Course Information

Students interested in Neuroengineering may take classes from a variety of different departments on campus. Please check the UC Davis General Catalog for the most up-to-date information about each of the courses listed below.

Track Name Key  

  • Tech: Neurotechnology & Computational Tools
    • Devices, in vitro models, control algorithms under development state
  • Cog: Cognitive Neuroengineering 
    • Modulation of brain states, cognition, emotions
  • Bionic: NeuroBionics 
    • Prosthetics, brain-machine interfaces
  • Rehab: Human Performance & Rehabilitation
    • Biomechanics, rehabilitation of movement disorders
  • Rx: Neurotherapeutics
    • Device-/molecular therapies for neurological disorders, cancer, etc.
  • General: Relevant to all tracks above  

Relevant courses for each track above are listed in a PDF document accessible here.

The course description below is also available as a PDF at this link. 

 

Lower-Division Undergraduate Courses

  • PHI 005—Critical Reasoning [General] [Fall quarter]
  • Units: 4 (Lecture—3 hours; Discussion—1 hour).
    Criteria of good reasoning in everyday life and in science. Topics to be covered may include basic principles of deduction and induction; fallacies in reasoning; techniques and aids to reasoning; principles of scientific investigation; aids to clarity. Not open for credit to students who have completed PHI 006.
  • PHI 010—Introduction to Cognitive Science [Cog] [Fall Quarter] 
  • Units: 4 (Lecture/Discussion—4 hours).
    Pass One open to Cognitive Science majors only. Introduction to the interdisciplinary cognitive scientific approach to the study of mind, drawing concepts and methods from psychology, philosophy, linguistics, artificial intelligence, and other disciplines. (Same course as CGS 001.)
  • PHI 013G—Minds, Brains, & Computers with Discussion [General] [Fall Quarter]
  • Units: 4 (Lecture—3 hours; Discussion—1 hour).
    Computational theories of the nature of the mind. Mind as a computer process. Possibility of machine intelligence, consciousness, and mentality. Not open for credit for students who have completed PHI 013.
  • PHI 015—Introduction to Bioethics [General] [Fall Quarter]
  • Units: 4 (Lecture—3 hours; Discussion—1 hour).
    Critical analysis of normative issues raised by contemporary medicine and biology. Possible topics include euthanasia, reproductive technologies, genetic engineering, informed consent and patient autonomy, experimentation on human subjects and non-human animals.
  • NPB 017—The Path to Cyborgs: Introduction to Prostheses & Human Machine Interfaces [General] [Spring Quarter]
  • Units: 3 (Lecture—3 hours).
    Interface of biology and technology. Mind-controlled prosthetic limbs, artificial organs, and implantable devices. Emphasis on basic physiological functions and how they can be replaced by devices. Suitable for majors and non-majors.
  • PHI 024—Introduction to Ethics [General] [Fall quarter]
  • Units: 4 (Lecture—3 hours; Discussion—1 hour).
    Reading of historical and contemporary philosophical works in ethics. Topics include the nature of morality, the justification of moral claims, and major ethical theories, such as consequentialist, deontological, and virtue theories.
  • PHI 030—Introduction to Philosophy of Science [General] [Fall quarter]
  • Units: 4 (Lecture—3 hours; Discussion—1 hour).
    Not open for credit to students who have taken PHI 104. Basic problems in the philosophy of science, common to the physical, biological, and social sciences. Analysis of explanation, confirmation theory, observational and theoretical terms, the nature of theories, operationalism and behaviorism, realism, reduction.
  • PHI 038—Introduction to Philosophy of Biology [General]
  • Units: 4 (Lecture—3 hours; Discussion—1 hour).
    Non-technical introduction to philosophical, social, and scientific ideas, methods and technologies in contemporary biological fields such as evolution, genetics, molecular biology, ecology, behavior. Philosophical consideration of determinism, reductionism, explanation, theory, modeling, observation, experimentation. Evaluation of scientific explanations of human nature.

Upper-Division Undergraduate Courses

  • STS 100—Methods in Science, Technology, & Medicine Studies [General]
  • Units: 4 (Lecture/Discussion—3 hours; Extensive Writing/Discussion).
    Prerequisite(s): STS 001 recommended. Methodological approaches for studying science, technology, and medicine in social context. Detailed case studies illustrate different historical, philosophical, sociological, ethical, rhetorical, and political methods of analysis. Only two units of credit for students who have previously taken STS 020.
  • PMR 100—Research Approaches to Disability & Rehabilitation [Rehab, Rx]
  • Units: 2 (Lecture/Discussion—2 hours).
    Discussion and evaluation of research approaches to medical rehabilitation, community integration, and quality of life of disabled persons, with a focus on the progressive disabilities associated with neuromuscular diseases. Intent is to encourage interest in professions that serve the disabled community and increase awareness of rehabilitation goals.
  • UWP 102E—Writing in the Disciplines: Engineering [General] [Fall quarter]
  • Units: 4 (Lecture/Discussion—3 hours; Extensive Writing).
    Prerequisite(s): UWP 001 C- or better or UWP 001V C- or better or UWP 001Y C- or better or ENL 003 C- or better or COM 001 C- or better or COM 002 C- or better or COM 003 C- or better or COM 004 C- or better or NAS 005 C- or better; and upper division standing. Open to upper division students in the College of Engineering and to students enrolled in an upper division engineering or computer science course for the major. Advanced instruction in writing in engineering.
  • NEU/CHA 103—Human Clinical Neuroanatomy [General]
  • Units: 4 (Lecture—3 hours; Laboratory—3 hours).
    Prerequisite(s): CHA 101; or Consent of Instructor. Open to upper division students. Clinically relevant anatomy of the normal human nervous system, including external and internal anatomy of the brain, spinal cord, and cranial nerves. Blood supply to the brain and spinal cord. Functional neuroanatomy of motor, sensory, and cognitive systems. Application of neuroanatomical principles relevant to clinical problem solving for students entering health care professions.
  • EXB 106/CHA 101—Human Gross Anatomy [General] [Winter quarter]
  • Units: 4 (Lecture—4 hours).
    Prerequisite(s): BIS 002A; Concurrent enrollment in EXB 106L or CHA 101L strongly recommended. Upper division students only; Pass One open to upper division Exercise Biology or Anthropology majors only; Pass Two open to Seniors in any major; open enrollment at the start of the quarter for upper division students in any major. Detailed study of the gross anatomical structure of the human body, with emphasis on function and clinical relevance to students entering health care professions. 
  • EXB 106L/CHA 101L—Human Gross Anatomy Laboratory [General] [Winter quarter]
  • Units: 3 (Laboratory—9 hours).
    Prerequisite(s): BIS 002A; EXB 106 (can be concurrent) or CHA 101 (can be concurrent); Must have completed EXB 106 or CHA 101 or required concurrently. Upper division students only; Pass One open to upper division Exercise Biology or Anthropology majors only; Pass Two open to Seniors in any major; open enrollment at the start of the quarter for upper division students in any major; mandatory attendance on first day of lab. Detailed study of prosected human cadavers in small group format with extensive hands-on experience.
  • MAT/BIS 107—Probability and Stochastic Processes with Applications to Biology [General]
  • Units: 4 (Lecture—3 hours; Laboratory—2 hours).
    Prerequisite(s): (MAT 027A C- or better or BIS 027A C- or better) or (MAT 022A C- or better, (MAT 022AL C- or better or ENG 006 C- or better or EME 005 C- or better). Introduction to probability theory and stochastic processes with biological, medical, and bioengineering applications. Combinatorics, discrete and continuous random variables, Bayes’ formula, conditional probability, Markov chains, Poisson processes, and Brownian motion. Computer labs cover mathematical and computational modeling techniques. Only 2 units of credit for students who have completed MAT 135A or STA 131A. 
  • PHI 115—Problems in Normative Ethics [General]
  • Units: 4 (Lecture/Discussion—3 hours; Term Paper).
    Prerequisite(s): One previous course in Philosophy recommended. Moral philosophy studied through examination of moral problems and the moral principles and common sense intuitions that bear on them. Problems discussed may include: animal rights, fetal rights, euthanasia, justice and health care, war, nuclear deterrence, world hunger, environmental protection.
  • PSC 121—Physiological Psychology [General] [Winter quarter]
  • Units: 4 (Lecture—3 hours; Laboratory—3 hours.
    Prerequisite(s): (PSC 001 or PSC 001Y); PSC 041; PSC 101. Pass One open to Psychology majors. Relationship of brain structure and function to behavior, motivation, emotion, language, and learning in humans and other animals. An introduction to the methodology of physiological psychology and neuroscience. Not open for credit to students who have completed former PSC 108.
  • PHI 121—Bioethics [General]
  • Units: 4 (Lecture/Discussion—3 hours; Extensive Writing).
    Prerequisite(s): PHI 015 recommended. In-depth coverage of topics in bioethics including resource allocation, measures of health and disease/disability, public health, and ethical issues related to research on human subjects and emerging technologies.
  • NPB/PSC 124—Comparative Neuroanatomy [General] [Winter quarter]
  • Units: 3 (Lecture—3 hours).
    Prerequisite(s): NPB 101 or NPB 100 or NPB 110B or PSC 121. Overview of the neuroanatomy in mammalian vertebrates, focusing on the cerebral cortex and experimental techniques. Examine changes or modifications to neural structures as a result of morphological or behavioral specializations. 
  • PHE 131—Physical Activity & the Disabled [General]
  • Units: 4 (Lecture—3 hours; Laboratory—3 hours).
    Study of the diverse and complex nature of individuals with disabilities and how they adapt to their disabilities in daily living. Integration of individuals with disabilities into the community, schools, and physical activity and recreational programs. Not open for credit to students who have taken EXB 131.
  • PSC 135—Cognitive Neuroscience: The Biological Foundations of the Mind [General, Cog] [Winter quarter]
  • Units: 4 (Lecture—4 hours).
    Prerequisite(s): (PSC 001 or PSC 001Y); PSC 041; or Consent of Instructor. PSC 101 or PSC 121 recommended. Neuroscientific foundations of higher mental processes including attention, memory, language, higher-level perceptual and motor processes, and consciousness. Emphasis on the neural mechanisms which form the substrates of human cognition and the relationship of mind to brain. May be taught abroad.
  • NPB 136—Neural Networks & Machine Learning in Biology [General, Cog, Tech] [Winter quarter]
  • Units: 4 (Lecture—4 hours).
    Prerequisite(s): MAT 017C C or better or MAT 021B C or better; or consent of Instructor; some background in neuroscience, cognitive science or programming (any one of the three) is recommended. Neural networks as models of brain function and as powerful tools in machine learning. How neuroscience and machine learning have shaped each other. Applications of machine learning tools to biological research and health.
  • NPB 163—Systems Neuroscience [General]
  • Units: 4 (Lecture—3 hours; Lecture/Discussion—1 hour).
    Prerequisite(s): NPB 100 or NPB 110B; Or equivalent basic neuroscience training with consent of instructor. Concepts and techniques in systems neuroscience: e.g., measuring and manipulating neural activity, structure of neocortex, sensory processing, motor control, storage of information, neural codes, neural mechanisms underlying cognitive functions.
  • NPB 165—Neurobiology of Speech Perception [Bionic]
  • Units: 3 (Lecture—3 hours). Prerequisite(s): NPB 110B or NPB 100 or NPB 101; or Consent of Instructor. Interdisciplinary approach to speech perception with emphasis on functional neuroanatomy and behavior. Topics include auditory processing in time and space, intelligibility in noisy environments, visual speech, evolution of vocal communication, models of speech perception, development, and hearing impairment.
  • DES 166—Human Centered Design [General] [Fall quarter]
  • Units: 4 (Studio—6 hours).
    Prerequisite(s): DES 001; (DES 014 or DES 021); DES 015. Pass One restricted to Design majors. Human-centered approach to problem solving, ethnography, ideation, project framing, rapid prototypes, visual communication, and experiential learning. Creative approaches to graphic design, industrial design, fashion, business, and entrepreneurship.
  • NPB 167/NSC 267—Computational Neuroscience [Tech]
  • Units: 5 (Lecture—4 hours; Lecture/Lab—3 hours).
    Prerequisite(s): (NPB 100 or NPB 110B); (MAT 016A, MAT 016B, MAT 016C) or (MAT 017A, MAT 017B, MAT 017C) or (MAT 021A, MAT 021B, MAT 021C); or Consent of Instructor. PHY 007A, PHY 007B or equivalent recommended. Mathematical models and data analysis techniques used to describe computations performed by nervous systems. Lecture topics include single neuron biophysics, neural coding, network dynamics, memory, plasticity, and learning. Lab topics include programming mathematical models and data analysis techniques in MATLAB.
  • DES 167—Prototyping: From Objects to Systems [Tech] [Winter quarter]
  • Units: 4 (Studio—6 hours).
    Prerequisite(s): DES 001; (DES 014 or DES 021); DES 015; or Consent of Instructor. Pass One restricted to Design majors. Exploration of rapid prototyping techniques for objects, interactive experiences, services and organizations. Understanding of additive manufacturing, foam models, digital interfaces and business models.
  • ECS 170—Introduction to Artificial Intelligence [Tech] [Winter quarter]
  • Units: 4 (Lecture—3 hours; Discussion—1 hour).
    Prerequisite(s): ECS 060 or ECS 032B or ECS 036C. Pass One open to Computer Science and Computer Science Engineering Majors only. Design and implementation of intelligent computer systems. Knowledge representation and organization. Memory and inference. Problem solving. Natural language processing.
  • ECS 171—Machine Learning [Tech, Bionic, Rehab] [Fall quarter]

  • Units: 4 (Lecture—3 hours; Discussion—1 hour).
    Prerequisite(s): ECS 060 or ECS 032B or ECS 036C; or Consent of Instructor. Probability equivalent to STA 032 or STA 131A or ECS 132 recommended; linear algebra equivalent to MAT 22A recommended. Pass One open to Computer Science and Computer Science Engineering Majors only. Introduction to machine learning. Supervised and unsupervised learning, including classification, dimensionality reduction, regression and clustering using modern machine learning methods. Applications of machine learning to other fields.

  • BIM 172-Neuroengineering Lab [General] [Winter quarter]
  • Units: 2 (Discussion—1 hour; Laboratory—3 hours). 
    Prerequisite(s): BIM 105; (ENG 100 or EEC 100). Basics of electroencephalography (EEG). Recording EEG signals from the brain. Machine learning tools for brain-computer interface (BCI) techniques. The power of neural signals to improve health outcomes.
  • ECS 174—Computer Vision [Tech, Bionic, Rehab]
  • Units: 4 (Lecture—3 hours; Discussion—1 hour).
    Prerequisite(s): (ECS 060 or ECS 032B or ECS 036C); (STA 032 or STA 131A or MAT 135A or EEC 161 or ECS 132 recommended); (MAT 022A or MAT 067 recommended). Pass One open to Computer Science and Computer Science and Engineering Majors only. Computer vision is the study of enabling machines to "see" the visual world; e.g., understand images and videos. Explores several fundamental topics in the area, including feature detection, grouping and segmentation, and recognition.
  • DES 178—Design & Wearable Technology [Tech]
  • Units: 4 (Studio—6 hours).
    Prerequisite(s): DES 001; (DES 014 or DES 021); DES 015; DES 016; (DES 037 or DES 111); or Consent of Instructor. Pass One restricted to Design majors. Introduction to wearable technology and related technologies. Emphasis on designing, and fabricating prototypes of wearable technology for value-added designs and to improve quality of life.
  • EEC 179—Applied Machine Learning for Electrical & Computer Engineers [Tech] [Spring quarter]
  • Units: 4 (Lecture/Discussion).
    Prerequisite(s): EEC 161. Fundamental techniques in machine learning for data preparation, preprocessing, classification, and regression. Bringing practical machine learning algorithms to the field and deploying them on real problems in hardware, mobile health, embedded systems, security, and other related topics. Course credit limitations: Only 2 units of credit for students who have previously taken ECS 171.
  • ECS 188—Ethics in an Age of Tech [Tech] [Fall quarter]
  • Units: 4 (Lecture - 3 hours; Discussion: 1 hour) 
    Prerequisite(s): Upper division standing. Pass One open to Computer Science Engineering Majors only; Pass Two open to Computer Science and Computer Science Engineering Majors only. Foundations of ethics. Views of technology. Technology and human values. Costs and benefits of technology. Character of technological change. Social context of work in computer science and engineering.
  • ECS 189G—Special Topics in Computer Science: Artificial Intelligence [Tech, Bionic, Rehab]

  • Units: Variable, 1-5 (Lecture; Laboratory; Lecture/Lab).
    Prerequisite(s): Consent of instructor. Open to undergraduate students only. Special topics in Artificial Intelligence. May be repeated for credit when topic differs.

Graduate Courses

  • NSC 200LB—Laboratory Methods in Neurobiology [Tech, Rx] [Winter Quarter]
  • Units: 3 (Laboratory—9 hours).
    Prerequisite(s): Graduate standing in the Neuroscience Graduate Group. Individual research in the laboratory of a faculty member. Research problems emphasize the use of contemporary methods and good experimental design. May be repeated for credit. (S/U grading only.)
  • CLH 204—The Ethics of Research [General] [Fall & Winter quarter]
  • Units: 1 (Lecture—1 hour).
    Prerequisite(s): Consent of Instructor. Priority given to those with acceptance into the Clinical Research Graduate Group, K12, T32 or other SOM/CTSC training program. Acquire information about ethical responsibilities; Explore major questions in ethics; Apply ethical principles, concepts and values; Gain an appreciation of the role of trust in scientific research. Recommend three quarters of CLH204. Must enroll in Fall to continue through Spring. May be repeated up to 3 unit(s). (S/U grading only.)
  • CLH 207—Team Science [General]
  • Units: 1 (Lecture/Discussion—1 hour).
    Prerequisite(s): Participation in CTSC Research Education and Training Programs, or consent of instructor. Restricted to 25 students. Today’s scientific challenges necessitate cross-disciplinary engagement and high collaboration levels. Offers guidance on how best to engage in team science to pursue complex questions, work effectively with team members, and produce high impact research that meets society’s needs. (S/U grading only.)
  • STA 208—Statistical Methods in Machine Learning [Tech, Bionic, Rehab] 

  • Units: 4 (Lecture—3 hours; Discussion/Laboratory—1 hour).
    Prerequisite(s): STA 206; STA 207; STA 135; Or their equivalents. Focus on linear and nonlinear statistical models. Emphasis on concepts, methods, and data analysis; formal mathematics kept to minimum. Topics include resampling methods, regularization techniques in regression and modern classification, cluster analysis and dimension reduction techniques. Use professional level software.

  • PSC 208—Physiological Psychology [Cog]
  • Units: 4 (Seminar—4 hours).
    Prerequisite(s): Graduate standing in Psychology or consent of instructor. A conceptual analysis of the contributions of neuroanatomy, neurophysiology and neurochemistry to an understanding of animal and human behavior.
  • PSC 208A—Fundamentals of Human Electrophysiology [Tech, Cog]
  • Units: 4 (Lecture/Discussion—1.5 hours; Laboratory—3 hours; Extensive Problem Solving—1.5 hours; Project (Term Project)—3 hours).
    Prerequisite(s): Consent of Instructor. Restricted to 15 students. In-depth introduction and hands-on experience with the event-related potential (ERP) method in the study of attention, executive control, memory, language and social cognitive neuroscience.
  • CLH 208—Introduction to Grant Writing, I [General] [Fall quarter]
  • Units: 2 (Lecture/Discussion—2 hours); Extensive Writing.
    First in a two-quarter series. Scholars are encouraged to enroll in both classes. The two-course sequence provides training in practical aspects of competitive grant writing. The focus is NIH, but information will apply to other funding agencies. (S/U grading only.)
  • CLH 209—Introduction to Grant Writing, II [General] [Winter quarter]
  • Units: 1 (Lecture/Discussion—1 hour).
    Prerequisite(s): CLH 208; Consent of Instructor. Restricted to students who have completed CLH 208. Second in a two-quarter series. Two-course sequence provides training in practical aspects of competitive grant writing. (S/U grading only.)
  • STA 209—Optimization for Big Data Analytics [Tech, Bionic, Rehab] [Fall quarter]

  • Units: 4 (Lecture—3 hours; Discussion—1 hour).
    Prerequisite(s): STA 200A; STA 208. Optimization algorithms for solving problems in statistics, machine learning, data analytics. Review computational tools for implementing optimization algorithms (gradient descent, stochastic gradient descent, coordinate descent, Newton’s method.) ​​​​​​

  • NSC/NPB/PSC 211-- Advanced Topics in Neuroimaging [Tech]
  • Units: 3 (Seminar—2 hours; Laboratory—1 hour). Prerequisite(s): PSC 210; or Consent of Instructor. Restricted to 16 students. Critical presentation and discussion of the most influential advanced issues in neuroimaging, emphasizing fMRI design/analysis and the integration of fMRI with EEG/MEG. May be repeated for credit when topic differs.
  • CLH 214A—Biodesign I [General]
  • Units: 2 (Lecture—2 hours).
    Prerequisite(s): Consent of Instructor. Prior approval by instructor required; student must commit to taking both courses; Biodesign I and Biodesign II. Focuses on the principles of needs identification and invention of biomedical technologies. Two part course provides a basic understanding of the elements of the innovation process and how to translate these principles into biomedical device design.
  • CLH 214B—Biodesign II [General]
  • Units: 2 (Lecture—2 hours).
    Prerequisite(s): CLH 214A; Consent of Instructor. Prior approval by instructor required; student must commit to taking both courses; Biodesign I and Biodesign II. Focuses on the implementation of biomedical technologies and translational process. Two part course provides a basic understanding of the elements of the innovation process and how to translate these principles into biomedical device design.
  • PTX/MCP 215—Electrophysiology Techniques and Applications [Tech, Bionic, Rx]

  • Units: 3 (Lecture—1.5 hours; Discussion—1.5 hours).
    Broad scope of topics in electrophysiology techniques and applications. (S/U grading only.) ​​​​​

  • NSC 219—Design to Data: Statistics for Modern Neuroscience [General]
  • Units: 3 (Lecture—3 hours). Prerequisite(s): graduate student standing in neuroscience or related discipline or consent of instructor. Statistical methods and applications for neuroscience. Quantitative foundations covering key concepts, methods, and applications, from descriptive analysis through data science. Statistical considerations in experimental design, analysis and statistical testing in hypothesis and data-driven contexts, and responsible conduct of research in the acquisition, storage, analysis, and presentation of scientific data.
  • NCS/NPB 222—Systems Neuroscience [Tech, Bionic, Rehab] [Winter quarter]

  • Units: 5 (Lecture—4 hours; Discussion—1 hour).
    Prerequisite(s): Graduate standing or consent of instructor. Integrative and information-processing aspects of nervous system organization. Topics include sensory systems, motor function, sensorimotor integration, the limbic system, and the neurobiology of learning and memory.
    Note: NSC/NPB 222 is a core course for Neuroscience graduate students, very intensive. NPB 163 may be a better alternative for non-Neuroscience graduate students.

  • NSC 223/ PSC 261—Cognitive Neuroscience [Cog]
  • Units: 4 (Lecture—3 hours; Discussion—1 hour).
    Prerequisite(s): Graduate student standing in Psychology or Neuroscience or consent of instructor. Graduate General course for neuroscience. Neurobiological bases of higher mental function including attention, memory, language. One of three in three-quarter sequence.
  • BST 227—Machine Learning in Genomics [Tech, Bionic, Rehab]

  • Units: 4 (Lecture/Discussion—3 hours; Project (Term Project)).
    Prerequisite(s): STA 208 or ECS 171; or consent of instructor. Emerging problems in molecular biology and current machine learning-based solutions to those problem. How deep learning, kernel methods, graphical models, feature selection, non-parametric models and other techniques can be applied to application areas such as gene editing, gene network inference and analysis, chromatin state inference, cancer genomics and single cell genomics.

  • EEC 244—Introduction to Neuroengineering [Tech, Cog, Bionic, Rehab, Rx, General] [Winter quarter]
  • Units: 4 (Lecture/Discussion)
    Prerequisite(s): Graduate standing or consent of instructor.
    This team-taught interdisciplinary course will introduce students to the key research areas and tools in neuroengineering. Survey of neuroengineering field from engineering and biological perspectives; micro-/nano-fabrication technology; optical and electrical techniques to monitor and modulate neural activity; computational tools and control systems; prosthetics and human machine interfaces; human performance and rehabilitation; cognitive neuroengineering; neuroethics; extensive proposal development to merge aforementioned themes into a multidisciplinary project.
  • EEC/EMS/ECH/MAE 245—Micro- and Nano-Technology in Life Sciences [Tech, Rx]
  • Units: 4 (Lecture/Discussion—4 hours).
    Prerequisite(s): Graduate standing or consent of instructor. Survey of biomedical device design from the engineering and biological perspectives; micro-/nano-fabrication and characterization techniques; surface chemistry and mass transfer; essential biological processes and models; proposal development skills to merge aforementioned themes in a multidisciplinary project.
  • BME 248—Multi-modal Neuroimaging Techniques [Tech]
  • Units: 4 (Lecture/Project—4 hours).
    Prerequisite(s): BIM 108, BIM 142. Neuroimaging techniques including magnetic resonance imaging (MRI) and positron emission tomography (PET) and their multi-modal applications in neuroscience and neurological disorders. Imaging methods and brain biomarkers. Software and coding experience to analyze imaging datasets of brain structure, function, and pathology.
  • MAE 252—Information Processing for Autonomous Robotics [Bionic, Rehab]

  • Units: 4 (Lecture—3 hours; Discussion—1 hour).
    Prerequisite(s): EME 154; EME 171; ENG 006; EME 005; Or equivalent programming experience to ENG 006 and EME 005. MAE 154, MAE 171, or consent of instructor. Computational principles for sensing, reasoning, and navigation for autonomous robots.

  • BIM 254—Statistical Methods in Genomics [Tech, Bionic, Rehab]

  • Units: 4 (Lecture—4 hours).
    Statistical approaches to problems in computational molecular biology and genomics; formulation of questions via probabilistic modeling, statistical inference methods for parameter estimation, and interpretation of results to address biological questions; application to high-impact problems in functional genomics and molecular biology.

  • NSC/NPB 267/ NPB 167—Computational Neuroscience [Tech, Bionic, Rehab]
  • Units: 5 (Lecture—4 hours; Lecture/Lab—3 hours).
    Prerequisite(s): (NPB 100 or NPB 110B); (MAT 016A, MAT 016B, MAT 016C) or (MAT 017A, MAT 017B, MAT 017C) or (MAT 021A, MAT 021B, MAT 021C); or Consent of Instructor. PHY 007A, PHY 007B or equivalent recommended.
    Mathematical models and data analysis techniques used to describe computations performed by nervous systems. Lecture topics include single neuron biophysics, neural coding, network dynamics, memory, plasticity, and learning. Lab topics include programming mathematical models and data analysis techniques in MATLAB. 
  • ECS 270—Artificial Intelligence [Tech, Bionic, Rehab]
  • Units: 3 (Lecture—3 hours).
    Prerequisite(s): ECS 140A; ECS 172. Pass One and Pass Two open to Graduate Students in Computer Science only.
    Concepts and techniques underlying the design and implementation of models of human performance on intelligent tasks. Representation of high-level knowledge structures. Models of memory and inference. Natural language and story understanding. Common sense planning and problem solving. 
  • ECS 271—Machine Learning and Discovery [Tech, Bionic, Rehab]
  • Units: 4 (Lecture—3 hours; Project (Term Project)-1 hour).
    Prerequisite(s): ECS 170. Pass One and Pass Two open to Graduate Students in Computer Science only.
    Artificial intelligence techniques for knowledge acquisition by computers. Fundamental problems in machine learning and discovery. Systems that learn from examples, analogies, and solved problems. Systems that discover numerical laws and qualitative relationships. Projects centering on implementation and evaluation. 
  • MAE 272—Theory and Design of Control Systems [Tech, Bionic, Rehab]
  • Units: 4 (Lecture—4 hours).
    Prerequisite(s): EME 172; Or the equivalent.
    Mathematical representations of linear dynamical systems. Feedback principles; benefits and cost of feedback. Analysis and design of control systems based on classical and modern approaches, with emphasis on applications to mechanical and aeronautical systems. 
  • BME/BIM 280-Design of Neural Control Systems [Tech, Rx] [Winter quarter]
  • Units: 4 (Lecture—4 hours).
    Select and use machine learning tools to analyze neural data. Knowledge of the definitions and fundamental principles of data analytics related to neural data including field potentials (EEG, iEEG, local field potentials, EMG) and single neuron or muscle action potentials. Neural decoding/encoding, how to apply classifiers, regression and dimension reduction techniques, factor analysis and dynamic modeling.
  • NPB/NSC 287A—Topics in Theoretical Neuroscience [General] [Fall quarter]
  • Units: 2 (Lecture/Discussion—2 hours).
    Prerequisite(s): Consent of Instructor.
    In-depth exploration of topics in theoretical neuroscience. Topic varies each year. Fall quarter (287A): foundational material from books and review articles. Spring quarter (287B): continuation of year's topic through readings of seminal articles from the primary literature. May be repeated for credit. (S/U grading only.) 
  • NPB/NSC 287B—Topics in Theoretical Neuroscience [General] [Winter quarter]
  • Units: 2 (Seminar—2 hours).
    Prerequisite(s): Consent of Instructor.
    In-depth exploration of topics in theoretical neuroscience. Topic varies each year. Fall quarter (287A): foundational material from books and review articles. Spring quarter (287B): continuation of year's topic through readings of seminal articles from the primary literature. May be repeated for credit. (S/U grading only.) 
  • BIM 289A—Selected Topics in Biomedical Engineering
  • Units: Variable (1-5 hours).
    Prerequisite(s): Consent of Instructor.
    Selected topics in Cellular and Molecular Systems Engineering. May be repeated for credit when topic differs. 
  • BIM 289B—Selected Topics in Biomedical Engineering; Biomedical Imaging [Tech, Rx]
  • Units: Variable (1-5 hours).
    Prerequisite(s): Consent of Instructor.
    Selected topics in Biomedical Imaging. May be repeated for credit when topic differs. 
  • BIM 289C—Selected Topics in Biomedical Engineering; Computational Bioengineering [Tech, Bionic, Rehab]
  • Units: Variable (1-5 hours).
    Prerequisite(s): Consent of Instructor.
    Selected topics in Computational Bioengineering. May be repeated for credit when topic differs.
  • BIM 289D—Selected Topics in Biomedical Engineering; Cell and Tissue Biomechanics [Tech, Rx] [Fall quarter]
  • Units: Variable (1-5 hours).
    Prerequisite(s): Consent of Instructor.
    Selected topics in Cell and Tissue Biomechanics. May be repeated for credit when topic differs. 
  • BIM 289E—Selected Topics in Biomedical Engineering; Analysis of Human Movement [Bionic, Rehab]
  • Units: Variable (1-5 hours).
    Prerequisite(s): Consent of Instructor.
    Selected topics in Analysis of Human Movement. May be repeated for credit when topic differs. 
  • ECS 289G—Special Topics in Computer Science [Tech] [Fall quarter]
  • Units: Variable (Lecture; Laboratory; Lecture/Lab. 1-5 hours).
    Prerequisite(s): Consent of Instructor.
    Special topic in Artificial Intelligence. May be repeated for credit when topic differs. 
  • EEC 289Q—Special Topics in Electrical and Computer Engineering; Computer Engineering [Tech]
  • Units: Variable (Lecture/Lab 1-5 hours).
    Prerequisite(s): Consent of Instructor.
    Special topics in Computer Engineering. May be repeated for credit when topic differs. 
  • BIM/NSC 295—Literature in Neuroengineering [General] [Fall quarter]
  • Units: 2 (Seminar—2 hours).
    Open to graduate students only.
    Critical presentation and discussion of current literature in neuroengineering. May be repeated for credit. (S/U grading only.) 
  • MAE/BIM 298—Directed Group Study. Design of Neural Control Systems [Tech, Bionic, Rehab] [Fall quarter]
  • Units: Variable (1-5 hours).
    Open to graduate students in the Biomedical Engineering Graduate Group, or with consent of Instructor. Directed group study in Biomedical Engineering. May be repeated for credit. (S/U grading only.) 
  • MAE 298- Introduction to Neural-Machine-Interfaces and Assisted Human Movement [Tech, Bionic, Rehab] [Fall quarter]
  • Units: 4.
    Open to graduate students. Topics related to human-machine neural interfaces and their applications in restoring and understanding impaired human movement. Content includes selected topics in: brain and spinal cord organization; peripheral nerve and sensory receptor function; closed-loop control of body movements; biomechanical analyses of healthy, impaired, and assisted human movement; neural-machine interfaces and accompanying assistive technologies to improve or understand impaired human movement.
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