Improving deep neural networks as a modelling framework for human ventral stream computations

我博士研究的总体目标是开发更好的腹侧流模型。这种模型和大脑之间的机械相似性的标准测试是该模型预测健康大脑的神经成像数据的能力。目前，DNN可以解释灵长类电生理数据的一半以上的变异。然而，腹侧流的模型不仅应该预测健康的神经激活，而且应该复制类似人类的错误，以响应系统的进行性损害。为了填补这一空白，我正在开发一个测试案例，作为对DNN的视觉信息处理有多像大脑的额外评估。为此，我使用了语义性痴呆(SD)患者的数据。SD患者在颞叶有局限性的损害，并表现出明确的损害，例如，从属水平分类受损，而基本水平分类保留。因此，当模型的一部分受损时，充当人类颞叶模型的DNN应该复制这些损伤。一旦发现了成功的模型，受损的DNN也可能是对SD患者可能难以做出的判断和区分类型的有用预测指标。我提议的研究的第二阶段的目标是开发能够更好地预测健康神经成像数据和患者数据的DNN。虽然DNN是受生物启发的，但它们的开发目的并不是为了解释神经计算，而是作为计算机视觉问题的最常用的工程解决方案。因此，有理由建议，它们的结构、功能目标(S)、输入训练数据和学习算法都可以基于神经解剖学和神经心理学进行改进，以实现生物学上的可信。我假设，相关生物学细节的纳入将导致更多类似大脑的模型。我将首先探索具有不同功能目标的DNN；经过训练以预测项目在高维语义嵌入空间中的位置，而不是通常使用的分类目标。视觉系统的主要目标不太可能是分类。类别训练的DNN的目标是在模型的特征空间中，以尽可能远的垂直距离来分隔“猫”、“狗”和“伞”的簇，以便能够区分它们，但为了更真实地区分它们，“猫”和“狗”的簇应该比“伞”更近。因此，我将训练DNN来预测高维单词嵌入向量，该向量是从语言学获得的，其中具有相似WEV的项目出现在类似的书面上下文中。我假设这些DNN将能够更好地预测健康人和SD患者的行为数据，当这些模型作为我的新测试床的一部分时。

The overall aim of my doctoral research is to develop better models of the ventral stream. The standard test of mechanistic similarity between such a model and the brain is the model's ability to predict neuroimaging data from healthybrains. Currently, DNNs can explain more than half the variance of primate electrophysiological data. However, a model of the ventral stream should not only predict healthy neural activations, but should also reproduce human-like errors in response to progressive lesions of the system. Filling in this gap, I am developing a test case that would act as an additional assessment of how brain-like a DNN's visual information processing is. To this aim, I am using semantic dementia (SD) patient data. SD patients have localised lesions in the temporal lobe and exhibit well-defined impairments, for example, subordinate level categorisation is impaired while basic level categorisation is preserved. DNNs that act as models of the human temporal lobe should therefore replicate these impairments when parts of the model are lesioned. Once successful models are found, lesioned DNNs mayalso be useful predictors of the types of judgements and distinctions that may be difficult for SD patients. The aim of the second stage of my proposed research will be to develop DNNs that better predict both healthy neuroimaging data and patient data. Whilst DNNs are biologically-inspired, they have not been developed with the intention of explaining neural computations, but instead are used most commonly as engineering solutions to computer vision problems. Therefore, it is reasonable to suggest that their architecture, functional objective(s), input training data and learning algorithms could all be improved based on neuroanatomy and neuropsychology in order to achieve biological plausibility. I hypothesise that the inclusion of relevant biological detail will result in more brain-like models. I will initially explore DNNs with a different functional objective; trained to predict an item's position in a high-dimensional semantic embedding space rather than the commonly used categorisation objective. It is unlikely that the visual system's primary goal is categorisation. A category-trained DNN aims to separate the clusters of 'cat,' 'dog' and 'umbrella' orthogonally as far apart as possible in order to be able to differentiate between them, but to be more realistic, the 'cat' and 'dog' clusters should be much closer together than 'umbrella' in the model's feature space. I will therefore train a DNN to predict high-dimensional word embedding vectors, obtained from linguistics in which items with similar WEVs appear in similar written contexts. I hypothesis that these DNNs will be able to better predict both behavioural data from healthy humans and also SD patients when the models are lesioned as part of my new testbed.