Collaborative Research: Neural Mechanisms of Active Sensing

包括人类在内的动物，通常通过运动来感知周围的世界。例如，为了感知物体的质地，一个人可能会在物体表面移动她的手，而为了测量物体的重量，她可能会把它放在手掌里上下移动。这种利用不同的运动来感知环境的特征被称为主动感知。虽然主动感知在人类行为中很常见，但人们对大脑如何产生和控制这些运动却知之甚少。这个项目的目标是揭示和描述（用数学方程式）大脑主动感知的策略。这将通过研究一种特殊的动物物种——弱电玻璃刀鱼来实现。选择这种动物是因为它具有一系列特性，使其非常适合实验方法。预期的发现将对其他动物（包括人类）的主动感知产生广泛的影响，因为主动感知行为在物种之间是相似的。这项工作将产生广泛的社会影响，包括机器人控制系统的可能转变，以及对大脑的进一步了解，最终可能提高我们对神经系统疾病的理解。此外，这项工作还包括对关键STEM领域有前途的学生进行多学科培训。这项研究的中心假设是，生物体调整主动运动，以调整产生的感觉反馈，以匹配中枢神经系统回路的处理特征。这是一个具有挑战性的问题，因为感觉输入和运动输出是由一个闭环连接的。实验方法克服了这一挑战：(1)利用一个非常适合的模型系统的独特特征，弱电鱼，(2)开发一个闭环行为控制系统，(3)在自由游动的鱼中进行慢性神经生理记录。这种综合方法将能够量化生物体用于产生和调节主动感知运动的神经力学控制策略，识别对主动运动反馈的神经反应的细胞和突触机制，并发现主动运动中的这些变化如何影响中脑回路中的感觉运动整合。

Animals, including humans, routinely use movement to sense the world around them. For example, to sense the texture of an object, a person might move her hand over the surface, whereas to measure the object's weight, she might hold it in her palm and move it up and down. This use of different movements to sense features of the environment is called Active Sensing. Although active sensing is commonplace in human behavior, how the brain generates and controls these movements is poorly understood. The goal of this project is to reveal and describe (in mathematical equations) the brain's strategies for active sensing. This will be achieved by studying a specialized animal species, the weakly electric glass knifefish. This animal was chosen because it has a suite of properties that make it ideally suited for the experimental approach. The expected findings will have broad implications for active sensing in other animals (including humans) because active sensing behaviors are similar across species. This work will have broad societal impacts, including the possible transformation of robotic control systems and enhanced understanding of the brain that may ultimately improve our understanding of neurological disorders. Further this work includes multidisciplinary training of promising students in critical STEM fields.The central hypothesis for this research is that organisms adjust active movements in order to tune the resulting sensory feedback to match processing features of CNS circuits. This is a challenging problem because sensory inputs and motor outputs are linked by a closed loop. The experimental approach overcomes this challenge by (1) exploiting unique features of a well-suited model system, weakly electric fishes, (2) developing a closed-loop behavioral control system, and (3) performing chronic neurophysiological recordings in freely swimming fish. This integrated approach will enable the quantification of neuromechanical control strategies that organisms use to produce and modulate movements for active sensing, identification of cellular and synaptic mechanisms underlying neural responses to feedback from active movements, and discovery of how these changes in active movements affect sensorimotor integration in midbrain circuits.