小脑结构连接支持弹性的模式分离，这为人工神经网络的设计提供了新的思路

中文摘要

小脑被有助于检测和纠正预期命令和执行命令之间的误差，并对社会行为、认知和情绪至关重要。小脑必须快速执行运动控制计算，以实时纠正误差，并且需对模式之间的小差异敏感，以便进行精细误差纠正，同时对噪声具有抵抗力。小脑信息处理的影响理论在很大程度上假设了随机网络的连接模式，这增加了网络第一层的编码能力。然而，最大化编码能力会降低系统对噪声的抵抗力。为了了解小脑神经元环路如何解决这一基本权衡问题，科学家使用自动大规模透射电子显微镜和卷积神经网络图像分割绘制了小鼠小脑皮层的前馈连接。他们发现，神经网络的输入和输出层都表现出冗余和选择性的连接模块，这与主流模型不同。数值模拟表明，这些冗余的、非随机的连接模块增加了对噪声的抵抗力，而对整体编码容量的成本消耗可以忽略不计。这项工作揭示了神经元网络结构如何支持编码能力和冗余之间的权衡，揭示了生物网络架构的原理，并对人工神经网络的设计产生了影响。

英文摘要

The cerebellum is thought to help detect and correct errors between intended and executed commands and is critical for social behaviours, cognition and emotion. 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 noise. Influential theories of cerebellar information processing have largely assumed random network connectivity, which increases the encoding capacity of the network's first layer. However, maximizing encoding capacity reduces the resilience to noise. 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.