Wang Peter Y, Sun Yi, Axel Richard, Abbott L F, Yang Guangyu Robert
The Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA.
Department of Mathematics, Columbia University, New York, NY 10027, USA.
Neuron. 2021 Dec 1;109(23):3879-3892.e5. doi: 10.1016/j.neuron.2021.09.010. Epub 2021 Oct 7.
The convergent evolution of the fly and mouse olfactory system led us to ask whether the anatomic connectivity and functional logic of olfactory circuits would evolve in artificial neural networks trained to perform olfactory tasks. Artificial networks trained to classify odor identity recapitulate the connectivity inherent in the olfactory system. Input units are driven by a single receptor type, and units driven by the same receptor converge to form a glomerulus. Glomeruli exhibit sparse, unstructured connectivity onto a larger expansion layer of Kenyon cells. When trained to both classify odor identity and to impart innate valence onto odors, the network develops independent pathways for identity and valence classification. Thus, the defining features of fly and mouse olfactory systems also evolved in artificial neural networks trained to perform olfactory tasks. This implies that convergent evolution reflects an underlying logic rather than shared developmental principles.
苍蝇和小鼠嗅觉系统的趋同进化促使我们思考，在经过训练以执行嗅觉任务的人工神经网络中，嗅觉回路的解剖学连接性和功能逻辑是否会发生进化。经过训练用于气味识别分类的人工网络再现了嗅觉系统固有的连接性。输入单元由单一受体类型驱动，由相同受体驱动的单元汇聚形成一个小球。小球在更大的肯扬细胞扩展层上呈现出稀疏、无结构的连接。当经过训练既能进行气味识别分类又能赋予气味先天效价时，该网络会为识别和效价分类发展出独立的通路。因此，苍蝇和小鼠嗅觉系统的决定性特征在经过训练以执行嗅觉任务的人工神经网络中也得到了进化。这意味着趋同进化反映的是一种潜在逻辑，而非共同的发育原则。
