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Primate vision: a single layer perception
Neural Computing and Applications ( IF 6 ) Pub Date : 2021-03-16 , DOI: 10.1007/s00521-021-05868-0
Satyabrat Malla Bujar Baruah , Deepsikha Nandi , Plabita Gogoi , Soumik Roy

Visual signals play a significant role in learning, as eyes are actively acquiring new data frames every second and extract relevant information for learning of the newly acquired data in terms of pattern recognition, object identification, and so on. An attempt has been made to link functionality to distinct morphologies of retinal ganglion cells (RGC). Each RGC’s are organized in specific modular connectivity patterns with the photoreceptor cells via bipolar cells. Two distinct morphologies, separately integrated to a single layer network of RGCs, suggest multi-scale feature extraction and identification as one of the functional aspects, depending on the spatial spread of the dendrites of an individual neuron. Apart from texture selectivity, the model also suggests image segmentation as the basic functionality of a single-layered network of RGCs, which might be further feed-forward to successive networks for clustering and classification of visual information. The model shows directional edge selectivity as connectivity specific computation whereas the sensitivity toward fine to coarse edges is specific to the dendritic spread of the connected RGC. Later, the proposed model is incorporated in the hmax model designed by Poggio inspired by Hubel and Wiesel’s functional architecture of the striate cortex that produces some significant results in terms of pattern learning and object recognition.



中文翻译:

灵长类动物视觉:单层感知

视觉信号在学习中起着重要作用,因为眼睛正在每秒主动地获取新的数据帧,并从模式识别,对象识别等方面提取用于学习新获取的数据的相关信息。已经尝试将功能性链接到视网膜神经节细胞(RGC)的不同形态。每个RGC通过双极细胞与感光细胞以特定的模块化连接方式组织。分别集成到RGC的单层网络中的两种截然不同的形态建议将多尺度特征提取和识别作为功能方面之一,具体取决于单个神经元树突的空间分布。除了纹理选择性之外,该模型还建议将图像分割作为RGC单层网络的基本功能,可能会进一步前馈到用于视觉信息的聚类和分类的连续网络。该模型将方向性边缘选择性显示为特定于连接性的计算,而对细到粗边缘的敏感性则特定于所连接的RGC的树枝状分布。后来,该提议的模型并入了Poggio设计的hmax模型中,该模型是受Hubel和Wiesel的条纹皮质功能结构启发而设计的,该模型在模式学习和对象识别方面产生了一些重要成果。该模型将方向性边缘选择性显示为特定于连接性的计算,而对细到粗边缘的敏感性则特定于所连接的RGC的树枝状分布。后来,该提议的模型并入了Poggio设计的hmax模型中,该模型是受Hubel和Wiesel的条纹皮质功能结构启发而设计的,该模型在模式学习和对象识别方面产生了一些重要成果。该模型将方向性边缘选择性显示为特定于连接性的计算,而对细到粗边缘的敏感性则特定于所连接的RGC的树枝状分布。后来,该提议的模型并入了Poggio设计的hmax模型中,该模型是受Hubel和Wiesel的条纹皮质功能结构启发而设计的,该模型在模式学习和对象识别方面产生了一些重要成果。

更新日期:2021-03-16
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