A crucial part of neuroscience is understanding how function has its foundation in anatomy. This course traces neuroanatomical pathways through the central nervous system. It emphasizes the primate brain, especially the human brain. The course covers how nuclei, ganglia, and layered structures such as cortex are arranged physically in the brain, the fiber pathways by which they connect to each other, and how this connectivity relates to their function. The material will encompass systems within the brain stem, sensory systems, motor systems, higher cognitive systems, and the interconnectivity and interaction of these systems.

An intensive introduction to fundamental topics in neuroscience, including neuronal excitability, synaptic physiology, neural networks, and circuits that mediate perception, action, emotion, and memory. We will examine neuroscience at scales ranging from single neurons, to the activity of small sets of neurons, to the organization of brain and behavior. The course will address broad questions including: How does information enter the brain? What neural pathways transmit these signals? How is information processed and used to construct an internal model of reality? How does the brain choose and execute the correct behavioral response?

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 lecture course will introduce students to the mathematical and computational tools necessary to work with data sets in neuroscience. A primary goal of the course will be to introduce students to key concepts from linear algebra, probability and statistics, and machine learning, with an emphasis on practical implementations via programming. Lectures on each topic will develop relevant mathematical background, derivation of basic results, and examples of applications. The course will include problem sets requiring programming in Python. No prior programming experience is required, though it will certainly be helpful.

Innate behaviors, whether fleeing from predators, looking for mates, defending territory, or caring for young, are the foundation of life. How does the brain generate these complex behaviors across the lifespan of an individual? What are the links between hormone systems and the generation of survival behaviors? We will look at a range of social and nonsocial innate behaviors, and examine their relationship to neuroethology, endocrinology, and to an emerging understanding of mammalian subcortical circuits for survival. A combination of lecture and student-led deep paper reads and emphasis on modern methods for neural circuit analysis.

This seminar course will explore the interaction of viral infections and the human nervous system. Topics will include both direct effects of neurotropic viruses affecting the central and peripheral nervous systems and indirect effects of infection on these systems (e.g., rabies encephalitis, Covid-19 brain fog, EBV and multiple sclerosis). The course will be discussion based, focused on primary literature from a multidisciplinary perspective - considering the function of neural circuits and systems, mechanisms of neuroinvasion, and viral pathogenesis.

The goal of this course is to introduce students to the field of motor control from an interdisciplinary and comparative biological perspective. We will focus on how organisms move through a complex, unpredictable environment. Major topics will include muscle and limb control, how animals build and execute a motor program, and how they incorporate sensory feedback into that motor program. We will use examples from both vertebrate and invertebrate systems and look across scales of biological organization. The class will be a mix of the occasional lecture and discussion of primary literature.

This course will provide an overview of the major epigenetic mechanisms of gene regulation and the research tools that are used to study epigenetic modifications in different model systems, including humans. We will explore various topics in molecular and behavioral neuroscience including: developmental sensitive periods during for epigenome disruption by environmental factors, the role of epigenetic mechanisms in the dynamic regulation of adult brain function, epigenetic dysregulation in psychiatric disorders, and the controversial hypothesis that environmentally-induced epigenetic modifications can be heritable.

Computational psychiatry is an emerging field of research that strives to leverage recent discoveries in the computational basis of high-level cognitive functions in order to understand, diagnose, and treat mental illness. Psychiatry is the only field of medicine where there are currently no laboratory tests, due in part to a lack of understanding what is the biological basis of symptoms. Computational theories of the brain's mechanisms for evaluation and decision may provide a foundation for such an understanding, and tasks measuring their function can offer objective measures. This seminar will discuss recent findings in this field.

Among the deepest mysteries in neuroscience are the questions of how brain activity results in conscious awareness and to what extent our actions are truly free. Students may be surprised to learn how much modern neuroscience has to say on these topics. We will rigorously define these phenomena and discuss results from neuroscience. We will describe recent theories about the function of major brain divisions like the neocortex, thalamus, and basal ganglia as well as relevant circuits in the hypothalamus and tegmentum. We will synthesize knowledge of these brain circuits to explore new theories of conscious awareness and volition.

A survey of modern neuroscience in lecture format, focusing on brain function from cells and the molecules they express to the function of circuits. The course emphasizes theoretical and computational/quantitative approaches. Topics include cellular neurophysiology, neuroanatomy, neural circuits and dynamics, cell fate decisions, neural development and plasticity, sensory systems, and molecular neuroscience. Students read and discuss primary literature throughout the course. This is one-half of a double-credit core course required of all Neuroscience Ph.D. students.

This laboratory course complements NEU 501A and introduces students to the variety of techniques and concepts used in modern neuroscience, from the point of view of experimental and computational/quantitative approaches. Topics include synaptic transmission and plasticity, two-photon imaging, central neuron activity patterns, optogenetic methods to control neural activity and student-designed special projects. In-lab lectures give students the background necessary to understand the scientific content of the labs but the emphasis is on the laboratory work. Second half of a double-credit core course required of all NEU Ph.D. students.

Advanced seminar that reflects current research on brain and behavior.

Computational psychiatry is an emerging field of research that strives to leverage recent discoveries in the computational basis of high-level cognitive functions in order to understand, diagnose, and treat mental illness. Psychiatry is the only field of medicine where there are currently no laboratory tests, due in part to a lack of understanding what is the biological basis of symptoms. Computational theories of the brains mechanisms for evaluation and decision may provide a foundation for such an understanding and tasks measuring their function can offer objective measures. This seminar discusses recent findings in the field.

This is a graduate-level lecture course covering statistical reasoning and techniques for neuroscience. The focus is on, 1. the foundations of statistical inference (probability theory, linear algebra, and statistical models); 2. hierarchical (mixed effect) general linear models as a framework for both classic techniques and modern extensions; 3. other contemporary methods relevant to neuroscience (including nonparametric and Monte Carlo techniques, Bayesian approaches, and estimating models by maximizing likelihood). There is emphasis on practical exercises with computation using R, and on example applications to neuroscientific data.

Attention is our ability to select information relevant to behavior; focusing our limited cognitive/neural resources on those stimuli and thoughts that are critical to our current task. This course will review the neuroscience of selective attention, from the theoretical foundations provided by cognitive psychology to the neural underpinnings identified by systems neuroscience. The course will present a 'hands on' science experience, combining experimental demonstrations and discussions of current research topics to learn the design and analyses of contemporary experiments in the attention field.

Examination of issues in the responsible conduct of scientific research, including the definition of scientific misconduct, mentoring, authorship, peer review, grant practices, use of humans and of animals as subjects, ownership of data, and conflict of interest.