University of Cambridge, Department of Zoology, Cambridge, UK.
MRC Laboratory of Molecular Biology, Neurobiology Division, Cambridge, UK.
Science. 2023 Mar 10;379(6636):eadd9330. doi: 10.1126/science.add9330.
Brains contain networks of interconnected neurons and so knowing the network architecture is essential for understanding brain function. We therefore mapped the synaptic-resolution connectome of an entire insect brain ( larva) with rich behavior, including learning, value computation, and action selection, comprising 3016 neurons and 548,000 synapses. We characterized neuron types, hubs, feedforward and feedback pathways, as well as cross-hemisphere and brain-nerve cord interactions. We found pervasive multisensory and interhemispheric integration, highly recurrent architecture, abundant feedback from descending neurons, and multiple novel circuit motifs. The brain's most recurrent circuits comprised the input and output neurons of the learning center. Some structural features, including multilayer shortcuts and nested recurrent loops, resembled state-of-the-art deep learning architectures. The identified brain architecture provides a basis for future experimental and theoretical studies of neural circuits.
大脑包含相互连接的神经元网络,因此了解网络结构对于理解大脑功能至关重要。我们因此绘制了具有丰富行为(包括学习、价值计算和动作选择)的整个昆虫大脑(幼虫)的突触分辨率连接组图谱,其中包含 3016 个神经元和 548000 个突触。我们对神经元类型、中枢、前馈和反馈途径以及半球间和脑神经索相互作用进行了描述。我们发现普遍存在的多感觉和半球间整合、高度重复的结构、来自下行神经元的大量反馈以及多种新的电路模块。大脑中最具重复性的电路包括学习中心的输入和输出神经元。一些结构特征,包括多层捷径和嵌套的循环回路,类似于最先进的深度学习架构。所确定的大脑结构为未来对神经回路的实验和理论研究提供了基础。