Dynamics of sensory processing: from single neurons to behaviour

想象一下，当你第一次用静止的指尖触摸粗糙的表面，然后以不同的速度来回移动时，你会感受到不同的感觉。这些感知的变化可以告诉我们皮肤中触觉感受器的动态特性，以及参与体感信息处理的大脑网络的动态特性。这种“主动感测”被各种感觉系统使用。然而，总的来说，我们对感觉结构（如指尖或眼睛）的运动如何与神经处理的动力学协调以优化感觉信息的获取知之甚少。我的研究主要集中在弱电刀鱼的主动感知和神经动力学。这些鱼是感官处理的专家。它们通过监测自生电场的微小变化来检测周围的物体，尽管存在高水平的自然干扰或噪音。为了做到这一点，他们用固定的来回游泳动作来探测周围的环境，从而直接影响皮肤上专门的电感受器编码的电感觉信号。该建议的重点是运动的作用，在对象检测和定位使用电感提示。我们的方法是双管齐下的，结合行为分析和单神经元电生理学，在多学科的设置。我们将开发一个新的系统来创建虚拟的电感景观测试对象检测行为研究。我们将使用我们最近开发的神经元-计算机接口来确定网络相互作用如何影响单个神经元的运动处理。从这些不同的方法产生的多学科培训场将是理想的学生在各个层次，准备他们在生物技术和高科技，以及在学术界的一系列职业生涯。

Imagine the different sensations you perceive as you first touch a rough surface with a motionless fingertip, and then with back-and-forth movements at various speeds. These changes in perception can tell us about the dynamic properties of touch receptors in the skin, as well as those of the brain networks involved in somatosensory information processing. Such "active sensing" is used by a variety of sensory systems. Yet, in general, we know little about how movement of a sensory structure (like your finger tip or your eye) is coordinated with the dynamics of neural processing to optimize the acquisition of sensory information. My research focuses on active sensing and neural dynamics in the weakly electric knifefish. These fish are experts in sensory processing. They detect objects in their surroundings by monitoring minute changes in their self-generated electric field, despite high levels of natural interference, or noise. To do this, they probe their environment with stereotyped back-and-forth swimming movements, thus directly influencing the electrosensory signals encoded by the specialized electroreceptors on their skin. This proposal focuses on the role of motion in object detection and localization using electrosensory cues. Our approach is two-pronged, combining behavioural analyses and single neuron electrophysiology, in a multi-disciplinary setting. We will develop a novel system to create virtual electrosensory landscapes for testing object detection in behavioural studies. And we will use our recently developed neuron-computer interface to determine how network interactions influence motion processing by single neurons. The multidisciplinary training ground resulting from these diverse approaches will be ideal for students at all levels, preparing them for a range of careers in biotechnology and high-technology, as well as in academia.