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.

Much of what we know about the brain systems underlying perception, attention, memory, and language has been first derived from patients with brain lesions or other brain pathology. Despite our advances in functional brain imaging the study of clinical cases in neuropsychology is still important to determine the causal role of certain brain regions in contributing to a given cognitive process.

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.

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.

In this course, we will explore the diverse and complex interactions between the brain and the immune system from the perspective of current, cutting-edge research papers. In particular, we will focus on the molecular mechanisms of these interactions and their role in brain development and function as well as their potential contributions to specific neurological disorders, including autism. In the process, students will learn to read, critically evaluate, and explain in presentations the content of articles from the primary literature. Prerequisites: MOL 214/215.

This course examines behavior, learning, and cognitive capacities with a focus on the cerebellum, a brain structure that is universal to vertebrates. The cerebellum's microcircuit architecture is largely conserved, so that its local information processing can provide a rigorous starting point for analysis. Cerebellar function will be considered in terms of evolution, development, microcircuit physiology, connectomics, long-distance connectivity to the rest of the brain, animal behavior, and human function and dysfunction, including autism. Readings will draw on original literature, and weekly discussions will be led partly by students.

A survey of modern neuroscience that covers experimental and theoretical approaches to understanding how the brain works. This semester builds on 501, focusing on how the circuits and systems of the brain give rise to cognition. The course covers the neural mechanisms responsible for vision, long-term memory, sleep, motor control, habits, decision making, attention, working memory, and cognitive control. How these functions are disrupted in neurodegenerative and neuropsychiatric disorders are also covered. This is the second 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.

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 aims to introduce students to advanced statistical and machine learning methods for analyzing of neural data, with an emphasis on methods derived from regression (supervised) and latent factor (unsupervised) models. Each technique is illustrated via applications to neural datasets. The course has a heavy emphasis on programming, and a substantial portion of the grade comes from homework assignments that involve writing code to implement relevant methods and apply them to data. The course covers methods for analyzing single and multi-neuron spike train data, calcium imaging and fMRI datasets.

The goal of this course is to illuminate how one becomes an individual from the perspective of integrative biology and behavior. We will follow the journey of a single fertilized egg to becoming a parent and starting the life cycle again. The many forms of inheritance (beyond genes) and cultural influences on our biology will be a recurring theme.

This course is an integrative treatment of memory in humans and animals. We explore working memory (our ability to actively maintain thoughts in the face of distraction), episodic memory (our ability to remember previously experienced events), and semantic memory (our ability to learn and remember the meanings of stimuli). In studying how the brain gives rise to different kinds of memory, we consider evidence from behavioral experiments, neuroscientific experiments (neuroimaging, electrophysiology, and lesion studies), and computational models.

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.