How do genes and neural circuits encode behavior? How have genes and circuits evolved to generate the incredible diversity of behaviors we see across the animal kingdom? This course will explore these questions with emphasis on recent advances in the primary literature. Each class will focus on a specific behavior with a lecture introducing what is known about its genetic and neural basis followed by a discussion of a paper that builds on that knowledge to examine how the behavior evolves. A major goal of the class will be to learn how to critique contemporary research, generate new hypotheses, and design experiments to test those hypotheses.

Cognitive neuroscience is a young and exciting field with many questions yet to be answered. This course surveys current knowledge about the neural basis of perception, cognition and action and will comprehensively cover topics such as high-level vision, attention, memory, language, decision making, as well as their typical and atypical development. Precepts will discuss the assigned research articles, pertaining to topics covered in class with an emphasis on developing critical reading skills of scientific literature.

The Neuroscience Research Experience is designed to provide sophomore students with research experience in the labs of individual faculty members. NEU250 is intended to be a credit-bearing P/D/F course. Students will gain research experience in the laboratory of a faculty member in the Princeton Neuroscience Institute. Students are expected to work with their faculty mentor to develop a schedule that involves spending 10 hours per week engaged in research, including attending weekly research meetings and reading research papers. At the end of the semester, students will present their findings to the faculty member and research group.

This course will provide an introduction to the scientific study of sensation and perception, the biological and psychological processes by which we perceive and interpret the world around us. We will undertake a detailed study of the major senses (vision, audition, touch, smell, taste), using insights from a variety of disciplines (philosophy, physics, computer science, neuroscience, psychology) to examine how these senses work and why. We will begin with physical bases for perceptual information (e.g., light, sound waves) and proceed to an investigation of the structures, circuits, and mechanisms by which the brain forms sensory percepts.

This course introduces undergraduate students to modern methods of analysis applied to the activity of single neurons, the synaptic connections between neurons, and the dynamics of networks of neurons underlying learning and decision-making. Methods include intracellular and extracellular recording of neural activity at scales from single to hundreds of neurons; the application of optogenetic approaches to manipulate neuronal function and behavior; and noninvasive measurement of human cognitive information processing using EEG and fMRI. The capstone of the course is a 2-week independent research project designed and carried out by students.

This course will consider how drugs affect the nervous system with a focus on cellular and molecular mechanisms. Both therapeutic and recreational drugs, as well as the intersection between the two, will be considered. The course will begin with basic pharmacological principles followed by an introduction to drug targets in the brain. Following this, each week will focus on a different class of brain signaling molecules in the context of drug effects.

Introduction to a mathematical description of how networks of neurons can represent information and compute with it. Course will survey computational modeling and data analysis methods for neuroscience. Example topics are short-term memory and decision-making, population coding, modeling behavioral and neural data, and reinforcement learning. Classes will be a mix of lectures from the professor, and presentations of research papers by the students. Two 90 minute lectures, one laboratory. Lectures in common between NEU 437/NEU 537.

This course explores the process by which the human brain and all of its cells and connections emerge and develop. Through discussion-based seminars, we traverse major developmental events of the brain, tracing development at prenatal and postnatal stages through the protracted stages of young adulthood and aging. Insights into basic mechanisms of brain development come from animal models, but recent technological advancements, from molecular tools to neuroimaging, enable studies of human brain maturation directly. We examine how brain development is related to evolution, and how typical and atypical development influences behavior.

The purpose of this seminar course is to explore brain function through the lens of food. Why do we get hungry? How do we search for food? Why do we like some tastes and not others? What is the relationship between what we eat and our cognitive function or even our cognitive evolution? We will learn about the life-essential functions of obscure neural circuits and the more obscure processes of commonly known neural circuits. Over the course of the semester, we will move from neuropeptides to cooking culture; from gut-brain interactions via microbes to the importance of teeth on our neuroanatomy.

A survey of experimental & theoretical approaches to understanding how cognition arises in the brain. This complements 501, focusing on the mechanisms responsible for perception, attention, decision making, memory, cognitive & motor control, and planning, with emphasis on the representations involved & their transformations in the service of cognitive function. Source material spans neuroscience, cognitive science, & work on artificial systems. Relevance to neurodegenerative and neuropsychiatric disorders is also discussed. This is the 2nd term of a double-credit core lecture course required of all Neuroscience Ph.D. students.

This lab course introduces students to the variety of experimental and computational techniques and concepts used in modern cognitive neuroscience. Topics include functional magnetic resonance imaging, scalp electrophysiological recording, and computational modeling. In-lab lectures provide students with the background necessary to understand the scientific content of the labs, but the emphasis is on the labs themselves, including student-designed experiments using these techniques. This is the second term of a double-credit core lab course required of all Neuroscience Ph.D. students.

A major component of many careers is mentoring trainees. Mentoring practices profoundly affect the well-being of a team, and consequently, its productivity and success. While there is no magic formula, there are best practices that can help you improve your mentoring and develop a reflective mentoring style. In this course, we discuss these and practice them weekly. Small groups meet for weekly facilitated discussions of what worked and what did not, and help each other solve dilemmas encountered. We also practice self-mentoring through goal-setting and tracking.

Advanced seminar that reflects current research on brain and behavior.

Introduction to a mathematical description of how networks of neurons can represent information and compute with it. Course surveys computational modeling and data analysis methods for neuroscience. Example topics are short-term memory and decision-making, population coding, modeling behavioral and neural data, and reinforcement learning. Classes are a mix of lectures from the professor, and presentations of research papers by the students. Two 90 minute lectures. Lectures in common between NEU 437/NEU 537. Graduate students carry out a semester-long project.

This course explores the neural foundations of social cognition in natural contexts. Highly controlled lab experiments fail to capture and model the complexity of social interaction in the real world. Recent advances in artificial neural networks provide an alternative computational framework to model cognition in natural contexts. In the course, we will review and critically evaluate deep learning models related to visual perception, speech, language, and social cognition, juxtaposing them against conventional cognitive models.