Benchmarking birds core object recognition abilities with rodents and primates

用啮齿动物和灵长类动物对鸟类的核心物体识别能力进行基准测试

• 批准号: 533395153
• 负责人: Dr. Jonas Rose
• 金额: --
• 依托单位: Abteilung Neuropsychologie
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/533395153?language=en
• 关键词: Benchmarking; birds; core; object; recognition

The primate visual system can recognize objects despite large changes in their appearance on the retina (e.g., size, position, lighting etc.). This ability is known as invariant object recognition, and it is the foundation for cognitive and memory processes that depend on visual information. Despite invariant object recognitions importance in vision, its computational mechanisms remain poorly understood. Primates have traditionally been the animal model of choice for investigations into invariant object recognition and all the species that have been tested display advanced object invariance capabilities. These capabilities depend on a hierarchical stage of processing equivalent to inferior temporal cortex. More recently, rodents have been shown to also be capable of object invariance, though at a lower-level equivalent to mid-level primate visual areas (V4). An unsolved question is whether birds – who have advanced eyesight and visual systems like primates – are also capable of invariant object recognition despite having a very different neuronal architecture to the mammalian brain. In the current application, we will benchmark the performance of pigeons and jackdaws on the same object invariance tasks as what have been used to evaluate primates and rodent performance. Corvids display advanced cognitive abilities like primates – such as face-recognition, tool use etc. – and have a greatly expanded endbrain relative to less cognitively advanced birds like pigeons. We will determine for the first time if the expanded jackdaw brain is capable of supporting object invariance at the level of primate inferior temporal cortex. By benchmarking the performance of birds in directly comparable object invariance tasks, we will understand what the universal and essential computations are that mediate object invariance across brains with very different architectures.These investigations are broken down into three stages. Firstly, we will train pigeons and jackdaws on a simple object discrimination task to evaluate if their visual systems can contend with relatively simple object transformations. Secondly, we will train pigeons and jackdaws on a complex object discrimination task to determine if the avian brain can contend with complex transformations of objects in complex backgrounds, revealing the upper limit of shape complexity that pigeons and jackdaws can compute from images. Lastly, we will perform large-scale recordings from different stages of the visual system in pigeons and jackdaws while they passively view images of objects. The final stage enables us to determine how the avian nuclear neuronal architecture represents the physical structure of objects. These investigations - in collaboration with the Issa lab at Columbia University - will reveal the computational underpinnings of invariant object recognition. These insights will be used to help guide the development of the next generation of machine vision systems.

灵长类动物的视觉系统可以识别物体，尽管物体在视网膜上的外观发生了很大的变化（例如，大小、位置、光线等）。这种能力被称为不变物体识别，它是依赖视觉信息的认知和记忆过程的基础。尽管不变物体识别在视觉中的重要性，其计算机制仍然知之甚少。传统上，灵长类动物一直是研究不变物体识别的首选动物模型，所有经过测试的物种都显示出先进的物体不变性能力。这些能力依赖于相当于下颞叶皮层的分层处理阶段。最近，啮齿类动物也被证明具有物体不变性能力，尽管其水平较低，相当于灵长类动物的中级视觉区域（V4）。一个尚未解决的问题是，尽管鸟类的神经结构与哺乳动物的大脑截然不同，但它们是否也有能力识别不变的物体——它们拥有像灵长类动物一样先进的视力和视觉系统。在当前的应用程序中，我们将对鸽子和寒鸦在相同的对象不变性任务上的性能进行基准测试，这些任务已用于评估灵长类动物和啮齿动物的性能。鸦科动物表现出像灵长类动物一样先进的认知能力，比如面部识别、工具使用等，而且相对于鸽子等认知能力不那么先进的鸟类，鸦科动物的脑末梢要大得多。我们将首次确定扩展后的寒鸦大脑是否能够在灵长类动物的下颞叶皮层水平上支持物体不变性。通过对鸟类在直接可比较的对象不变性任务中的性能进行基准测试，我们将了解在具有非常不同架构的大脑中调解对象不变性的通用和基本计算是什么。这些调查分为三个阶段。首先，我们将训练鸽子和寒鸦进行简单的物体识别任务，以评估它们的视觉系统是否能够应对相对简单的物体转换。其次，我们将训练鸽子和寒鸦进行复杂的物体识别任务，以确定鸟类大脑是否能够应对复杂背景下物体的复杂变换，揭示鸽子和寒鸦从图像中计算形状复杂性的上限。最后，我们将在鸽子和寒鸦被动观看物体图像时，从视觉系统的不同阶段进行大规模记录。最后一个阶段使我们能够确定鸟类核神经元结构如何代表物体的物理结构。这些研究——与哥伦比亚大学的Issa实验室合作——将揭示不变物体识别的计算基础。这些见解将用于帮助指导下一代机器视觉系统的开发。