Neuroengineering is an emerging interdisciplinary field with the goal of developing effective, robust devices that interact with the nervous system. These devices may act in closed loop with the nervous system to augment, repair, or even replace aspects of its basic function. Neuroengineering presents a set of interesting computational challenges that may require diverse solutions. For instance, How do we perform efficient computations on large quantities of neural data with severely limited computing resources? What is the best way to incorporate fundamental insights from biology and neural computation into algorithms? I will give a brief overview of neuroengineering, give the context of its computational considerations and constraints, then share some recent work in dimensionality reduction, dynamical models, and sparse sensing by myself and my collaborators to approach these challenges.
Bing Brunton is the WRF Assistant Professor of Biology and Neuroengineering at UW. She is a member of the UW Institute of Neuroengineering (UWIN, uwin.washington.edu), the Center for Sensorimotor Neural Engineering (CSNE, www.csne-erc.org), and a Data Science Fellow of the eScience Institute.