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Poster Session B: Wednesday, August 13, 1:00 – 4:00 pm, de Brug & E‑Hall

Cortically-Embedded RNNs for integration of cortex-wide neuroscience data into recurrent neural network models

Eva Sevenster¹, Aswathi Thrivikraman², Guy Davies², Ulysse Klatzmann, Dabal Pedamonti³, Sean Froudist-Walsh²; ¹University of Amsterdam, ²University of Bristol, ³University of Oxford

Presenter: Aswathi Thrivikraman

Current state-of-the-art recurrent neural network models can capture complex neural dynamics during the performance of higher cognitive tasks. However, they largely overlook anatomy, limiting their ability to make species-specific and anatomically-precise predictions for experimentalists. Cortex-wide dynamical models increasingly integrate anatomical features including connectivity, dendritic spines and receptors, but are incapable of solving most cognitive tasks. Here, we introduce Cortically-Embedded Recurrent Neural Networks (CERNNs), which embed artificial neural networks into a species-specific cortical space, facilitating direct comparisons to empirical neuroscience data across the entire cortex and allowing the incorporation of biologically-inspired constraints. We trained CERNNs, with macaque or human anatomy, to perform multiple cognitive tasks (e.g. working memory, response inhibition). CERNNs were trained with different architectural constraints and biologically-inspired loss functions. We evaluated CERNNs on (1) task performance, (2) alignment of connectivity with the macaque mesoscopic connectome, and (3) task-evoked activity patterns. The best performing models penalized both long-distance connections and deviations from empirical spine density. These results suggest that distributed cognitive networks may arise naturally as the brain attempts to solve complex tasks under wiring constraints with systematic gradients of single neuron properties. More broadly, CERNNs constitute a framework by which artificial neural networks can be integrated with cortex-wide neuroanatomy, physiology and imaging data to produce anatomically-specific testable hypotheses across species.

Topic Area: Brain Networks & Neural Dynamics

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