best computational neuroscience phd programs

Neuroscience Institute

Ph.d in neural computation.

Computational neuroscience is an area of brain science that uses technology to develop and analyze large data sets that are used to understand the complexities of neurobiological systems. In recent years, these methods have become more and more vital to the field of neuroscience as a whole. The use of quantitative methods in neurophysiology has led to important advances, and there has been a continuing stream of related work within mathematics and applied physics. More recently, engineers, computer scientists, and statisticians have contributed to the field, further expanding the definition of computational neuroscience. At the same time, the number of investigators with requisite skills who are actively engaged in this domain of research is relatively small. There is a widely recognized need for increased training in the application of computational, mathematical, and statistical methods to biology and medicine, and to problems in neuroscience in particular.

The Ph.D. Program in Neural Computation seeks to train new scientists in the field. The environment at Carnegie Mellon University and the University of Pittsburgh has much to offer to students interested in computational approaches and it is a perfect home for the Ph.D. Program in Neural Computation. The neuroscience community in Pittsburgh is known for being particularly strong in computation. The program also offers joint Ph.D. degrees with Machine Learning and Statistics .

This program is designed to attract students with strong quantitative backgrounds and to train them in quantitative disciplines relevant to neuroscience and also to provide them the essential background in experimental neuroscience.

In doing so, we leverage the special strengths of our institution and the unique neuroscience community here in Pittsburgh. Training faculty and courses will be drawn both from CMU and Pitt as described. The PNC PhD program is designed for students with backgrounds in computer science, physics, statistics, mathematics, and engineering who are interested in computational neuroscience, particularly with an emphasis on quantitative methods from computer science, machine learning, statistics and nonlinear dynamics.

The program consists of the following core activities:

Coursework in computational neuroscience, quantitative methodologies and experimental neuroscience
Research milestone presentations
Exposure to experimental approaches through rotations or thesis research
Training in teaching, scientific presentations and responsible conduct of research
Successful defense of a PhD Thesis

Additional satellite activities through the CNBC will also foster students’ professional and scientific development. Read more about the curriculum .

The PNC program is overseen by the PNC training faculty, the Academic Program Manager, and the Program Directors. Questions about any aspect of the program should be directed either to the Academic Program Manager, Melissa Stupka , or one of the Program Directors: Steve Chase at CMU and Gelsy Torres-Oviedo at University of Pittsburgh.

Joint Programs

PNC/ Machine Learning
PNC/ Statistics
M.D.-Ph.D. Program

The CMU Rales Fellow Program is dedicated to developing a diverse community of STEM leaders from underrepresented and under-resourced backgrounds by eliminating cost as a barrier to education. Learn more about this program for master's and Ph.D. students. Learn more

Diversity in Neuroscience

CMU Diversity, Equity, and Inclusion
Dietrich College Diversity and Inclusion
Mellon College of Science Diversity
CMU Rales Fellows Program

Neural Computation Contacts

Academic program manager.

Melissa Stupka Mellon Institute 116C [email protected]

Program Director (CMU)

Steve Chase Professor, Biomedical Engineering & Neuroscience Institute Carnegie Mellon University Mellon Institute 115N [email protected]

Program Director (Pitt)

Gelsy Torres-Oviedo, Ph.D. Associate Professor, Bioengineering University of Pittsburgh Schenley Place, Room 115 [email protected]

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Graduate Programs

Computational neuroscience.

"The Computational Neuroscience program here at UChicago has provided me with the knowledge, training, and confidence to pursue my ambitions. The rigorous coursework perfectly addressed my weaknesses, coming from a non-computational background. My network of mentors, friends, and staff have also greatly fostered and supported my success as a graduate student."

Caleb Sponheim

PhD candidate in the lab of Nicholas Hatsopoulos

Computational Neuroscience: Quantitative approaches to studying nervous system function

The University of Chicago has a long tradition of innovative research in the neurosciences. K. C. Cole developed the voltage clamp here, Stephen Polyak and C. J. Herrick did pioneering work on the anatomy of the retina and brain, and Jack Cowan and Hugh Wilson were among the first to develop mathematical analyses of the dynamics of cortical neurons using nonlinear dynamics.

This tradition is continued in the Committee on Computational Neuroscience, which draws on faculty from many departments in all four graduate divisions in the University to create a multidisciplinary program in neuroscience. Computational neuroscience is a relatively new area of inquiry that is concerned with how components of animal and human nervous systems interact to produce behaviors. Using quantitative and modeling methods, the interdisciplinary approach of computational neuroscience seeks to understand the function of the nervous system, natural behaviors and cognitive processes and to design human made devices that duplicate behaviors.

Course work in computational neuroscience prepares students for research in neurobiology, psychology, or in the mathematical or engineering sciences. Graduates from this program move to traditional academic careers, to careers in biomedical research or engineering, or to opportunities in the corporate world.

Faculty in Computational Neuroscience Program Website

Current Students

Multi-disciplinary

PhD Program in Computational Neuroscience

The Graduate Program in Computational Neuroscience (CNS) provides an interdepartmental and interdivisional focus for innovative multidisciplinary training in neuroscience.

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Neuroscience Institute
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This tradition is continued in Graduate Program in Computational Neuroscience, which provides an interdepartmental and interdivisional focus for multidisciplinary training in neuroscience.

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Welcome from the Chair

"I'm proud of the history of excellence in Computational Neuroscience here at UChicago and I'm excited about the future of our field, especially when it is populated with our exceptional graduates."

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See our upcoming events.

Neuroscience Student Talks Monday, April 29th, 12:00 pm Ziqi Wang - CNS (Oswald Lab) Lai Wei - CON (Maunsell Lab) SBRI J461

Museum talks give UChicago graduate students’ research a new audience
Olivia Lutz and Rory Cooley receive NRSA fellowships
CNS and CON graduate students awarded NSF GRFP

Emory University Laney Graduate School Neuroscience Graduate Program

Home » Overview » Computational Neuroscience

Computational Neuroscience

Seeking a degree in Computational Neuroscience?

The graduate program in Neuroscience at Emory University is an interdisciplinary program, spanning many departments and priding itself on a collaborative atmosphere encouraging excellence. Our faculty and students have a broad scope of research interests within neuroscience, ranging from molecular to cellular to behavioral neuroscience.

Our program is one of eight Ph.D. programs that comprise the Emory Graduate Division of Biological and Biomedical Sciences (GDBBS). There are over 260 faculty members in the Division, and graduate students of any program in the Division face no departmental barriers. They can do laboratory rotations and research with any of the Division Faculty. The can also switch to one of the other programs, have an advisor from another program and take any course offered by the Division. This structure gives students tremendous flexibility in choosing coursework, advisors and research plans. There are currently 98 students enrolled in the program, and the average time to finish the degree is about 5.5 years.

Joint Degree Programs

Neuroscience/Biomedical Engineering In 1997, Emory University and the Georgia Institute of Technology joined forces to create a joint Biomedical Engineering Department , which includes 15 Emory faculty members (4 from Neuroscience), and over 25 faculty members from Georgia Tech. The collaboration provides enormous opportunity and intellectual resources for students interested in neuroengineering, neuronal modeling, computational neuroscience and other cutting-edge challenges. Emory has eleven faculty that participate in this joint effort. The Chairman of the new department, Dr. Don Giddens, headed the Johns Hopkins College of Engineering before coming to Emory.
MD/PhD Approximately 10-15% of Neuroscience graduate students are working towards an M.D. degree or already have one. MD/PhD students are admitted through the MD/PhD Program .

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In addition, all students must participate in weekly seminars for the first 2 years of the program. These seminars are informal venues where students present either relevant papers or their own research to their peers and a small group of faculty members. From this experience, students learn presentation and communication skills essential to a career in science.

Related Terms

Behavioral Neuroscience
Electrophysiology Research
Molecular Neuroscience
Neurodegeneration
Neuropharmacology Research
Neurophysiology PhD Graduate Programs
Neuroscience Degree
Neuroscience PhD
Neuroscience PhD Graduate Programs
Proteomics Research
Systems Neuroscience

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PhDs in Neuroscience and Computational Neuroscience

For contact information, please visit the Graduate Program for Neuroscience website .

Program Description

The Graduate Program for Neuroscience (GPN) is a University-wide PhD degree-granting training program in neuroscience that unites the graduate training faculty and students present on our two campuses, the Charles River Campus (CRC) and the Medical Campus (MED). We are a diverse community of faculty, students, and staff who come from multiple departments, schools and colleges, and campuses of the University. Our individual disciplinary interests combine to form a comprehensive research and educational environment that rejects all forms of racism and thrives on our shared excitement for neuroscience.

Faculty administration of the program is delivered by the Program Director in association with the GPN Steering Committee, Graduate Education Committee, and the Computational Neuroscience Curriculum Committee. The research of GPN training faculty covers virtually all areas of neuroscience, from molecular and cellular to systems and computational.

In addition to the PhD in Neuroscience, there is a specialized PhD in Computational Neuroscience for students interested in a more rigorous curriculum in the area. Students pursuing the PhD in Computational Neuroscience get a strong primary training in neuroscience that is shared with their fellow students pursuing the PhD in Neuroscience through the “Core” Curriculum.

An essential feature of the GPN training mission for all students (PhD in Neuroscience and PhD in Computational Neuroscience) is a set of core courses that are aimed at developing a community of thinkers, who move through their training together, building relationships that cross interdepartmental and intercampus barriers, and foster cross-disciplinary collaborations. Most students complete their PhD in roughly 5.8 years.

As members of the unified program, the Neuroscience faculty serve as thesis research mentors and/or knowledge facilitators and work together to help students close any gaps between their knowledge base of individual disciplines as well as their understanding of computational and experimental models. Every effort is expended to provide an individually tailored mentorship and educational program for each student that builds upon their unique strengths and interests, while also recognizing areas that need enrichment through faculty guidance and curriculum choice.

There are four aspects of modern neuroscience that our program addresses:

First , it is becoming increasingly clear that important breakthroughs in the field require ideas, approaches, and techniques originating from many disciplines. The GPN curriculum provides both a broad cross-disciplinary core education including molecular, cellular, and systems; cognitive and behavioral; computational; and clinical neuroscience; and the flexibility to take neuroscience-related coursework in any of the departments and programs of the University to build depth of specialization along different perspectives in a particular area of neuroscience.
Second, a critical aspect of GPN is the formation of a unified group of graduate students from across BU, including the Colleges of Arts & Sciences, Engineering, Health & Rehabilitation Sciences: Sargent College, and BU’s medical school. For the first year of training in GPN, these students take the “core” curriculum courses together, have the opportunity to be involved in common projects, and participate as a community in all Boston University neuroscience activities.
Third , critical to the interdisciplinary focus of the training, is the participation of traditional science departments, which provide a large number of the elective courses and specialized training opportunities to complement the GPN curriculum. Several departments at the Medical Campus (Anatomy & Neurobiology and Pharmacology, Physiology & Biophysics) also offer a joint degree in neuroscience that is coordinated with GPN to further enhance the interdisciplinary nature of the student community.
Fourth , a strong emphasis is placed on building relationships among students and faculty across multiple disciplines to complement the traditional mentorship by the thesis advisor and to provide entry into the neuroscience research/student community of multiple BU schools with alternative scientific perspectives.

The Diverse Student Body

Because students who enter GPN come from diverse backgrounds—including psychology, engineering, biology, chemistry, physics, and mathematics—upon their mutual acceptance into the program, they will be given the opportunity to fill any gaps in their training that might interfere with their ability to do their best in the upcoming core curriculum of their first and second years. This could mean enrolling in a particular summer course(s); taking a summer hands-on laboratory methods section (Tools of the Trade) organized by GPN faculty to introduce basic techniques in molecular or behavioral research; or even structured readings/discussions over the summer with a faculty member that are designed to stimulate a deeper understanding of a core discipline such as biology, biochemistry, or mathematics that might not have been fully emphasized in undergraduate coursework.

It is our belief that with a coherent educational program that embraces multiple complementary attitudes and approaches to scientific inquiry—breadth vs. depth, multidisciplinary vs. traditional discipline, basic vs. clinical science, and experimental approaches vs. theoretical (computational)—there is the greatest opportunity to create a young generation of researchers with sufficient expertise and flexibility to be able to come together and address some of the “big problems” in neuroscience.

Learning Outcomes

By taking a common set of core courses, students will develop an advanced understanding in the diverse field of neuroscience from molecular to cellular and systems to human cognition. The Computational Neuroscience curriculum supplements core neuroscience training with advanced training in a wide array of computational methods for (i) studying the nervous system; (ii) developing neuroscience-related technologies; (iii) and the critical thinking to use this knowledge to conduct rigorous and reproducible scientific discoveries. Students will also develop an appreciation for the human condition by participating in clinical rotations held at the West Roxbury VA Hospital and BMC where they have the opportunity to follow cases in neurology, neurosurgery, and psychiatry.

Experimental Proficiency

Students will develop scientific proficiencies that will permit them to undertake graduate-level research in their area of interest. Students are required to participate in laboratory rotations in their first year to aid in the development of important scientific skills in lab bench or computational research as well as identifying a laboratory for their future thesis research. During their second year, students enroll in electives that enhance their research interests. All GPN students must take the mandated workshop requirement in Responsible Conduct of Research (RCR) that is offered across the University with BU faculty participation.

Analytical Skill Development

Students will develop quantitative skills that permit the analysis of data in their field of research. First-year students begin their analytical skill development with a seven-week intensive introductory course in Computational Neuroscience using Python that emphasizes data analysis and mathematical modeling for students regardless of where they are in their use of quantitative and programming skills. Subsequently, students further develop these skills by enrollment in a course in probability and statistics that is relevant to their research. Mentors work with students to help them to develop the quantitative and analytical skills particular to their research areas.

Communication

Students will be able to communicate their research and that of the work of others orally and in written formats. This proficiency is developed via their curriculum in Frontiers in Neuroscience and their written qualifying exam that is in the form of an NRSA fellowship application. After students pass their qualifying exam, they are required to present seminars to the larger graduate neuroscience community annually. Roughly one year prior to their defense, students present a longer progress report seminar to the full neuroscience community in a formal setting. Students regularly present posters and papers for campus events as well as at national conferences such as SfN.

As the Graduate Program for Neuroscience is a cross-University program, significant emphasis is placed on building relationships that cross inter-departmental and inter-campus barriers and foster cross-disciplinary collaborations. Students are expected to show good citizenship by volunteering in ways that support the local community through outreach activities such as bringing science activities to the local public schools, mentoring activities of fellow students, and cultural enrichment for our own community through events like the Neural Arts Forum. We also encourage participation in our student groups (NGSO and CNSO) that help to sustain the student community via social and educational interactions.

Curriculum Overview

Most students take 28 credits of required study that includes laboratory rotations and clinical rounds, as described below, and fulfill the 64-credit post-bachelor’s, or the 32-credit post-master’s, requirement for the PhD by participating in the student seminar series, attending GPN-sponsored activities such as the distinguished lecture series and the neuroscience retreat, and from directed study with their thesis research mentor and GPN faculty facilitators.

In the first year, students take 18 credits of core coursework (courses taken together as an entering class) that cover the diverse field of neuroscience, from molecular to cellular and systems to human cognition (12 credits), an introductory course in computational modeling that is tuned to the specific background of individual students (2 credits), and Frontiers in Neuroscience (4 credits) where they share lunch every week with a member of the broad group of faculty that make up the neuroscience community here at BU; develop important oral presentation skills; and learn to critically evaluate the literature in their field of interest as well as in areas outside of their earlier academic and research training. Here they also develop important writing skills through faculty and peer mentoring and acquire the basic skills to write a compelling Specific Aims section of an individual training grant application.

During the first year, students also receive required credit (2–4 credits) for participating in laboratory rotations that help them develop important skills in lab bench research as well as identify a laboratory for future thesis research.

Second Year

During their second year, students choose elective curriculum (12 credits) that enhances their research interests (some of our elective curriculum is organized into discipline-specific training opportunities that enable our students to receive T32 support, see below), develop an appreciation for the human condition by participating in a unique opportunity to observe clinical cases in neurology, neurosurgery, and psychiatry (1–2 credits), and take an elective in probability and statistics that is relevant to their research. Together throughout their time in GPN they also take the mandated workshop requirement in Responsible Conduct of Research (RCR) that is offered across the University with BU faculty participation and have access to multiple GMS and faculty-organized workshops in grant writing and professional development. GMS is especially proud of its accomplishments in being able to deliver an exceptional professional development curriculum, having received the BEST award from NIH in 2018.

Computational Neuroscience

For those students wanting to specialize in computational neuroscience , there is additional required study that leads to the Doctor of Philosophy (PhD) in Computational Neuroscience. Computational neuroscience students take their first-year “core” classes with all GPN students and a minimum of two (rather than three) laboratory rotations, with at least one that gives them the experience of experimental research. Additional rotations can be arranged if a student wants to do more and this is encouraged by GPN leadership.

Additional Curriculum

All students have the option of taking additional academic coursework rather than using directed study credits with the thesis mentor to make up the 64-credit requirement for the degree, especially as needed based upon their research interests or to supplement a lack of certain background during undergraduate study.

The goal for the majority of students will be to complete core requirements and to choose the laboratory for their thesis research by the end of the first year. Course requirements for elective study will most likely be completed by the end of the second year. All efforts will be made to tailor the training program to the individual goals of the student, taking into account their previous training experiences either at the undergraduate or master’s level. GPN committees will continually evaluate, expand, and redesign core coursework and choices of advanced electives in order to offer students the best curriculum available across the University.

Core Courses

An essential feature of the program is a set of “core” courses: these are taken by all students in GPN (Neuroscience & Computational Neuroscience) during their first year and are aimed at developing a community of thinkers who move through the training program together, building relationships that cross departmental and campus barriers, and foster cross-disciplinary collaborations.

Students complete 12 credits of “core” neuroscience coursework that provides a strong foundation in this diverse field of graduate study. The fall semester course Systems Neuroscience I (4 credits) is a team-taught lecture/discussion course that meets on alternate days on the Charles River Campus and the Medical Campus. The curriculum engages students to develop basic skills in critical thinking as well as basic principles of brain function, neuroanatomy, and the cellular and molecular neurobiology that will be essential for them as they move into the spring semester integrated curriculum (8 credits) that critically evaluates the use of novel technologies, model vertebrate/invertebrate systems, computational models, and studies with human subjects, with the goal of providing the most up-to-date thinking that can elaborate on the function and dysfunction of the human brain.

GMS NE 700 Principles of Neuroscience I: From Molecules to Systems (4 cr)
GRS NE 741 Neural Systems I: Functional Circuit Analysis (4 cr)
GRS NE 742 Neural Systems II: Cognition and Behavior (4 cr)

Additional “core” neuroscience requirements include: a seven-week intensive introductory course in data analysis and mathematical models for students regardless of where they are in their use of quantitative and modeling skills. This introductory course combines lectures and hands-on computer time to treat real laboratory data like case studies and motivates students to use the mathematical approach as a means to better understand their own research via statistical data analysis and modeling.

GRS MA 665 An Introduction to Mathematical Models and Data Analysis in Neuroscience (2 cr)

Students pursuing the PhD in Computational Neuroscience (or who have taken an undergraduate course in the area) can substitute a more advanced elective for this requirement. Likewise, students who have taken the required course and would like more exposure to the area can continue on in the class to take the next module that is offered sequentially (4 credits instead of 2 credits).

Additional Required Curriculum

In addition to the core curriculum, students take the following seminar coursework during their first year and enroll in laboratory rotations:

GRS NE 500/501 Frontiers in Neuroscience (4 cr)

During the first semester, students attend a unique weekly journal club and professional development class that is run by the GPN Director over lunch on Fridays (fall and spring of Year 1). In the fall semester, students are assigned key papers from a BU faculty member’s laboratory and supporting manuscripts in the field. The particular faculty member, whose research is being reviewed, cohosts the class with the Director. During the two-hour session, student presenters review and critique experimental findings and approaches, building their skills in critical thinking and developing the basic tools for successful oral presentations. They also get to share their scientific ideas and interests with the leaders of neuroscience at Boston University, an activity that enriches our neuroscience community. Research from monthly GPN distinguished lecturers from across the world are integrated into the training experience to provide a balanced exposure for students to all areas of neuroscience and to give them firsthand interactions with exceptional individuals who are defining the field of the future.

In the spring semester, students learn to write a compelling Specific Aims and Approach section for an individual training grant application and develop the peer group skills to help each other grow professionally in both oral and writing exercises. These new skills they will bring to their NRSA and/or NSF application for future research funding in Year 2 and for their written qualifying exam. The course stresses the evolution of critical thinking and the use of constructive criticism to improve the training of their fellow graduate students.

Laboratory Rotations

GMS NE 800/801 Laboratory Rotations (2–4 cr)

Providing an enriching set of laboratory research experiences directed by GPN faculty for students during their first year is a central feature of the neuroscience training program at Boston University. The multitude of highly talented mentors who have funded research projects provides students with a large number of potential laboratories from which to choose the thesis research mentor who will complement their current interests and, through laboratory rotations, expand their horizons into different areas of investigation that they may grow toward in the future. The majority of students pursuing the PhD in Neuroscience take a minimum of three rotations, with at least one rotation in an area outside of their initial research interests; students pursuing the PhD in Computational Neuroscience take a minimum of two rotations, with at least one in an experimental laboratory. Students can also request additional rotations should they not find a mentor, or if they would like more exposure to other methodologies used in neuroscience.

Clinical Rounds (2 credits)

During their second year, all students participate in a unique opportunity to interact with human patients suffering from neuropsychiatric disorders. These experiences take place at the Boston VA supervised by a clinician scientist who is a member of the GPN training faculty.

Hands-On Laboratory Boot Camp/Neuroscience Retreat

Before starting in the training program, the Graduate Education Committee (GEC) reviews the research experiences of each student to determine whether they have had basic training in molecular, behavioral, and/or cognitive research. Based upon their history of undergraduate or post-baccalaureate experiences, they will be advised to take a series of group method sessions called Tools of the Trade, run by faculty in the summer, that provides students with the essential hands-on experience necessary to make their laboratory rotations in the fall meaningful for their graduate-level training. In Tools of the Trade, students learn some of the basic techniques necessary for conducting laboratory research in the field of neuroscience, independent of their current research interests. Students who have already had experience in both molecular and cognitive research can petition to the GEC to waive the requirement and students who are unable to attend during the summer can take the sessions as part of their Laboratory Research Experience class during the fall semester, before beginning laboratory rotations.

For instance, group activities may be organized around detection of an important neuronal RNA via real-time PCR, the identification of a single nucleotide polymorphism in a DNA sample from a patient with a neurodegenerative disease, identification of protein in brain slices using immunohistochemistry and fluorescence microscopy, electrophysiological measurements or calcium imaging of living neurons, interaction of transcription factors with DNA regulatory elements that control expression of neural-specific genes, neuroimaging of the brain to detect the activation of particular brain structures, and running of a behavioral task with animals to address questions of learning and memory. Projects vary with the expertise and interests of the participating GPN faculty.

The entering class in GPN is also invited to the annual GPN Neuroscience Retreat. Every effort is made to schedule the retreat right after the Tools of the Trade so that students are well integrated in our community before arriving in the fall for formal admittance.

Elective Study

The rest of the didactic credits toward the PhD come from elective study (12-credit minimum) that is organized for simplicity into three distinct pathways of emphasis (Molecular & Cellular, Systems, and Computational) to help students choose a relevant curriculum for their interests and choose electives within any area of neuroscience. Those students enrolled in the computational neuroscience training specialization should reference the requirements specific to that curriculum as it applies to required and elective choices. Taking advantage of the translational research and history of clinical training at the MED campus, and rehabilitative Health Sciences at the CRC, students are required to take elective coursework (minimum of 2 credits) and participate in clinical rounds (see above) that provide an exposure to patients and topics relevant to human disease (such as Autism, Alzheimer’s, Drug Abuse, Epilepsy, Parkinson’s, Schizophrenia, and Disorders of Vision, Hearing & Speech). They also take a required elective in probability and statistics that is appropriate to their area of thesis research upon the recommendation of their thesis mentor.

Program Requirements for Continued Student Registration

All students must maintain full-time enrollment each semester. Additional program credits come from Directed Study (GRS NE 901/902) during thesis research with the mentor, and attendance is required at neuroscience ethics and responsible conduct of research (RCR) workshops, at the majority of distinguished lectures, faculty seminars, and program events of the GPN (including student recruitment, the annual Neuroscience Retreat, GPN social gatherings such as the Fall Welcome Reception, the Laboratory Matching Ceremony, and the Holiday Party), and most importantly at GPN graduate student seminars. All students are required to give at least one short presentation annually at the Neuroscience Graduate Student Seminar Series and to fulfill the calendar deadlines of their graduate milestones. Please note that at least one published first author manuscript is required for moving toward the thesis defense.

As members of GPN, students will acquire their more advanced training from coursework offered in departments around the University in order to fulfill the credit requirements for the PhD degree. The following is a list of potential electives organized by topic area as a guide to help students choose their curriculum.

* Medical Campus

Relevant to Molecular, Cellular & Systems (see also Computational)

CAS BI 520 Sensory Neurobiology (4)
CAS BI 545 Neurobiology of Motivated Behavior (4)
CAS BI 575 Techniques in Cellular and Molecular Neuroscience (4)
CAS BI 599 Neurobiology of Synapses (4)
CAS PS 530 Neural Models of Memory Function (4)
GMS AN 702 *Neurobiology of Learning and Memory (2)
GMS AN 709 *Neural Development and Plasticity (2)
GMS AN 804 *Methods in Neuroscience (4)
GMS AN 807 *Neurobiology of the Visual System (2)
GMS BN 798 *Functional Neuroanatomy in Neuropsychology (4)
GMS PM 860 *Electrophysiology and Pharmacology of the Synapse (2)
GMS PM 892 *Molecular and Neural Bases of Learning Behaviors (2)
GRS BI 644 Neuroethology (4)
GRS BI 645 Cellular and Molecular Neurophysiology (4)
GRS BI 655 Developmental Neurobiology (4)
GRS BI 681 Molecular Biology of the Neuron (4)
SAR HS 550 Neural Systems (4)
SAR HS 755 Readings in Neuroscience (4)

Relevant to Biomedical & Translational

CAS BI 554 Neuroendocrinology (4)
GMS AN 707 *Neurobiology of Aging (2)
GMS AN 713 *Autism: Clinical and Neuroscience Perspectives (2)
GMS AN 808 *Neuroanatomical Basis of Neurological Disorders (2)
GMS BN 782 *Forensic Neuropsychology (4)
GMS BN 793 *Adult Communication Disorders (4)
GMS BN 796 *Neuropsychological Assessment I (4)
GMS BN 797 *Neuropsychological Assessment II (4)
GMS BN 821 *Neuroimaging Seminar (2)
GMS BN 891 & 892 *Case Studies in Neuropsychology (three different clinical rounds, sections A1, B1, and C1) (2 credits each section)
GMS BN 893 *Child Clinical Neuropsychology (4)
GMS IM 690 *Imaging of Neurologic Disease (2)
GMS PM 820 *Neuropsychopharmacology (2)
GMS PM 840 *Neuroendocrine Pharmacology (2)
GMS PM 850 *Biochemical Neuropharmacology (2)

Behavioral & Cognitive Neuroscience

CAS PS 520 Research Methods in Perception and Cognition (4)
CAS PS 525 Cognitive Science (4)
CAS PS 528 Human Brain Mapping (4)
CAS PS 544 Developmental Neuropsychology (4)
CAS PS 721 General Experimental (4)
CAS PS 734 Psychopharmacology (4)
CAS PS 737 Memory Systems of the Brain (4)
CAS PS 738 Techniques in Systems & Behavioral Neuroscience (4)
CAS PS 821 Learning (4)
CAS PS 822 Visual Perception (4)
CAS PS 824 Cognitive Psychology (4)
CAS PS 828 Seminar in Psycholinguistics (4)
CAS PS 831 Seminar in Neuropsychology (4)
CAS PS 833 Advanced Physiological Psychology (4)
CAS PS 835 Attention (4)
ENG BE 715 Functional Neuroimaging (4)
GMS AN 716 *Developmental Cognitive Neuroscience (4)
GMS BN 795 *Neuropsychology of Perception and Memory (4)
GRS PS 829 Principles in Neuropsychology (4)

Theoretical & Computational Neuroscience

CAS CN 500 Computational Methods in Cognitive and Neural Systems (4)
CAS CN 510 Principles and Methods of Cognitive and Neural Modeling I (4)
CAS CN 520 Principles and Methods of Cognitive and Neural Modeling II (4)
CAS CN 530 Neural and Computational Models of Vision (4)
CAS CN 540 Neural and Computational Models of Adaptive Movement and Planning Control (4)
CAS CN 550 Neural and Computational Models of Recognition, Memory, and Attention (4)
CAS CN 560 (colisted as BE 509) Neural and Computational Models of Speech and Hearing (4)
CAS CN 570 Neural and Computational Models of Conditioning, Reinforcement, Motivation, and Rhythm (4)
CAS CN 580 Introduction to Computational Neuroscience (4)
ENG BE 509 (colisted as CN 560) Quantitative Physiology of the Auditory System (4)
ENG BE 570 Introduction to Computational Vision (4)
ENG BE 701 Auditory Signal Processing: Peripheral (4)
ENG BE 702 Auditory Signal Processing: Central (4)
ENG BE 707 Quantitative Studies of Excitable Membranes (4)
ENG BE 710 Neural Plasticity and Perceptual Learning (4)
GRS CN 700 Computational and Mathematical Methods in Neural Modeling (4)
GRS CN 710 Advanced Topics in Neural Modeling: Comparative Analysis of Learning Systems (4)
GRS CN 720 Neural and Computational Models of Planning and Temporal Structure in Behavior (4)
GRS CN 730 Models of Visual Perception (4)
GRS CN 740 Topics in Sensory Motor Control (4)
GRS CN 760 Topics in Speech Perception and Recognition (4)
GRS CN 780 Topics in Computational Neuroscience (4)
GRS CS 640 Artificial Intelligence (4)

Coursework in related disciplines:

CAS BI 551 Biology of Stem Cells (4)
CAS BI 552/553 Molecular Biology (4,4)
CAS BI 555 Techniques in Cell Biology (4)
CAS BI 556 Membrane Biochemistry and Cell Signaling (4)
CAS BI 721 Biochemistry (4)
CAS MA 565 Math Models in the Life Sciences (4)
CAS MA 573 Qualitative Theory of Ordinary Differential Equations (4)
CAS MA 581 Probability (4)
CAS MA 582 Mathematical Statistics (4)
CAS MA 583 Introduction to Stochastic Processes (4)
CAS MA 584 Multivariate Statistical Analysis (4)
CAS MA 585 Time Series and Forecasting (4)
CAS MA 684 Applied Multiple Regression and Multivariable Method (4)
CAS MB 722 Advanced Biochemistry (4)
ENG BE 515 Introduction to Medical Imaging (4)
ENG BE 540 Bioelectrical Signals: Analysis and Interpretation (4)
ENG BE 550 Bioelectromechanics (4)
ENG BE 560 Biomolecular Architecture (4)
ENG BE 561 DNA and Protein Sequence Analysis (4)
ENG BE 700 Advanced Topics in Biomedical Engineering (4)
ENG BE 740 Parameter Estimation and Systems Identification (4)
ENG BE 747 Advanced Signals and Systems Analysis for Biomedical Engineering (4)
GMS BI 776 *Gene Targeting in Transgenic Mice (2)
GMS BI 782 *Molecular Biology (4)
GMS BI 786 *Biochemical Mechanisms of Aging (2)
GMS BI 789 *Physical Biochemistry (2)
GMS BI 797 *Molecular Mechanisms of Growth and Development (2)
GMS BL 755/756 *Biochemistry (4,4)
GMS MI 713 *Comprehensive Immunology (4)
GMS MM 701 *Genetics and Epidemiology of Human Disease (2)
GMS MM 703 *Cancer Biology and Genetics (2)
GMS MM 710 *Molecules to Molecular Therapeutics: The Translation of Molecular Observations to Clinical Implementation (4)
GMS PM 800 *Systems Pharmacology (4)
GMS PM 832 *Pharmacogenomics (2)
GMS PM 843 *Pharmacologic Intervention in Inflammatory Responses (2)
GMS PM 880 *Gene Regulation and Pharmacology (2)
GMS PM 881 *Drug Discovery and Development (2)
GRS BI 621/622 Biochemistry (4,4)
GRS BI 735 Advanced Cell Biology (4)
MET AD 893 Technology Commercialization: From Lab to Market (4)