Structured connectivity in the cerebellum enables noise-resilient pattern separation

Abstract

SummaryThe cerebellum is thought to detect and correct errors between intended and executed commands1–3 and is critical for social behaviors, cognition and emotion4–6. 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 resiliency to noise7. To understand how neuronal circuits address this fundamental tradeoff, we mapped the feedforward connectivity in the mouse cerebellar cortex using automated large-scale transmission electron microscopy (EM) 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 these redundant, non-random connectivity motifs increase discriminability of similar input patterns at a minimal cost to the network’s overall encoding capacity. This work reveals how neuronal network structure can balance encoding capacity and redundancy, unveiling principles of biological network architecture with implications for artificial neural network design.

Automated Synapse Detection Method for Cerebellar Connectomics

“…The investigations into the microcircuits in “partial connectomes” from diverse brain regions and species yield crucial knowledge’s of the fundamental principles of neural connectivity and function ( Takemura et al, 2013 ; Kim et al, 2014 ; Ohyama et al, 2015 ). For the cerebellum, the development of the climbing fiber in the infant mouse cerebellum, a new type of Purkinje cell layer interneuron, and the connectivity between granule cells and Purkinje cells have been studied by 3D EM image analyses ( Wilson et al, 2019 ; Nguyen et al, 2021 ; Osorno et al, 2021 ). Nonetheless, the functional circuit mechanisms of the motor control and learning of the cerebellum are still largely in veil.…”