EEBM E6020: Methods of Computational Neuroscience
| Lecturer: | Professor Aurel A. Lazar | |
| Office hours: | By appointment, Room 819 Schapiro | |
| E-mail address: | aurel "at" ee.columbia.edu | |
| Class Web Site: | Offered by CourseWorks | |
| Day and Time: | Mondays, 4:10 PM - 6:40 PM | |
| Class Location: | 233 Mudd | |
| Credits for course: | 4.5 points | |
| Prerequisites | BMEB W4011 or the instructor's approval | |
| Description: | Formal methods in computational neuroscience including methods of signal processing, communications theory, information theory, systems and control, system identification and machine learning. Molecular models of transduction pathways. Robust adaptation and integral feedback. Stimulus representation and groups. Stochastic and dynamical systems models of spike generation. Neural diversity and ensemble encoding. Time encoding machines and neural codes. Stimulus recovery with time decoding machines. MIMO models of neural computation. Synaptic plasticity and learning algorithms. Major project(s) in Matlab. | |
| Required texts: | ---Izhikevich E.M., Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting, The MIT Press, Cambridge, MA, 2007. Christensen, O., An Introduction to Frames and Riesz Bases, Birkhauser, Boston, MA, 2003. |
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| Homework(s): | Reading book chapters | |
| Paper(s): | --- | |
| Project(s) | 2 Projects | |
| Midterm exam: | Project | |
| Final Exam: | Project | |
| Grading | Classroom Participation and Projects | |
| Hardware requirements: | Laptop for demos | |
| Software requirements: | Matlab (student version) or Python | |