Computational modelling visual perception in a biologically realistic neural network: Developing rich, hierarchical representations of visual scenes

生物现实神经网络中视觉感知的计算建模：开发视觉场景的丰富、分层表示

• 批准号: 2108388
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2108388
• 关键词: Computational; modelling; visual; perception; biologically

The task of visual object recognition has been solved by many computational neural networks; however, this alone is insufficient for making sense of the visual world. It is also necessary to form hierarchical representations of the features and objects within a scene, encoding their semantic relations to one another (e.g. "wheel" is part of "car"). This is the binding problem in psychology: when multiple objects are present, how do we know which features are part of which objects? A potential solution to the binding problem has recently been proposed, in the form of a model network which incorporates four key biological features that set it apart from previous computer vision networks: (i) top-down and lateral connections, (ii) spiking dynamics, (iii) randomly distributed axonal delays, and (iv) spike-timing dependent plasticity. This network has the potential to encode stimuli in the form of groups of neurons which fire in a temporally precise sequence (polychronous neural groups), and has been shown to develop neurons that fire only if a low-level feature representation is driving a high-level feature representation. So far, however, only very limited stimulus sets have been used for training and relatively surface-level analyses employed. I propose to train this network on a more ecologically valid stimulus set than that previously used, including images of 3D objects and multi-object scenes, and carry out an in-depth analysis of the representations that develop through the layers of the network, encoded both by individual neurons and polychronous groups. Such a project would take steps towards the creation of a biologically- realistic computer network that can truly make sense of the visual world. This would be of much practical use, and provide insight into how the primate visual system develops hierarchical representations in which visual features are correctly bound: a long-standing and crucial problem in visual psychology.

许多计算神经网络已经解决了视觉对象识别的任务。但是，仅此一项就不足以理解视觉世界。还必须在场景中形成特征和对象的层次结构表示，将其语义关系彼此编码（例如，“车轮”是“汽车”的一部分）。这是心理学中的约束力问题：当存在多个对象时，我们如何知道哪些特征是哪些对象的一部分？最近提出了针对结合问题的潜在解决方案，该模型网络的形式结合了四个关键的生物学特征，这些特征将其与以前的计算机视觉网络区分开来：（i）自上而下和侧向连接，（ii）峰值动力学，（iii）随机分布的轴突延迟，以及（iv）尖峰触发依赖性可塑性。该网络具有以神经元组的形式编码刺激的潜力，这些神经元以时间精确的序列（多级神经群）发射，并且已证明只有在低级特征表示驱动高级特征表示形式时，才会开发神经元。然而，到目前为止，仅使用非常有限的刺激集用于训练和所采用的相对表面水平分析。我建议将该网络训练该网络在更有效的刺激集上，包括3D对象和多对象场景的图像，并对通过网络层次发展的表示形式进行深入分析，并由单个神经元和多位数组编码。这样的项目将采取步骤来创建具有现实的计算机网络，该网络可以真正理解视觉世界。这将是很多实际的使用，并提供了有关灵长类动物视觉系统如何发展视觉特征正确绑定的层次表示的见解：视觉心理学中的长期和至关重要的问题。