Weeks of August 17 and August 24, 2020
Course Overview
Neural Engineering is one of the most exciting inter-disciplinary technologies to impact neuroscience and information science, computation and robotics. A greater understanding of the brain and how it learns, acts, and interprets the world has immensely impacted the development of neural interfaces and the design of bio-inspired robots. The basic concept of signal processing and machine learning is a fundamental tool to understand neural coding, and finds applications in neural interfacing, neural prostheses, and neuromorphic brain-based robots.
Concepts from filtering, probabilistic estimation, and machine learning will be introduced in the summer school so that students can understand and implement real examples of neural engineering, and recognize the impact of the latest technologies. The course will enhance the vision of the students and encourage them to work in future inter-disciplinary research fields. Students are expected to obtain knowledge of neurophysiology and neuroscience, digital signal processing, machine learning, and hands-on experience through reports and projects on real data.
Each selected participant will receive a Backyard Brains Heart and Brain SpikerBox Kit to keep and to collect data for use during the virtual summer school.
Course Prerequisite
This course is mathematics-oriented. It requires basic knowledge of linear algebra, calculus, and probability. Familiarity with programming in the Matlab or Python environment is needed. Experience in computational neuroscience and signal processing/machine learning is strongly recommended.
Organizers
Yiwen Wang, Hong Kong University of Science and Technology
José C. Príncipe, University of Florida
Application
Enrollment in this workshop will be limited to 20 students. International student applications welcome.
Application Deadline – 11:00 pm Eastern Time, July 17, 2020
Acceptance Notices – July 26, 2020
Commitments – July 29, 2020
Preliminary Schedule
IEEE Brain Summer School - Week 1
| Day | Session Title | Lecturers, Lab Assistants, and Judges |
|---|---|---|
| Monday 17-August | • Welcome Remarks • Lecture 1 on Neurophysiology and Brain Organization • Lecture 2 on Brain Data Acquisition and Signal Processing | • Jose Principe, Univ of Florida, Yiwen Wang, HKUST • Jian-Young Wu, Georgetown Univ • Paul Sajda, Columbia Univ |
| Tuesday 18-August | • Lecture 3 on Brain and Neuromuscular Function • Presentation of the Backyard Brains Kits | • Lee Miller, Northwestern Univ • Tim Marzullo, Backyard Brains |
| Wednesday 19-August | • Lab 1: Classification of Pre-recorded Data (EEG, EMG, spikes) • Project Team Creation - Students divided into teams | • Pawan Lapborisuth, Columbia Univ Xuan Ma, Northwestern Univ Xiang Zhang, HKUST |
| Thursday 20-August | • Lecture 4 on Basic Neural Coding and Advanced Modeling Using Machine Learning | • Denis Erdogmus, Northeastern Univ Murat Akcakaya, Univ of Pittsburgh |
| Friday 21-August | • Lab 2: Improvement on Lab 1 Using Advanced Neural Coding | • TBD, Northeastern Univ Xiang Zhang, HKUST |
| Saturday- Sunday 22-23 August | Teams Work on Projects |
IEEE Brain Summer School - Week 2
| Day | Session Title | Lecturers, Lab Assistants, and Judges |
|---|---|---|
| Monday 24-August | • Project Team Report - Group Presentations of Progress and Challenges on Data Collection and Processing | • Tim Marzullo, Jian-Young Wu, Ning Jiang (Univ of Waterloo) |
| Tuesday 25-August | • Lab 3: Test Implementation & Debug on Data Acquisition and Preprocessing | • Pawan Lapborisuth, Xuan Ma |
| Wednesday 26-August | • Project Team Report - Group Presentations of Progress and Challenges on Project Test Preliminary Results | • Jose Principe, Deniz Erdogmus, Murat Akcakaya |
| Thursday 27-August | • Lab 4: Test Implementations & Debug on Preliminary Results and Further Improvement | • Xiang Zhang and TBD |
| Friday 28-August | • Final Project Team Report - Presentation on Test Results and Analysis | • Jose Principe, Yiwen Wang, Ning Jiang, Tim Marzullo |
