Dynamics of sensory processing: from single neurons to behaviour

想象一下，当你先用不动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摸感受器的动态特性，以及参与体感信息处理的大脑网络的动态特性。这种“主动感知”被各种感官系统所使用。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相协调以优化感觉信息的获取知之甚少。本文的研究重点是弱电刀鱼的主动感知和神经动力学。这些鱼是感官处理方面的专家。它们通过监测自身产生的电场的微小变化来探测周围的物体，尽管有很高水平的自然干扰或噪音。为了做到这一点，他们通过固定的来回游泳动作来探测他们的环境，从而直接影响他们皮肤上专门的电感受器编码的电感觉信号。这项建议侧重于运动在使用电感官线索进行目标检测和定位中的作用。我们的方法是双管齐下的，结合了行为分析和单个神经元电生理学，在一个多学科的环境中。我们将开发一种新的系统来创建虚拟电感场景，用于测试行为研究中的对象检测。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交互如何影响单个神经元的运动处理。通过这些不同的方法产生的多学科培训基础将是所有级别的学生的理想选择，为他们在生物技术和高科技以及学术界的一系列职业生涯做好准备。

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.