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USEFUL RESOURCES COURSE FLYER (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. Course pre-requisties ECE210, or BIOE205 and NE 330, or instructor approval. The course is building upon general knowledge of linear systems and analogue signals analysis 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 prior 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 |
Time | Room | Instructor | Office Hours | Office |
2:00pm-3:20pm Tue Thur |
2013 ECEB | Yurii Vlasov (yvlasov@illinois.edu) | Thurs 11:00 AM | 1250 MNTL |
TAs Miles Guo (ziangg2@illinois.edu), Alex Armstrong (alexga2@illinois.edu) Office Hours: Wednesdays 1PM in ECEB 2036
Textbook: Mostly based on classnotes.
Recommended Reading:Weeks/Topic | Tuesday 2:00pm ECEB 2013 |
Thursday 2:00pm ECEB 2013 |
Recommended reading | |
1. Neuro electricity | 01/21 L1 Introduction Brain anatomy and functional organization. Neuroelectricity. Notes | 01/23 L2 Membrane potential, ion channels. Action potential. Notes
HW1 Assignment Solutions |
PNS Ch1, Ch15, Ch5, TN Ch.5.5 | |
2. Neuron models | 01/28 L3 HH membrane model. LIF neuron model.
Notes
In-class quiz solutions |
01/30 L4 Dendritic transmission. Axonal signal transmission.
Notes
HW2 Assignment. Solutions. |
PNS Ch.6, TN Ch.6.3 | |
3. Modulating brain | 02/04 L5 Noninvasive neuromodulation. Limbic system. Basal ganglia. Deep brain stimulation.
Notes
In-class quiz |
02/06 L6 Auditory system. Auditory signal processing in cochlear.
Notes HW3 Assignment. Solutions. |
NE Ch7-Ch9, NE Ch.6, PNS Ch.30 | |
4 Sensory organs prosthetics | 02/11 L7 Cochlear implants. Signal processing.
Notes In-class quiz |
02/13 L8 Visual system. Hierarchical processing in visual system.
Notes HW4 Assignment. |
NE Ch.20, PNS Ch.26 | |
5 Brain Plasticity | 02/18 L9. Retinal implants. Advantages and challenges. Notes | 02/20 L10 Synaptic transmission. Synaptic plasticity.
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PNS Ch.8 | |
6 Neuro rehabilitation | 02/25 L11 Midterm Exam 1. |
02/27 L12 Hebbian learning. Neuromuscular junction. FES. Neuro rehabilitation.
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7 Recording from brain | 03/04 L13 Noninvasive recording of brain activity: MEG, PET, fMRI, IR imaging.
In-class quiz |
03/06 L14 EEG biopotential amplifiers. EEG signal processing. ICA and PCA.
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8 EEG BMI | 03/11 L15 Event related potential. Feature extraction.Classification. EEG-based BMI.
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03/13 L16 Invasive recording methods. ECOG. ECOG BMI.
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03/18 Spring Break |
03/20 Spring Break |
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9 Spike train analysis | 03/25 L17 Multielectrode arrays. Spikes and LFP. Spike sorting. Spiking variability. Firing rate. | 03/26 L18 Spike density. Tuning curve. Population coding. Neural coding problem.
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10 Limb prosthetics | 04/01 L19 Motor system. Neural control of movement.
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04/03 L20 Decoding algorithms. Dimensionality reduction.
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11 Control engineering | 04/08 L21 Midterm Exam II | 04/10 L22 Intro to linear systems control. Kalman filter and optimal state estimation.
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12 Neural decoding | 04/05 L23 BMI examples. Linear and population vector decoding. |
04/17 L24 BMI examples. Kalman filter decoding.
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13 Sensory-motor prosthetics | 04/22 L25 Reverse Engineering.
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04/24 L26 Dynamic feedback control. Manifold hypothesis. Sensory-motor integration.
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14 Beyond motor BMI | 04/29 L27 Sensory-motor BMI. Restoration of the sense of touch. | 05/01 L28 Memory and spatial navigation system. Memory prosthetics. | ||
15 Wrapping up | 05/06 L29 Projects presentations. Conclusions. |
05/07 Reading |
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Final Exam 05/14 7:00PM ECEB 2013 |
GRADING POLICY
3 credit hours | 4 credit hours | |
Homework and Quizes | 20% | 20% |
Exam 1 | 25% | 20% |
Exam 2 | 25% | 20% |
Final Exam | 30% | 25% |
Graduate Project | NA | 15% |