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首页> 外文期刊>Neuroinformatics >Cocaine-Induced Preference Conditioning: a Machine Vision Perspective
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Cocaine-Induced Preference Conditioning: a Machine Vision Perspective

机译：可卡因诱导的偏好条件：机器视觉透视

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Existing work on drug-induced synaptic changes has shown that the expression of perineuronal nets (PNNs) at the cerebellar cortex can be regulated by cocaine-related memory. However, these studies on animals have mostly relied on limited manually-driven procedures, and lack some more rigorous statistical approaches and more automated techniques. In this work, established methods from computer vision and machine learning are considered to build stronger evidence of those previous findings. To that end, an image descriptor is designed to characterize PNNs images; unsupervised learning (clustering) is used to automatically find distinctive patterns of PNNs; and supervised learning (classification) is adopted for predicting the experiment group of the mice from their PNN images. Experts in neurobiology, who were not aware of the underlying computational procedures, were asked to describe the patterns emerging from the automatically found clusters, and their descriptions were found to align surprisingly well with the two types of PNN images revealed from previous studies, namely strong and weak PNNs. Furthermore, when the set of PNN images corresponding to every mice in the saline (control) group and the conditioned (experimental) group were characterized using a bag-of-words representation, and subject to supervised learning (saline vs conditioned mice), the high classification results suggest the ability of the proposed representation and procedures in recognizing these groups. Therefore, despite the limited size of the dataset (1,032 PNN images of 6 saline and 6 conditioned mice), the results support existing evidence on the drug-related brain plasticity, while providing higher objectivity.
机译：存在对药物诱导的突触变化的工作表明，可以通过可卡因相关的记忆来调节小脑皮质在小脑皮层处的射孔网（PNNS）的表达。然而，这些对动物的研究主要依赖于有限的手动驱动程序，并且缺乏一些更严格的统计方法和更自动化的技术。在这项工作中，来自计算机视觉和机器学习的建立方法被认为是建立以前发现的更强大的证据。为此，图像描述符旨在表征PNNS图像;无监督的学习（聚类）用于自动查找PNN的独特模式;采用监督学习（分类）从PNN图像预测小鼠的实验组。未知潜在的计算程序的神经生物学专家被要求描述从自动发现的集群中出现的模式，并且发现他们的描述对于从之前研究的两种类型的PNN图像令人惊讶地对齐，即强大弱pnns。此外，当使用袋子的表示表征对对应于盐水（对照）组和调节（实验）组中的每只小鼠的PNN图像的组PNN图像时，并且受到监督学习（盐水与条件小鼠）的影响高分类结果表明拟议的代表和程序在承认这些群体方面的能力。因此，尽管数据集的大小有限（1,032个PNN图像的6种盐水和6个条件小鼠），但结果支持有关药物相关的脑可塑性的现有证据，同时提供更高的客观性。

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