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

视觉对象识别的任务已经由许多计算神经网络解决;然而，仅凭这一点还不足以理解视觉世界。还需要形成场景内的特征和对象的分层表示，对它们彼此的语义关系进行编码（例如，“车轮”是“汽车”的一部分）。这是心理学中的一个约束性问题：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方案，其形式为模型网络，该模型网络包含四个关键的生物特征，这些特征使其与以前的计算机视觉网络不同：（i）自上而下和横向连接，（ii）尖峰动态，（iii）随机分布的轴突延迟，以及（iv）尖峰定时依赖的可塑性。该网络有可能以神经元组的形式对刺激进行编码，这些神经元组以时间上精确的序列（多神经元组）发射，并且已经被证明只有在低级别特征表示驱动高级别特征表示时才能开发神经元。然而，到目前为止，只有非常有限的刺激集已被用于培训和相对表面水平的分析。我建议在一个比以前使用的更生态有效的刺激集上训练这个网络，包括3D物体和多物体场景的图像，并对通过网络层开发的表示进行深入分析，由单个神经元和多神经元组编码。这样一个项目将采取步骤，创造一个生物现实的计算机网络，可以真正使视觉世界的意义。这将有很大的实际用途，并提供洞察灵长类动物的视觉系统如何发展层次表示，其中视觉特征是正确的约束：视觉心理学中一个长期存在的关键问题。

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.