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.
