Structured cerebellar connectivity supports resilient pattern separation

Tri M. Nguyen, Logan A. Thomas, Jeff L. Rhoades, Ilaria Ricchi, Xintong Cindy Yuan, Arlo Sheridan, David G. C. Hildebrand, Jan Funke, Wade G. Regehr and Wei-Chung Allen Lee ()
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Tri M. Nguyen: Department of Neurobiology, Harvard Medical School
Logan A. Thomas: Department of Neurobiology, Harvard Medical School
Jeff L. Rhoades: Department of Neurobiology, Harvard Medical School
Ilaria Ricchi: Department of Neurobiology, Harvard Medical School
Xintong Cindy Yuan: Department of Neurobiology, Harvard Medical School
Arlo Sheridan: HHMI Janelia Research Campus
David G. C. Hildebrand: Department of Neurobiology, Harvard Medical School
Jan Funke: HHMI Janelia Research Campus
Wade G. Regehr: Department of Neurobiology, Harvard Medical School
Wei-Chung Allen Lee: Boston Children’s Hospital, Harvard Medical School

Nature, 2023, vol. 613, issue 7944, 543-549

Abstract: Abstract The cerebellum is thought to help detect and correct errors between intended and executed commands1,2 and is critical for social behaviours, cognition and emotion3–6. Computations for motor control must be performed quickly to correct errors in real time and should be sensitive to small differences between patterns for fine error correction while being resilient to noise7. Influential theories of cerebellar information processing have largely assumed random network connectivity, which increases the encoding capacity of the network’s first layer8–13. However, maximizing encoding capacity reduces the resilience to noise7. To understand how neuronal circuits address this fundamental trade-off, we mapped the feedforward connectivity in the mouse cerebellar cortex using automated large-scale transmission electron microscopy and convolutional neural network-based image segmentation. We found that both the input and output layers of the circuit exhibit redundant and selective connectivity motifs, which contrast with prevailing models. Numerical simulations suggest that these redundant, non-random connectivity motifs increase the resilience to noise at a negligible cost to the overall encoding capacity. This work reveals how neuronal network structure can support a trade-off between encoding capacity and redundancy, unveiling principles of biological network architecture with implications for the design of artificial neural networks.

Date: 2023
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DOI: 10.1038/s41586-022-05471-w

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