Neural Engineering Laboratory

12 units · Graduate

Spring: 12 units Neural engineering applies classic engineering principles to understand the nervous system and its function. Measuring neural activity relies on a set of tools, developed and refined over decades, that sense bioelectrical signals generated directly by neurons (individual cells, populations of neurons, and nerve fibers) or metabolic signatures linked to neural activity. A comparable set of tools has evolved to modulate the nervous system, changing neural activity locally or globally across short and long timescales. Applying these methods successfully, whether to measure or manipulate neural activity, requires both a grounding in the basic science and engineering principles behind how they work and experience using them in real-world settings. This laboratory course pairs lectures with lab exercises to build a deep, practical understanding of the tools used to measure and manipulate neural activity, along with the analytic approaches used to interpret the resulting data. Students will build and diagnose recording hardware, collect experimental data, analyze it in MATLAB or Python, and practice scientific writing. The overall goal is to give students a deep, working understanding of how experimental data in neuroscience is acquired and interpreted. Familiarity with signal processing, linear algebra, and introductory MATLAB or Python programming is suggested. This course is suitable for students from diverse backgrounds: 1) Students with experimental backgrounds who want to deepen their theoretical understanding and broaden their hands-on experience across new experimental settings and methods, 2) Students with engineering or other quantitative backgrounds who want exposure to experimental data collection methods and practices.