COURSE FLYER (pdf)
COURSE SYLLABUS (pdf)
Course is focused on hardware and software technologies that enable control and readout of neural activity in the brain. Engineering-grounded innovation will accelerate our understanding of the brain, will impact new therapies for restoring lost neural functions, as well as will lead to neural interfaces that will augment our interaction with the world and machines. We will start with general introduction to neurobiology introducing concepts of neural activity, brain chemical and electrical signaling. neuroanatomy, and sensory information processing. We will further focus on using physical, chemical, and biological principles to understand technology design criteria governing ability to observe and alter brain structure and function.
Topics include: noninvasive and invasive brain mapping and stimulation, neural interfaces and neural prosthetics, data processing problems, decoding techniques based on machine learning, future brain interfaces based on nanotechnology and optogenetics.
The course is building upon general knowledge of linear systems and analogue signals analysis (ECE210 or BIOE205 or analogous). The goal is to open this course to general population of EE, CS, and BioE students interested to learn principles and frontiers of neural interface engineering. Therefore, no special knowledge of neurobiology is required.
Information for graduate students: Graduate students will be required to complete an additional independent project based on reviewing the proposed scientific literature, writing an extended report, and presenting it in the class. Alternatively, to receive this credit, graduate students must complete an independent project based on analysis of publicly available datasets of brain electrophysiological recordings using machine learning based software.
Graduate credit hours: 4
|10:00-10:50am, MWF||3081 ECEB||Yurii Vlasov (firstname.lastname@example.org)||Tue 12:00 PM||1250 MNTL|
Homework TA TBA (email@example.com) Office Hours: TBD
Textbook: Mostly based on classnotes.Recommended Reading:
|1||Introduction to neurophysiology. Membrane potential, ion channels, action potential, synaptic transmission. Modalities of neuron signaling synaptic, gap junctions, and chemical communications. Brain anatomy and functional organization.||PNS Ch 1, 2, 7, 9|
|2||Modulating the brain. Noninvasive neuromodulation methods. Electrical stimulation. Limbic system. Deep brain stimulation. Technological challenges.||BQA, Ch.12; MOTCD pp.201-235; NE p.405|
|3||Sensory organs prosthetics. Auditory system. Auditory signal processing in cochlear. Cochlear implants. Visual system. Retinal implants. Advantages and challenges.||PNS Ch 26, 31; NE p.635|
|4-5||Brain plasticity. Synaptic plasticity, Hebbian learning rules. Functional electrical stimulation. Peripheral and electro-cortical FES. Neurorehabilitation.||TN Ch.8.2; FSNMC Ch.19.1|
|6-7||Recording from the brain. Noninvasive methods to detect brain activity: EEG, ECoG, MEG, PET, fMRI, infrared imaging. Evoked potentials in EEG. EEG signal processing and decoding. Feature extraction and selection. Performance measures. EEG-based brain-machine interface.||NE p. 223,p.259|
|8-9||Brain spiking codes. Invasive recording methods: electrophysiology, optical imaging, multiphoton microscopy, bioluminescence. Spike detection. Spike sorting. Principal component analysis. Spike train analysis, Spike histogram, Tuning curve, Poisson process.||PNS Ch 21; TN Ch 1|
|10-11||Limb prosthetics.Motor system. Cortical control of movement. Decoding algorithms. Dimensionality reduction. Classification. Expectation-maximization. Kalman filter. Firing rate and population codes for limb prosthetics. Advantages and challenges.||PNS Ch 33, 37; PRML Ch.9|
|12-13||Sensorimotor prosthetics. Hierarchy of sensory processing. Perception. Principles of closed feedback loop control. Sensorimotor brain machine interface. Restoration of the sense of touch.||PNS Ch.21, 22, 23|
|14||Beyond BMI. Novel technologies for large-scale recording and modulation of brain activity. Memory and spatial navigation system. Artificial perception and memory prosthetics.||PNS Ch.65; NE p.725|
|15||Dynamic brain. Modeling complex neural dynamics. Artificial Intelligence for decoding brain network dynamics. Future brain data portals.||FSNMC Ch.16.3|
|3 credit hours||4 credit hours|