From sensory ecology to cognition - the multi-sensory perception and representation of objects in fish

大多数动物使用来自多个感官的信息来分析它们环境的特征并指导行为。感觉生物学中的一个基本问题是，多个感觉系统如何协同工作，以增加整体感知的可靠性。在我们之前的项目中，我们已经证明了弱电鱼类Gnathonemus petersii能够跨模式识别物体，即它们可以跨模式识别物体，而无需事先训练正在测试的感觉。这表明，在学习之后，包含对象特征的表征被存储在大脑中，随后可以被其他感官访问。此外，我们还表明，多感官对象知觉受到感官输入的动态加权的影响，即根据可靠性来最大限度地减少不确定性的单一感官模式的鱼的重量输入。这种感觉输入的权重也可能导致在特定情况或任务(感觉捕获)中某种感觉的支配地位。在这个项目中，我们想要研究脊椎动物多感官知觉的基本过程。以G.petersii为模型，我们将探索支配跨通道物体识别和感官输入加权的规则和神经基础。我们将使用专门设计的物体，当通过视觉或电子感官感知时，这些物体提供不同的信息。在行为部分，我们将调查Gnathonemus是否以最佳方式整合信息，即感觉权重是否取决于特定感觉通道的可靠性。通过改变训练条件，我们将检验多感官输入的动态加权是受先前经验的影响，还是知觉系统的固有属性。此外，我们还将探讨如果不同的感官在某些定义的可靠性水平上提供了相互冲突的信息，那么感官信息是如何加权的。在神经解剖学部分，我们的目标是寻找跨通道物体识别和感官加权的神经基础。首先，我们将在视觉和电子敏感的大脑区域注入选择性示踪剂，以确定提升的感觉系统，并能够解释单感觉和多感觉融合。在物体检测过程中，相关大脑区域的主要候选区域是小脑、视顶盖(中脑)和大脑皮质(前脑)。除了示踪研究外，我们还将进行旨在揭示多感觉学习和跨模式迁移过程中神经元激活过程的实验。这是通过可视化鱼脑中激活标记(磷酸化外部信号相关激酶，PERK)的产生来实现的。我们将定义在多感官对象检查过程中活跃的单感官和多感官部位。这可能最终会告诉我们，感觉权重在彼得氏菌大脑中的哪里以及如何发生。

Most animals use information from multiple senses to analyse features of their environment and to guide behaviour. A fundamental question in sensory biology is how multiple sensory systems operate together to increase the reliability of the overall perception. In our previous project we have shown that the weakly electric fish Gnathonemus petersii is capable of cross-modal object recognition, i.e. they can recognise objects cross-modally without prior training with the sense being tested. This suggests that after learning, a representation containing object features is stored in the brain and can subsequently be accessed by other senses. Furthermore we showed that multi-sensory object perception is influenced by a dynamic weighting of sensory inputs, i.e. fish weight input from single sensory modalities according to their reliability to minimise uncertainty. This weighting of sensory inputs can also lead to the dominance of a certain sense during specific situations or tasks (sensory capture). In this project we want to investigate the fundamental processes that underpin multi-sensory perception in vertebrates. Using G. petersii as a model, we will explore the rules and the neural substrates governing cross-modal object recognition and weighting of sensory input. We will use specially designed objects, which provide different information when sensed either through vision or the electric sense. In the behavioural part, we will investigate whether Gnathonemus integrates information in an optimal fashion, i.e. whether sensory weighting depends on the reliability of the particular sensory modality. By varying the training conditions we will examine whether dynamic weighing of multisensory input is influenced by prior experience or is an innate property of the perceptual system. In addition we will explore how sensory information is weighted if the different senses provide conflicting information at certain defined reliability levels. In the neuroanatomical part, we aim at finding the neural substrates for cross-modal object recognition and sensory weighting. First, we will inject selective tracers into visual and electro-sensitive sensory brain areas to define the ascending sensory systems and to be able to interpret unisensory and multisensory convergence. Prime candidates for relevant brain areas during object detection are parts of the Cerebellum, the Optic Tectum (midbrain) and the Pallium (forebrain). In addition to the tracing studies, we will perform experiments aimed at revealing neuronal activation processes during multi-sensory learning and cross-modal transfer. This is achieved by visualizing the production of activation markers (phosphorylated external signal related kinase, pERK) in the fish brain. We will define the uni- and multi-sensory sites which are active during multi-sensory object inspection. This might ultimately tell us, where and how sensory weighting takes place in the brain of G. petersii.