Neural Networks and Deep Learning

ECE

Jose Principe

Optimal filters in vector spaces. Linear machines and discriminant functions. Gradient descent in additive neural model. Performance measure of multilayer perceptions and Hopfield. Dynamic neural networks and issues of short-term memory; unsupervised learning; feature extraction, data reduction; machine learning.

Future Micro/Nano System and their Applications

ECE

Jack Judy

Introduction to the wide range of exciting new applications for microscale and nanoscale devices and systems, including transducers, as well as their use in metamaterials, biological science, medical diagnostics, environmental, IoT, biometric cyber security, neural interfaces, multi-functional micro/nanosystems, and other application areas.

Neurotechnologies in Education

Education Technology

Pavlo Antonenko

This seminar focuses on the neuroscience frameworks and technologies that are gained popularity in education research as a way to supplement and offset the weakness of self-report data.

Introduction to Neural Interfaces and Systems

ECE

Adam Khalifa

The interdisciplinary course focuses on invasive neural implants and covers various types of recording and stimulating neural interface technologies used in animal research and humans. The course examines the neurophysiological principles and computational modeling of neurons, as well as the tissue-electrode interface, to better understand how electrical signals are transmitted.

Neural Engineering

BME

Aysegul Gunduz

Applying engineering to neuroscience; includes such diverse areas as neural tissue engineering, models of neural function, and neural interface technology. Focuses mainly in the context of neural interfaces and prosthetics, from basic neural physiology and models of neural mechanisms to advanced neural interfaces currently in development or produced commercially.

Neural Signals, Systems, and Technology

ECE

Karim Oweiss

Biophysical principles of neural signaling, characterization of neural circuits and systems, technology design principles for interfacing with biological neural systems, overview of clinical applications and industrial opportunities for neurotechnology.

Bioelectric Systems

ECE

Karim Oweiss

Covers the theoretical and quantitative perspective of bioelectrical signals reflecting the activity of the brain, the muscles, and the heart. Examines bases of modeling, measuring, processing and analyzing bioelectrical signals and systems, as well as common clinical applications. Laboratory.