仿生脑初级视皮层超柱结构研究
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摘要
初级视皮层(V1)是视觉信息在传递给脑皮层时的第一站,信息在初级视皮层上的处理对后续皮层对外界的识别和认识具有非常重要的意义。超柱结构是普遍认为的初级视皮层上的基本结构单元,对超柱的研究是了解和模拟初级视皮层的关键过程。
本文根据生理学上的研究,总结分析了初级视皮层与外膝体(LGN)的神经连接、本层内亚层间的神经传递关系、与V2的神经连接,以及V1内各功能柱的结构关系。LGN对V1的信息输入是V1响应的根源,其它皮层的响应输入只能调节这些信息的响应情况,V1第6亚层能够向LGN发出反馈信号,这些信号能够调节LGN上神经元的响应值,从而使这些神经元具有一定的方位特性。V1内部的神经连接根据信息的顺向传递可以分为三层,依次为最简单细胞层(4C层)、简单细胞层(3B和4B层)、复杂细胞层(2/3A层),起调节作用的亚层有两个,分别是第5层和第6层,这两个亚层对V1内部回路的形成起到了重要作用,这种反馈调节了信息在V1内部的处理。初级视皮层对V2的输入根据不同的功能是有所分组的,不同的功能信息输入到了V2不同的区域内,这样有利于视觉信息的提取和分别处理。V1内的功能柱有眼优势柱、方位柱、颜色柱等,这些功能柱位置之间和功能上都有联系。有能分辨方位的颜色敏感神经元,也有同时对方位、运动方向敏感的双眼细胞。
本文对神经传递的网络结构进行了设计,简化了一些神经传递中次要的连接关系。V1内部的神经传递网络分成了两个回路结构,一个是底层的4C、3B(4B)和第6层,一个是表层的3B、2/3A和第5层。底层回路中,第6层是反馈最重要的一环,它不仅接收3B层的反馈信息,还接收4C层的输入和LGN的信息输入,4C层的输入是第6层神经元响应值的主要输入源,3B层的信息作为反馈输入响应值并不大,但是却决定了第6层神经元是否能向4C层和LGN反馈信号,也就是起到一个控制信号的作用。表层回路中,第5层神经元在这里的作用与底层回路中的第6层类似,2/3层起到控制信号的作用。在对V1的柱状结构设计时,主要分析了方位柱内不同层神经元的响应特性及它们之间的作用效果,设计了简单细胞和复杂细胞的响应模型；建立了颜色通路中双拮抗神经元的响应模型；设计了方向敏感神经元与方位结合的实现模型；对神经元的响应情况进行了计算,分析了模型中同层连接的作用效果及反馈的作用效果。
通过建立神经元的位置范畴和神经回路关于传递的回路范畴,说明了视觉系统中神经元的位置对应关系在信息稳定和正确传递中的重要性,并发现了神经传递通路之间的同构关系。这有利于我们通过建立单个超柱结构来推广研究整个V1的功能。这些基本的规则是可以用在非生物视觉系统中的,也就能用在仿生脑的研究中。
通过实验研究,我们发现简单细胞对外界事物探测的不精确,也就是功能上是很不完善的,这也是单个神经元简单逻辑的结果；通过神经元的相互作用传递给复杂细胞,以及通过同层神经元的相互作用之后,神经元的响应情况就已经比较精确了。这就说明了神经系统中大量神经元的相互作用能够提高整个系统的响应特性。对双拮抗神经元和对运动细胞的模拟实验研究说明了这种功能实现的机理,进一步证实了神经连接对功能特性识别的重要性。及这种这些研究成果说明对生物视觉系统的研究是能够用在仿生领域的,并且能够很大限度的模拟生物系统的性能。
Primary visual cortex (Ⅵ) is the first cortical regions, when the visual information project to the brain cortex. Information processing on the primary visual cortex has very important significance for the perception and understanding of the outside world of follow-up cortex. Generally agreed that hyper-column Structure is the basic unit of the primary visual cortex. So it is a key process to study the hyper-column for understanding and simulating the primary visual cortex.
According to the research on the physiology, this paper summarized and analyzed the neural connections between Lateral geniculate (LGN) and V1, the local neural interconnection of V1, and the neural connections between V1 and V2. This paper also Summarized and analyzed the structure relationship of function columns. The information input from LGN is the fundamental source of the V1 response. The input from other cortical regions only can maintain the response of V1. Layer 6 of V1 can project back to LGN, and this project can modify the response of LGN, and thus make this neurons have some orientation characteristics. According to the forward projection, the local interconnection of V1 can be divided into three layer, they were the layer of most simple cells (layer 4C), the layer of simple cells (layer 3B/4B), and the layer of complex cells (layer 2/3A). V1 also have two layers, layer 5 and layer 6, which mainly modify the response of V1. These two layers played an important role in the formation of V1 local loop, and the feedback interconnection can maintain the processing in V1. Different information from V1 project to different regions of V2, and this is good for extracting and processing visual information respectively. The function columns of V1 including the ocular dominance columns, orientation columns, the color column and so on, these columns connect to each other both on the position and functional characteristics. Some color-sensitive cells also sensitive to orientation, and some binocular cells also sensitive to orientation and direction.
In this paper, we designed the neural network structure, and simplified several secondary neurotransmitters in the connection. We divided the neural Transmission network of V1 into two loops, the bottom loop (layer 4C, layer 3B/4B and layer 6) and the surface loop (layer 3B/4B, layer 2/3A and layer 5). As the most important part of the bottom loop, layer 6 receive not only the project from layer3B, but also 4C and LGN. The projection from 4C is the main input source of layer 6, but the small 3B projection determine whether the layer 6 neurons feedback to layer 4C and LGN, in other words, it plays the role of a control signal. The role of layer 5 in the surface loop is similar to the layer 6 in the bottom loop, and layer 2/3 plays the role of a control signal. In the design of the V1 columnar structure, we mainly analyzed the response characteristics of neurons at different layers, and the response effect between them, and designed response models of simple cell and complex cell. We established the response model of double-opponent neurons in the color channel, calculated the response of neurons, and analyzed the effect of the local layer connection and the value of feedback in the model.
Through the establishment of the position category of neurons and the loop category of the neural loop about projection, we found the importance of the correspondence of neurons location in the visual system for the steady and correct information projection, and found the isomorphic relationship between different Neurotransmitter pathways. This will help us studying the functions of the V1 through the creation of a single super-column structure. These basic rules can be used in non-biological visual system, so can be used in the bionic brain.
By experimental research, we found the imprecise detection of simple cells to external things, that is the function is not perfect, this is the result of single neurons' simple logic. After the interconnection between neurons and project to complex cells, the response of neurons becomes more accurate. This indicates that the interaction of the large number of neurons in the nervous system can increase the response of the whole system. These results note that the study of biological visual system is able to be used in the field of biomimetic, and it is ability to simulate the performance of biological systems.
本文根据生理学上的研究,总结分析了初级视皮层与外膝体(LGN)的神经连接、本层内亚层间的神经传递关系、与V2的神经连接,以及V1内各功能柱的结构关系。LGN对V1的信息输入是V1响应的根源,其它皮层的响应输入只能调节这些信息的响应情况,V1第6亚层能够向LGN发出反馈信号,这些信号能够调节LGN上神经元的响应值,从而使这些神经元具有一定的方位特性。V1内部的神经连接根据信息的顺向传递可以分为三层,依次为最简单细胞层(4C层)、简单细胞层(3B和4B层)、复杂细胞层(2/3A层),起调节作用的亚层有两个,分别是第5层和第6层,这两个亚层对V1内部回路的形成起到了重要作用,这种反馈调节了信息在V1内部的处理。初级视皮层对V2的输入根据不同的功能是有所分组的,不同的功能信息输入到了V2不同的区域内,这样有利于视觉信息的提取和分别处理。V1内的功能柱有眼优势柱、方位柱、颜色柱等,这些功能柱位置之间和功能上都有联系。有能分辨方位的颜色敏感神经元,也有同时对方位、运动方向敏感的双眼细胞。
本文对神经传递的网络结构进行了设计,简化了一些神经传递中次要的连接关系。V1内部的神经传递网络分成了两个回路结构,一个是底层的4C、3B(4B)和第6层,一个是表层的3B、2/3A和第5层。底层回路中,第6层是反馈最重要的一环,它不仅接收3B层的反馈信息,还接收4C层的输入和LGN的信息输入,4C层的输入是第6层神经元响应值的主要输入源,3B层的信息作为反馈输入响应值并不大,但是却决定了第6层神经元是否能向4C层和LGN反馈信号,也就是起到一个控制信号的作用。表层回路中,第5层神经元在这里的作用与底层回路中的第6层类似,2/3层起到控制信号的作用。在对V1的柱状结构设计时,主要分析了方位柱内不同层神经元的响应特性及它们之间的作用效果,设计了简单细胞和复杂细胞的响应模型；建立了颜色通路中双拮抗神经元的响应模型；设计了方向敏感神经元与方位结合的实现模型；对神经元的响应情况进行了计算,分析了模型中同层连接的作用效果及反馈的作用效果。
通过建立神经元的位置范畴和神经回路关于传递的回路范畴,说明了视觉系统中神经元的位置对应关系在信息稳定和正确传递中的重要性,并发现了神经传递通路之间的同构关系。这有利于我们通过建立单个超柱结构来推广研究整个V1的功能。这些基本的规则是可以用在非生物视觉系统中的,也就能用在仿生脑的研究中。
通过实验研究,我们发现简单细胞对外界事物探测的不精确,也就是功能上是很不完善的,这也是单个神经元简单逻辑的结果；通过神经元的相互作用传递给复杂细胞,以及通过同层神经元的相互作用之后,神经元的响应情况就已经比较精确了。这就说明了神经系统中大量神经元的相互作用能够提高整个系统的响应特性。对双拮抗神经元和对运动细胞的模拟实验研究说明了这种功能实现的机理,进一步证实了神经连接对功能特性识别的重要性。及这种这些研究成果说明对生物视觉系统的研究是能够用在仿生领域的,并且能够很大限度的模拟生物系统的性能。
Primary visual cortex (Ⅵ) is the first cortical regions, when the visual information project to the brain cortex. Information processing on the primary visual cortex has very important significance for the perception and understanding of the outside world of follow-up cortex. Generally agreed that hyper-column Structure is the basic unit of the primary visual cortex. So it is a key process to study the hyper-column for understanding and simulating the primary visual cortex.
According to the research on the physiology, this paper summarized and analyzed the neural connections between Lateral geniculate (LGN) and V1, the local neural interconnection of V1, and the neural connections between V1 and V2. This paper also Summarized and analyzed the structure relationship of function columns. The information input from LGN is the fundamental source of the V1 response. The input from other cortical regions only can maintain the response of V1. Layer 6 of V1 can project back to LGN, and this project can modify the response of LGN, and thus make this neurons have some orientation characteristics. According to the forward projection, the local interconnection of V1 can be divided into three layer, they were the layer of most simple cells (layer 4C), the layer of simple cells (layer 3B/4B), and the layer of complex cells (layer 2/3A). V1 also have two layers, layer 5 and layer 6, which mainly modify the response of V1. These two layers played an important role in the formation of V1 local loop, and the feedback interconnection can maintain the processing in V1. Different information from V1 project to different regions of V2, and this is good for extracting and processing visual information respectively. The function columns of V1 including the ocular dominance columns, orientation columns, the color column and so on, these columns connect to each other both on the position and functional characteristics. Some color-sensitive cells also sensitive to orientation, and some binocular cells also sensitive to orientation and direction.
In this paper, we designed the neural network structure, and simplified several secondary neurotransmitters in the connection. We divided the neural Transmission network of V1 into two loops, the bottom loop (layer 4C, layer 3B/4B and layer 6) and the surface loop (layer 3B/4B, layer 2/3A and layer 5). As the most important part of the bottom loop, layer 6 receive not only the project from layer3B, but also 4C and LGN. The projection from 4C is the main input source of layer 6, but the small 3B projection determine whether the layer 6 neurons feedback to layer 4C and LGN, in other words, it plays the role of a control signal. The role of layer 5 in the surface loop is similar to the layer 6 in the bottom loop, and layer 2/3 plays the role of a control signal. In the design of the V1 columnar structure, we mainly analyzed the response characteristics of neurons at different layers, and the response effect between them, and designed response models of simple cell and complex cell. We established the response model of double-opponent neurons in the color channel, calculated the response of neurons, and analyzed the effect of the local layer connection and the value of feedback in the model.
Through the establishment of the position category of neurons and the loop category of the neural loop about projection, we found the importance of the correspondence of neurons location in the visual system for the steady and correct information projection, and found the isomorphic relationship between different Neurotransmitter pathways. This will help us studying the functions of the V1 through the creation of a single super-column structure. These basic rules can be used in non-biological visual system, so can be used in the bionic brain.
By experimental research, we found the imprecise detection of simple cells to external things, that is the function is not perfect, this is the result of single neurons' simple logic. After the interconnection between neurons and project to complex cells, the response of neurons becomes more accurate. This indicates that the interaction of the large number of neurons in the nervous system can increase the response of the whole system. These results note that the study of biological visual system is able to be used in the field of biomimetic, and it is ability to simulate the performance of biological systems.
引文
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[2]贺伟.2006.范畴论[M].北京：科学出版社.
[3]寿天德,周逸峰,俞洪波.2000.猴和猫初级视皮层细胞方向选择性及其功能组织的比较研究[J].中国科学C辑,04期356-362.
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