Courses
Markus teaches these courses:
Bi/CNS/NB 153 – Brain Circuits
What functions arise when many thousands of neurons combine in a densely connected circuit? Though the operations of neural circuits lie at the very heart of brain science, our textbooks have little to say on the topic. This course explores what is known, and how we will learn more. The emphasis is on experimental science, but theory and computation play essential roles in linking the structure and function of large neural systems. Neural circuits are best understood in brain areas whose basic purpose is well known. So we will begin with a foray into sensory systems and motor systems. In each case we consider what basic functions need to be accomplished and examine neural circuits that implement them. After this survey, we will ask whether the various circuit motifs we encountered are also found in central brain areas, and what role they play there. Here the emphasis will be on sensory-motor integration and learning. Finally we will explore design principles for neural circuits and what constraints have shaped their structure and function in the course of evolution. [Currently paused]
Bi/CNS/NB 154 – Principles of Neuroscience
A new course in fall of 2018! Prerequisites: Bi/CNS/NB 150 or equivalent; this is not a beginner course. Unlike the voluminous textbook with a similar title, this course aims to distill what are the fundamental tenets of brain science. Can we outline ten principles that are characteristic of this discipline? And how does neuroscience connect to other parts of life science, physics, and mathematics? My intent is to use the course as a discussion forum for that purpose. Lectures and guided reading will touch on a broad range of phenomena from evolution, development, biophysics, computation, behavior, and psychology. Students will benefit from prior exposure to at least some of these domains. [Currently paused]
A guiding question for our discussions will be: If you imagine a book of the same title as the course with less than 100 pages, what should be in that book? Here is a teaser of what I consider one of the principles:
Abstraction - In the nervous system all the world events with which we interact are represented by neuronal membrane potentials: sight, sound, smell, touch, speech, movement, sweating, and internal phenomena like thought, dreams, and emotions. This is a truly remarkable step of abstraction, by which phenomena of entirely different physical nature get encoded with the same symbol set of membrane voltages. From that point on the brain can use general purpose devices—namely, neurons and synapses—to create connections between these world events; for example, combining sights and sounds to cause thoughts and speech. We sometimes forget the immense power of this abstraction, but it offers perhaps the best parallel to man-made electronic computers, which similarly represent all world events by voltage signals.
Bi/CNS/NB 195 – Mathematics in Biology
Develops the mathematical methods needed for a quantitative understanding of biological phenomena, including data analysis, formulation of simple models, and the framing of quantitative questions. Topics include: probability and stochastic processes, linear algebra and transforms, dynamical systems, Python programming. [Currently paused]
Bi/CNS/NB 164 – Tools of Neurobiology
Ten professors in nine weeks! Offers a broad survey of scientific methods and approaches in modern neurobiology. The focus is on understanding the tools of the discipline, and their use will be illustrated with current research results. Topics include: molecular genetics, disease models, transgenic and knock-in technology, virus tools, tracing methods, gene profiling, light and electron microscopy, optogenetics, optical and electrical recording, neural coding, quantitative behavior, modeling and theory. [Fall term each year]
CNS/Bi/Ph/CS/NB 187 – Neural Computation
Prerequisites: introductory neuroscience (Bi 150 or equivalent); mathematical methods (Bi 195 or equivalent); scientific programming. This course aims at a quantitative understanding of how the nervous system computes. The goal is to link phenomena across scales from membrane proteins to cells, circuits, brain systems, and behavior. We will learn how to formulate these connections in terms of mathematical models, how to test these models experimentally, and how to interpret experimental data quantitatively. The concepts will be developed with motivation from some of the fascinating phenomena of animal behavior, such as: aerobatic control of insect flight, precise localization of sounds, sensing of single photons, reliable navigation and homing, rapid decision-making during escape, one-shot learning, and large capacity recognition memory. [Spring 2022, 2023]