Information Processing Models in Neural Systems
(3-0-0-3)
CMPE Degree: This course is Not Applicable for the CMPE degree.
EE Degree: This course is Not Applicable for the EE degree.
Lab Hours: 0 supervised lab hours and 0 unsupervised lab hours.
Technical Interest Group(s) / Course Type(s): Bioengineering
Course Coordinator: Christopher John Rozell
Prerequisites: ECE 2026 and (ECE 3075 or ECE 3077)
Corequisites: None.
Catalog Description
Examines 'top-down' modeling approaches for sensorineural systems, where optimal computational principles used in engineering (e.g., information theory, Bayesian inference, control theory) explain observed information processing.Textbook(s)
Hurri & Hoyer, Natural Image Statistics: A Probabilistic Approach to Computational VisionCourse Outcomes
Not Applicable
Student Outcomes
In the parentheses for each Student Outcome:"P" for primary indicates the outcome is a major focus of the entire course.
“M” for moderate indicates the outcome is the focus of at least one component of the course, but not majority of course material.
“LN” for “little to none” indicates that the course does not contribute significantly to this outcome.
1. ( Not Applicable ) An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics
2. ( Not Applicable ) An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors
3. ( Not Applicable ) An ability to communicate effectively with a range of audiences
4. ( Not Applicable ) An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts
5. ( Not Applicable ) An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives
6. ( Not Applicable ) An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions
7. ( Not Applicable ) An ability to acquire and apply new knowledge as needed, using appropriate learning strategies.
Strategic Performance Indicators (SPIs)
Outcome 1 (Students will demonstrate expertise in a subfield of study chosen from the fields of electrical engineering or computer engineering):
1. Explain the opportunities and challenges of using core concepts in electrical and computer engineering to model aspects of brain function, such as information theory, probabilistic inference, and artificial neural networks.
Outcome 2 (Students will demonstrate the ability to identify and formulate advanced problems and apply knowledge of mathematics and science to solve those problems):
1. Analyze the statistics of natural images to identify structures potentially exploited by sensory systems.
2. Utilize concepts from electrical and computer engineering to develop a model of some aspect of brain function and execute an evaluation of that model via analysis or computer simulation.
Outcome 3 (Students will demonstrate the ability to utilize current knowledge, technology, or techniques within their chosen subfield):
1. Read research papers to identify and critique the use of concepts from electrical engineering to modeling brain function.
Course Objectives
Topical Outline
1. Introduction and background
a. The types of modeling: Descriptive, mechanistic, and functional/interpretive
b. Review of mathematical preliminaries: linear systems, probability and statistics
c. Basic neurophysiology: Cell physiology, electrical potentials, and synaptic transmission
d. Basic system structure and function: Early visual pathway, auditory periphery, and characterization of individual cell responses
e. Natural sensory statistics and system efficiency
2. Optimal inference and decision theory
a. Optimal statistical inference and decision theory
b. Inference in perception and cue integration
c. Motion illusions and conditional perception as Bayesian inference
d. Optimal (sequential) decision making and evidence accumulation
e. Inference in neural codes incorporating sparsity
3. Dimensionality reduction
a. Manifold models of perception
b. Matrix factorization models of low-dimensional structure
c. Compressed sensing in learning and memory
d. Low-dimensional dynamics in neural populations
4. Information theory
a. The mathematical theory of information
b. Synaptic structure and function
c. Single unit responses in the visual pathway
d. Adaptive gain control maximizes information?
5. Feedback
a. Motor coordination
b. Predictive coding and attention
6. Hierarchical models and invariance
a. Building invariances in stages
b. Learning invariances with deep networks
7. Group project presentations