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

许多计算神经网络已经解决了视觉对象识别的任务；然而，仅凭这一点是不足以理解视觉世界的。还需要对场景中的特征和对象进行分层表示，对它们之间的语义关系进行编码。“wheel”是“car”的一部分)。这是心理学中的绑定问题：当多个物体存在时，我们如何知道哪个特征是哪个物体的一部分？最近提出了一个潜在的结合问题的解决方案，以一个模型网络的形式，它包含了四个关键的生物学特征，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）尖峰动力学，（iii）随机分布的轴突延迟，以及（iv）尖峰时间依赖的可塑性。该网络具有以神经元组的形式编码刺激的潜力，这些神经元组以时间精确的顺序（多时神经组）放电，并且已被证明只有在低级特征表征驱动高级特征表征时才会产生放电的神经元。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，并对通过网络层发展的表征进行深入分析，这些表征由单个神经元和多时间组编码。这样一个项目将朝着创造一个生物学上真实的计算机网络迈出一步，这个网络可以真正理解视觉世界。这将有很大的实际用途，并提供洞察灵长类视觉系统如何发展分层表征，其中视觉特征被正确地绑定：这是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.