The Science of Behavior (L33 PSYCH 5665 / L41 BIOL 5665)
The primary function of nervous systems is to control behavior. Understanding the links between brain and behavior requires an understanding of cognition—the computations performed by the brain, as well as the algorithms underlying those computations and the physical substrates that implement those algorithms. The goal of this course is to introduce students to the tools, concepts, and techniques for the experimental study of cognition and behavior in humans and nonhuman animals. We will focus on cognitive capacities that are well-developed in humans and can be compared with those of other species, to develop an understanding of how evolution shapes cognition and behavior. Students who complete this course will be able to ask questions and form hypotheses about the computations and algorithms underlying cognition and behavior, and to design experiments that test these hypotheses.
Instructors: Professors Jeff Zacks (firstname.lastname@example.org) and Bruce Carlson (email@example.com)
Neural Systems (L41 BIOL 5651)
The goal of this course is to ensure that all CCSN students have grounding in systems-level neurobiology and neuroanatomy. This course was originally a program-specific course devoted to students in the Neuroscience Program, often with single digit class sizes. Over the last 15 years, it has been adapted (in no small part because of CCSN) to be a richer, broader course that services many needs (CCSN students; and non-CCSN students from other PhD programs including Psychology, Engineering, Computer Science, Physics, Movement Science, etc.). Topic areas include: development and organization of the brain; sensory coding and plasticity; neural correlates of perception; circadian rhythms and sleep; motor control; sensorimotor transformations; decision making; navigation; emotion; learning and memory; and language. The course consists of lectures by a small group of faculty, hands-on laboratory work, and discussion sections. Weekly laboratory experiences include human brain dissections, histological examination of brain tissue, physiology labs on oculomotor control, nerve conduction and sensory-motor coding, along with demonstrations of current research methods in various laboratories.
Instructors: Professor Geoffrey Goodhill (firstname.lastname@example.org), Janine Bijsterbosch (email@example.com), and Ashley Morhardt (firstname.lastname@example.org)
Biological Neural Computation (E62 572 / L41 BIOL 5657)
The overarching aim of this course is to ensure all CCSN students share core knowledge of the computations performed in the nervous system, and foundational methods for using formal models to analyze and interpret these computations. This course was originally created for CCSN, but it is now attended by many non-CCSN students. The course is organized around three major sub-goals: (1) to integrate previous math, physical science, biology and engineering studies into a rigorous investigation of the quantitative foundations of neurophysiology; (2) to provide students with quantitative tools essential for systems-level investigation of neural circuits; and (3) to introduce fundamental pattern recognition and machine learning concepts required for neural data analysis. Topics covered include: phase-plane analysis, reduction of Hodgkin-Huxley equations, models of neural circuits, plasticity and learning, artificial neural networks, and pattern recognition & machine learning algorithms for analyzing neural data. Computations performed by neural circuits and population-level encoding/decoding are particularly emphasized. For non-engineers, conceptual understanding, evaluation and selection of appropriate neural data analysis technique is stressed. Through interaction with previous CCSN cohorts, particularly those from the Psychology program, CCSN has developed two enrichments: a “crash course” in Matlab programming prior to the start of the term (see below) and a programming tutor specifically dedicated to Biological Neural Computation.
Instructor: Professor Barani Raman (email@example.com)