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.

中文翻译：

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可卡因诱导的偏好调节：机器视觉的视角。

有关药物引起的突触变化的现有研究表明，可卡因相关的记忆可调节小脑皮层神经周围神经网（PNN）的表达。但是，这些对动物的研究主要依靠有限的手动操作程序，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，从计算机视觉和机器学习中建立的方法被认为可以为这些先前的发现建立更强有力的证据。为此，设计了一个图像描述符来表征PNNs图像。无监督学习（聚类）用于自动查找PNN的独特模式；并采用监督学习（分类）从其PNN图像中预测小鼠的实验组。神经生物学专家 谁不知道底层的计算程序，他们被要求描述从自动发现的簇中出现的模式，并且他们的描述与先前研究揭示的两种类型的PNN图像出奇地吻合，即强和弱PNN。此外，当使用单词袋表示法来表征对应于生理盐水（对照组）和条件（实验）组中每只小鼠的一组PNN图像，并接受监督学习（盐水与条件小鼠）时，较高的分类结果表明拟议的代表性和程序能够识别这些群体。因此，尽管数据集的大小有限（6只盐水和6只条件小鼠的1,032张PNN图像），