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.

视觉对象识别的任务已经通过许多计算神经网络来解决；然而，仅凭这一点还不足以理解视觉世界。还需要形成场景中特征和对象的分层表示，编码它们之间的语义关系（例如“轮子”是“汽车”的一部分）。这是心理学中的绑定问题：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？最近提出了一种解决绑定问题的潜在解决方案，其形式是模型网络，该网络包含四个关键的生物学特征，使其与以前的计算机视觉网络区分开来：（i）自上而下和横向连接，（ii）尖峰动力学，（iii）随机分布的轴突延迟，以及（iv）尖峰时间依赖性可塑性。该网络有潜力以神经元组的形式编码刺激，这些神经元以时间精确的序列（多时神经组）激发，并且已被证明可以开发出仅在低级特征表示驱动高级特征表示时才激发的神经元。级别特征表示。然而，到目前为止，只有非常有限的刺激集被用于训练和相对表面水平的分析。我建议在比以前使用的更生态有效的刺激集上训练该网络，包括 3D 对象和多对象场景的图像，并对通过网络各层开发的表示进行深入分析，编码既由单个神经元又由多时神经元组影响。这样的项目将采取步骤创建一个能够真正理解视觉世界的生物现实计算机网络。这将具有很大的实际用途，并提供对灵长类动物视觉系统如何发展视觉特征正确绑定的分层表示的深入了解：视觉心理学中长期存在且至关重要的问题。