Modeling Multisensory Enhancement in the Superior Colliculus

大脑是已知的最强大的信息处理器。 它的部分力量来自于它联合收割机（或整合）来自多个感觉系统的信息的能力。 例如，我们可以将联合收割机视觉、听觉和触觉结合起来，形成对环境中事件的综合感知。 将来自多个感觉系统的输入结合起来被称为多感觉整合。 了解多感觉整合在大脑中的实际工作方式将为理解感知的本质提供重要的见解。 在研究感知时，重要的是要认识到，无论感觉系统有多好，它们都不能提供完美的信息。 由感觉系统提供的信息在某种程度上必须被认为是不确定的。 感知可能涉及使用感官输入来提供环境中事件的证据。 上级丘是一种大脑结构，它使哺乳动物（如我们）将我们的头和眼睛转向环境中新事件的方向。 上级丘中的许多神经元接收来自多个感觉系统的输入。 这些多感觉神经元表现出一种被称为多感觉增强的特性，即对来自一个感觉系统的输入的反应可以被来自另一个感觉系统的输入大大增强。我们提出了一个假设，即多感觉增强是丘神经元使用多感觉输入来计算环境中发生事件的概率的过程的结果。 我们项目的目标是开发一个模型，解释神经元如何实际执行这种计算，然后使用来自丘神经元的实际数据来测试模型。 我们提出的计算模型将是自适应的，也就是说，能够根据其对环境的经验改变自己的行为。 它将由两个阶段组成，这两个阶段代表了大脑中多感觉增强发展的两个独立阶段。 已知丘神经元接收来自大脑中较低和较高水平的多感觉输入。 在第一阶段，模型丘神经元将学习从较低级别的多感官输入中提取最大量的信息。 在第二阶段，模型丘神经元使用更高级别的多感觉输入来改进它们对环境中事件概率的计算。 我们将通过将其行为与在猫中研究的实际丘神经元的行为进行比较来测试该模型。 我们的模型预测，观察到的丘神经元的多感觉增强量应取决于其他属性，如这些神经元在丘的位置。 我们的模型还应该预测当高层输入被移除时，丘神经元的行为应该如何改变。 提出一个多感觉增强的详细模型，并根据实际数据测试该模型，应该为我们提供一些新的见解，了解多感觉增强在大脑中是如何组织的。 更好地理解这种更基本的多感觉整合形式可能会为更好地理解一般的感知打开大门。

The brain is the most powerful information processor known. Part of its power derives from its ability to combine (or integrate) information from multiple sensory systems. For example, we can combine sight, sound, and touch in forming an integrated perception of events in our environment. Combining input from multiple sensory systems is known as multisensory integration. Understanding how multisensory integration actually works in the brain will provide important insights into the nature of perception. In studying perception it is important to realize that, no matter how good sensory systems may be, they can't provide perfect information. The information provided by sensory systems must be considered as uncertain to some extent. Perception may involve the use of sensory inputs to provide evidence of events in the environment. The superior colliculus is a brain structure that causes mammalian animals (like us) to turn our heads and eyes in the direction of new events in the environment. Many neurons in the superior colliculus receive inputs from more than one sensory system. These multisensory neurons exhibit a property know as multisensory enhancement, in which the response to an input from one sensory system can be greatly increased by input from another sensory system.We have developed an hypothesis that multisensory enhancement is the result of processing by which collicular neurons use multisensory input to compute the probability that an event has occurred in the environment. The goal of our project is to develop a model that explains how neurons might actually perform this computation, and then use actual data from collicular neurons to test the model. The computational model we propose will be adaptive, that is, capable of changing its own behavior on the basis of its experience with the environment. It will be composed of two stages that are meant to represent two separate stages in the development of multisensory enhancement in the brain. Collicular neurons are known to receive multisensory inputs from both lower and higher levels in the brain. In the first stage, model collicular neurons will learn to extract the maximum amount of information from lower-level multisensory inputs. In the second stage, model collicular neurons with use higher-level multisensory inputs to refine their computation of the probability of events in the environment. We will test this model by comparing its behavior with that of actual collicular neurons studied in cats. Our model predicts that the amount of multisensory enhancement observed for collicular neurons should depend upon other properties such as the location of those neurons in the colliculus. Our model should also make predictions concerning how the behavior of collicular neurons should change when the higher-level inputs are removed. Proposing a detailed model of multisensory enhancement and testing the model against actual data should provide us with some new insights into how multisensory enhancement may be organized in the brain. A better understanding of this more basic form of multisensory integration might open the door for a better understanding of perception in general.