PDF(Pubmed)
Abstract：
We have an example of a synergetic effect between neuroscience and connectome via artificial intelligence. The invention of Neocognitron, a machine learning algorithm, was inspired by the visual cortical circuitry for complex cells to be made by combinations of simple cells, which uses a hierarchical convolutional neural network (CNN). The CNN machine learning algorithm is powerful in classifying neuron borderlines on electron micrograph images for automatized connectomic analysis. CNN is also useful as a functional framework to analyze the neurocircuitry of the visual system. The visual system encodes visual patterns in the retina and decodes them in the corresponding cortical areas. The knowledge of evolutionarily chosen mechanisms in retinas may help the innovation of new algorithms. Since over a half-century ago, a classical style of serial section transmission electron microscopy has vastly contributed to cell biology. It is still useful to comprehensively analyze the small area of retinal neurocircuitry that is rich in natural intelligence of pattern recognition. I discuss the perspective of our study on the primary rod signal pathway in mouse and macaque retinas with special reference to electrical synapses. Photon detection under the scotopic condition needs absolute sensitivity but no intricate pattern recognition. This extreme case is regarded as the most simplified pattern recognition of the input with no autocorrelation. A comparative study of mouse and macaque retinas, where exists the 7-fold difference in linear size, may give us the underlying principle with quantitative verification of their adaptational designs of neurocircuitry.
摘要：
我们有一个通过人工智能在神经科学和连接体之间产生协同作用的例子。Neocognitron的发明，机器学习算法，灵感来自视觉皮层电路，由简单细胞的组合制成复杂细胞，它使用分层卷积神经网络(CNN)。CNN机器学习算法在对电子显微照片图像上的神经元边界线进行分类以进行自动化的连接组学分析方面是强大的。CNN作为分析视觉系统神经电路的功能框架也很有用。视觉系统对视网膜中的视觉模式进行编码，并在相应的皮质区域对其进行解码。视网膜中进化选择机制的知识可能有助于新算法的创新。半个多世纪前，一种经典的连续截面透射电子显微镜对细胞生物学做出了巨大贡献。综合分析富含模式识别自然智能的视网膜神经回路的小区域仍然有用。我讨论了我们对小鼠和猕猴视网膜中主要杆信号通路的研究观点，特别是电突触。暗空条件下的光子检测需要绝对的灵敏度，但没有复杂的模式识别。这种极端情况被认为是没有自相关的输入的最简化的模式识别。小鼠和猕猴视网膜的比较研究，其中存在线性尺寸的7倍差异，可以为我们提供基本原理，并对其神经电路的适应性设计进行定量验证。
