Neuromechanical Models of the Rat Vibrissal System

大鼠触须系统的神经力学模型。Peshkin，西北大学动物使用运动来获取和完善传入的感官数据，以构建有意义的环境表示。这个过程通常被称为“主动感知”。“在探索行为中，动物的每一个动作都有助于提取与任务相关的感觉数据。然而，到目前为止，神经科学家对身体和大脑如何协同工作以获取、编码和处理通过运动产生的感觉数据知之甚少。为了研究主动感知行为背后的神经力学原理，研究人员将基于对一个众所周知的感觉运动系统（大鼠触须阵列）的仔细建模，在硬件中构建一个主动感知系统。大鼠触须系统是研究主动感知行为的理想模型。当探索它们的环境时，老鼠会在空气中来回摆动它们的胡须，并以通常在5到12赫兹之间的频率对着物体。利用这种搅拌行为，老鼠可以提取有关物体空间属性的准确信息，包括大小，形状，方向和纹理。该项目的核心包括（1）表征大鼠胡须和自然搅拌运动的力学，无论是在空气中自由移动时，还是与物体接触时（2）构建一个驱动的仿生（机器人）胡须阵列，底部带有传感器（3）开发模型来解释胡须感觉激活（真实的和机器人）的时空模式，以提取物体特征。这些结果将直接产生关于信息如何在大鼠神经系统中表示的假设，并阐明每年从大鼠体感皮层（barrelcortex）进行的数百次神经记录。本计画开始建立胡须系统中感觉编码的严格数学模型，以推广至其他感觉运动路径。在计算机科学中，这项研究可能会激发使用非光学传感器进行无监督3D物体识别的研究。该项目有助于研究生和本科生的跨学科科学培训，为神经科学带来了定量工程方法，并直接补充了西北大学正在开发的神经工程课程。

Neuromechanical Models of the Rat Vibrissal System Mitra J. Hartmann, Michael A. Peshkin, Northwestern University Animals use movements to acquire and refine incoming sensory data to construct meaningful representations of the environment. This process is often called "active sensing." During exploratory behaviors, each movement an animal makes aids in the extraction of task-relevant sensory data. As yet, however, neuroscientists have little understanding of how the body and brain work together to acquire, encode, and process the sensory data generated through movement. To study the neuromechanical principles that underlie active sensing behaviors, the investigators will construct an active sensing system in hardware based on careful modeling of a well-understood sensorimotor system: the rat vibrissal (whisker) array. The rat whisker system is an ideal model for studying active sensing behaviors. When exploring their environment, rats sweep their whiskers back and forth in the air and against objects at frequencies typically between 5 and 12 Hz. Using this whisking behavior, the rat can extract accurate information about an objects spatial properties, including size, shape, orientation, and texture. The core of the project involves (1) characterizing the mechanics of rat whiskers and natural whisking movements, both when moving freely in air and when in contact with objects (2) constructing an array of actuated, biomimetic (robotic) whiskers with sensors at the base (3) developing models to interpret the spatiotemporal patterns of whisker sensory activation (both real and robotic) to extract object features. The results will directly generate hypotheses about how information is represented in the rat nervous system, and shed light on the many hundreds of neural recordings from rat somatosensory cortex (barrelcortex) that are performed each year. This project begins to establish rigorous mathematical models for sensory encoding in the whisker system that may generalize to other sensorimotor pathways. In computer science, the research may inspire studies on unsupervised 3D object recognition using non-optical sensors. The project contributes to the interdisciplinary scientific training of both graduate and undergraduate students, brings a quantitative engineering approach to neuroscience, and directly complements coursework in Neural Engineering being developed at Northwestern University.