisation strengths.

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🌐 Starting from standard RNNs, optimised for a working memory task, we create a spatial embedding by regularising them by distance in 3D Euclidean space & nudge them to prioritise highly communicative connections (Topological, lots of theory on this in paper!)

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