UG Courses applicable to the Neuroengineering Field:
ELEC 380/BIOE 380/NEUR 383 Introduction to Neuroengineering (Fall)
- This course serves as an introduction to qualitative modeling of neural activity and the methods used to stimulate and record brain activity. The course is Python-based and challenges those with engineering and neurobiology backgrounds to extend their capabilities and develop new ones. A background in neuroscience or engineering is highly recommended.
BIOE based course:
BIOE 492 Sensory Neuroengineering (Fall)
- This course explores how bioengineering techniques and principles are applied to understand and model sensory systems, focusing on the auditory, vestibular, and visual systems. The interaction between the electrical, mechanical, and optical aspects of these systems and ways to modulate these interactions will be explored, as well as emerging and current technologies for neural stimulation.
NEUR based courses:
BIOS 385 Fundamentals of Neuroscience (Fall)
- This course covers the cellular, molecular, and integrative mechanisms of neural function, including membrane and axon physiology, synaptic transmission and plasticity, sensory transduction and processing. Provides fundamental information to lay a groundwork for understanding the processes used in Neuroengineering.
BIOS 310 Independent Research for BIOS Undergrads
- Course credit awarded for independent research conducted in Rice Biosciences faculty laboratories or other Texas Medical Center laboratories. Students spend at least 3 hours per week conducting research for each semester hour of credit. Best way to get lab experience in the Neuroengineering lab of the student’s choice while receiving credit. Students will write weekly summaries of their lab work and conclude the semester with a presentation/paper/poster.
Supplemental Neuroscience based courses:
NEUR 380/PSYC 380/BIOC 380 Neurosystems (Spring)
- This course provides a broad overview of the brain’s neural systems that subserve perception, learning and behavior. Heavily focused on neuroanatomy and the biology of the brain, this course is highly integrative with thematic content including functional organization of the nervous system, neural encoding and decoding, sensory systems, motor systems, and high level concept processing. Provides valuable anatomy knowledge applicable to Neuroengineering.
- Recommended Prerequisite: PSYC 101
NEUR 362/PSYC 362 Cognitive Neuroscience (Spring)
- This course is a survey of theory and research on how mental processes are carried out by the human brain, with an emphasis on relating measures of brain activity to cognitive functioning. Methods used include electro physiological recording techniques, functional imaging techniques, and methods that involve lessoning or disrupting neural activity. Serves as an introduction to thinking critically about experiment paradigms and becoming comfortable analyzing neural data such as fMRI and EEG ERPs.
ELEC based courses:
ELEC 483 Machine Learning and Signal Processing for Neuroengineering (Fall)
- This course covers statistical signal processing and machine learning approaches for modern neuroscience data, including application of these approaches. Neuroscience applications include modeling neural firing rates, spike sorting and decoding. Topics include latent variable models, point processes, Bayesian inference, dimensionality reduction, dynamical systems, and spectral analysis.
- Recommended Prerequisites: ELEC 475, STAT 413, COMP 540, ELEC 242 or ELEC 243
ELEC 489 Neural Computation (Spring)
- This course introduces the mathematical theories of learning and computation by neural systems. Students learn to formalize and mathematically answer questions about neural computations by applying various theories to sensory computation, learning and memory, and motor control. This course focuses on the mathematical and theoretical side of neuroscience and a background in calculus, linear algebra, and probability and statistics is highly recommended.
ELEC 475 Learning from Sensory Data
- This course develops the basic machine learning tools for signal images and other data acquired from sensors (including principal components analysis, regression, support vector machines, neural networks, and deep learning) and overviews applications of sensor data science in neuroscience, image and video processing, and machine vision. Surveys computational methods for processing time series and graphical data, including hands-on work with EEG data
Supplemental ELEC Courses:
ELEC 327 Implementation of Digital Systems (Spring)
- This course concerns the implementation of digital systems using the Verilog hardware description language including Verilog test benches and timing simulations and techniques for implementing control units, data-flow units, pipelining and interrupts. The course requires the completion of a significant project involving the implementation of a modern instruction set architecture.
Additional Courses:
PSYC 430 Computational Modeling of Cognitive Processes
- This course surveys computational approaches to modeling cognitive processes with an emphasis on recent production system models. Involves an evaluation of existing models and hands-on experience in modeling. This course provides experience in understanding and developing brain computer interfaces (BCIs).
- Recommended Prerequisites: PSYC 203 and COMP 200 (or equivalent)
Useful Prerequisites:
ELEC 301 Signals, Systems, and Learning
- This course provides an analytical framework for analyzing signals and systems and provides an introduction to algorithms for machine learning on signals, including clustering, regression, and classification. A necessary introduction for future work in signal processing.
- Recommended Prerequisites: CAAM 335 or MATH 355
or
ELEC 303 Random Signals
- This course serves as an introduction to probability theory and statistics with applications to electrical engineering problems in signal processing, communications and control; probability spaces, conditional probability, independence, random variables, distribution and density functions, random vectors, signal detection and parameter estimation.
- May be taken concurrently with ELEC 303
PSYC 203 Intro to Cognitive Psychology
- This course is an introduction to topics in cognitive psychology, including perception, attention, language, memory, and decision making. This course can spark passion in problems tackled in Neuroengineering and aims to view these topics holistically.