Dynamics of sensory processing: from neurons to behaviour

感觉处理的动力学：从神经元到行为

• 批准号: RGPIN-2014-05872
• 负责人: Lewis, John
• 金额: $ 2.48万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=611894
• 关键词: Dynamics; sensory; processing; neurons; behaviour

Imagine looking out the window of a high-speed train; nearby objects move quickly by, but a distant farmhouse appears relatively stationary. Now recall the different sensations from touching a rough surface with a motionless fingertip, and then with back-and-forth finger movements. In both cases, motion helps provide information about the environment, the distance of different objects in the visual example, and surface texture in the touch example. Besides being entirely different senses, the examples differ in another important way: the source of the motion. You passively observe the scene from the train, but you actively move your finger over the rough surface. An advantage of playing an active role is that you can move your finger at different speeds with different pressure to gather different information about the surface. This is referred to as “active sensing” and more generally means that energy is expended to acquire information about the environment. Self-motion is only one method of active sensing. Bats actively sense by producing ultrasound calls, a behaviour called echolocation. In active sensing, there is a “chicken-and-egg” problem. Sensory perception influences action, and action influences perception. For this reason, understanding how brain networks coordinate and control active sensing is a complicated task. My research focuses on an expert in active sensing, the weakly electric knifefish. These fish sense their environment using a self-generated oscillating electric field. Specialized electroreceptors on their skin encode modulations in this field produced by nearby objects. Sensing with electric fields enables these fish to navigate, capture prey and communicate in the dark, avoiding vision-dependent predators. This does not come without significant challenges. Electric field modulations are miniscule and often contaminated with high levels of background noise, including the electric signals of other fish – think of trying to follow a conversation of whispers in a noisy room. The fish overcomes these challenges by using two strategies. First, it actively controls its swimming movements – the “knifefish” namesake is a result of their stereotyped back-and-forth swimming movements (recall the finger movements discussed above used for active touch). Second, the oscillating electric field is extremely precise to allow even the smallest modulations to be detected. It is generated by a brain “pacemaker” network that is the least variable (most precise) of all known biological clocks. My proposal focuses on these strategies in the context of two fundamental questions: (1) How does motion influence perception? (2) How do brain networks control oscillatory activity? Our approach is multi-disciplinary by necessity. We combine the analysis of perception and behaviour, with single neuron electrophysiology and computational modeling, drawing from expertise in a range of fields, from neurophysiology and molecular biology to computer science and engineering. This multidisciplinary training ground is ideal for students at all levels, preparing them for a range of careers in biotechnology and high-technology, as well as in government labs and academia. A better understanding of brain oscillations and associated electric fields will impact diverse areas in neuroscience, from deep brain stimulation and neuroprosthetics to the etiology of epilepsy. Electric field-based sensing can be used in robotics, as well as new touch-screens. But these studies will also impact our understanding of sensing in general, with the hope that one day we will understand how brain processes underlie the memory of a high-speed train ride, the touch of a surface, or the electrosensory world of electric fish.

想象一下，从高速列车的窗户向外看，附近的物体快速移动，但远处的农舍似乎相对静止。现在回想一下用静止的指尖触摸粗糙的表面，然后来回移动手指的不同感觉。在这两种情况下，运动有助于提供有关环境的信息，在视觉示例中不同对象的距离，以及在触摸示例中的表面纹理。除了完全不同的意义，例子在另一个重要方面也不同：运动的来源。你被动地从火车上观察场景，但你主动地在粗糙的表面上移动手指。扮演主动角色的一个好处是，你可以用不同的压力以不同的速度移动手指，以收集有关表面的不同信息。这被称为“主动感测”，并且更一般地意味着消耗能量来获取关于环境的信息。自动运动只是主动感知的一种方法。蝙蝠通过发出超声波来主动感知，这种行为称为回声定位。 在主动传感中，存在“鸡和蛋”的问题。感官知觉影响行动，行动影响知觉。因此，了解大脑网络如何协调和控制主动感知是一项复杂的任务。我的研究集中在一个专家在主动传感，弱电刀鱼。这些鱼利用自身产生的振荡电场来感知它们的环境。它们皮肤上的特殊电感受器对附近物体产生的电场调制进行编码。电场感应使这些鱼能够在黑暗中导航，捕获猎物和交流，避免依赖视觉的捕食者。这并非没有重大挑战。电场调制是微小的，并且经常受到高水平背景噪音的污染，包括其他鱼类的电信号-想想在嘈杂的房间里试图跟随耳语的谈话。 鱼通过使用两种策略来克服这些挑战。首先，它主动控制自己的游泳动作--“刀鱼”的同名是它们刻板的来回游泳动作的结果（回忆一下上面讨论的用于主动触摸的手指运动）。其次，振荡电场非常精确，即使是最小的调制也能被检测到。它是由大脑“起搏器”网络产生的，是所有已知生物钟中变化最小（最精确）的。我的建议集中在两个基本问题的背景下，这些策略：（1）运动如何影响知觉？(2)大脑网络如何控制振荡活动？ 我们的方法是多学科的必要性。我们结合联合收割机的感知和行为的分析，单神经元电生理学和计算建模，从一系列领域的专业知识，从神经生理学和分子生物学到计算机科学和工程。这个多学科培训基地非常适合各个级别的学生，为他们在生物技术和高科技以及政府实验室和学术界的一系列职业做好准备。 更好地了解脑振荡和相关电场将影响神经科学的各个领域，从脑深部刺激和神经修复术到癫痫的病因学。基于电场的传感可以用于机器人技术以及新的触摸屏。但这些研究也将影响我们对感觉的总体理解，希望有一天我们能理解大脑过程是如何支撑高速火车旅行、表面触摸或电鱼的电感觉世界的。