This course is the first in a two-course sequence designed to provide a uniform foundation for students engaged in graduate work in the Neurosciences. Its focus is on the molecular, cellular, and developmental aspects of neural function. The course is often team-taught, and in addition to a standard textbook, utilizes a variety of readings, problem sets and research presentations designed to introduce students to the methods and data of contemporary neuroscience research. Some background in genetics and molecular biology is highly desirable. An emphasis on interdisciplinary work, which is characteristic of contemporary neuroscience, is an important feature of the course.
This course is the second in a two-course sequence designed to provide a uniform foundation for students engaged in graduate work in the Neurosciences. Its focus is on systems, behavioral, and cognitive neuroscience. The course is often team-taught, and in addition to a standard textbook, utilizes a variety of readings, oral presentations and research critiques designed to introduce students to the methods and data of these disciplines.
Completion of Neuroscience I would be highly desirable. An emphasis on interdisciplinary work, which is characteristic of contemporary neuroscience, is an important feature of the course.
This course will provide students with an overview of cognitive neuroscience. Topics to be covered in this course include the neural basis for higher aspects of perception, object recognition, attention, reward and motivation, memory, language, executive control, decision-making, social cognition, and consciousness.
This course will cover descriptive and inferential univariate statistics, including correlation, regression, comparing means, non-parametric tests, and analysis of categorical data. Students will learn how to: (1) match specific univariate methods to particular types of research data; (2) compute univariate data analyses using the R programming language; (3) test assumptions and interpret results of statistical analyses; and (4) write up and present statistical findings.
This course will accompany CNS 70100: Statistics. It will focus on the applications of statistical concepts using R or other statistical computing software. As with CNS 70100, it will cover descriptive and inferential univariate statistics, including correlation, regression, comparing means, non-parametric tests, and analysis of categorical data.
This course will provide an opportunity for graduate students to evaluate the strengths and weaknesses of commonly used methods that cognitive neuroscientists use to measure central and peripheral nervous system activity. These methods include single-unit recordings, the lesion method, electroencephalography (EEG) and event-related potentials (ERPs), transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS), functional magnetic resonance imaging (fMRI), and optical imaging.
This course will provide students with an overview of the structure and function of the nervous system and its subdivisions. It will introduce students to the organizational structure of the human brain, including slide material of gross neuroanatomy, cerebral vasculature, spinal organization, and internal structure from medulla to cortex. Functional system mini-lectures are also provided for the sensory and motor systems, the thalamus, hypothalamus, basal ganglia, limbic system, cerebellum and cortex. Neuroanatomical mapping of major neurochemical systems and their receptors is also provided. Course expectations include both visuo-spatial and written fluency of the material.