BMEB W4011 Computational Neuroscience: Circuits in the Brain
Course Benefits
| • | Provides a straighforward theoretical foundation to computational neuroscience. | ||
| • | Focuses on the intuitive understanding of information representation and neural coding. | ||
| • | Enables the further exploration of key concepts in theoretical neuroscience. |
Professor Lazar
| • | Interests in Computational Neuroscience: In Silico: Time Encoding and Information Representation in Sensory Systems, Spike Processing and Computation in the Cortex. In Vivo: Olfactory System of the Drosophila Melanogaster. | ||
| • | Further information about the instructor is available under URL: http://www.ee.columbia.edu/~aurel. |
Applicable Degree Programs
Most courses 4000-level and above can be credited to all degree programs. All courses are subject to advisor approval.
| Lecturer: | Professor Aurel A. Lazar | |
| Office hours: | Mondays, 4:00 PM - 6:00 PM, EST, Room 819 CEPSR | |
| E-mail address: | aurel "at" ee.columbia.edu | |
| Class Web Site: | Offered by CourseWorks | |
| TA: | Robert J. Turetsky | |
| Office hours: | Thursdays, 4:00 PM - 6:00 PM, EST, Room 807 CEPSR | |
| E-mail address: | rjt72 "at" columbia.edu | |
| Day and Time: | Tuesdays, 6:50 PM - 9:20 PM | |
| Class Location: | 545 Mudd | |
| Credits for course: | 3 points | |
| Prerequisites | ELEN E3801 (Signals and Systems) or Biology W3004 (plus Matlab) or the instructor's approval. Biology students who do not have strong prior programming experience should consider taking Computer Science W1005. | |
| Description: | The Biophysics of Computation: Modeling Biological Neurons, The Hudgkin-Huxley Neuron, Modeling Channel Conductances and Synapses as Memristive Systems, Bursting Neurons and Central Pattern Generators, I/O Equivalence and Spiking Neuron Models. Information Representation and Neural Encoding: Stimulus Representation with Time Encoding Machines, The Geometry of Time Encoding, Encoding with Neural Circuits with Feedback, Population Time Encoding Machines. Dendritic Computation: Elements of Spike Processing and Neural Computation, Synaptic Plasticity and Learning Algorithms, Unsupervised Learning and Spike Time-Dependent Plasticity, Basic Dendritic Integration. Projects in Matlab. | |
| Recommended text(s): |
Daniel Johnston and Samuel Miao-Sin Wu, Foundations of Cellular Neurophysiology, The MIT Press, Cambridge, MA, 1995. Pascal Wallisch, Michael Lusignan, Marc Benayoun, Tanya I. Baker, Adam S. Dickey and Nicholas G. Hatsopoulos, Matlab for Neuroscientists, Academic Press, 2009. |
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| Reference text(s): | Peter Dayan and L.F. Abbott, Theoretical Neuroscience, The MIT Press, Cambridge, MA, 2001. W. Gerstner and W. Kistler, Spiking Neuron Models, Cambridge University Press, New York, NY, 2002. Izhikevich E.M., Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting, The MIT Press, Cambridge , MA, 2007. Christof Koch, Biophysics of Computation, Information Processing in Single Neurons, Oxford University Press, New York, NY, 1999. F.M. Rieke, D. Warland, R. de Ruyter van Steveninck, W. Bialek, Spikes: Exploring the Neural Code, The MIT Press, Cambr idge, MA, 1997. Peter M. Trappenberg, Fundamentals of Computational Neuroscience, Oxford University Press, New York, NY, 2002. Hugh R. Wilson, Spikes, Decisions and Actions, Oxford University Press, New York, NY, 1999. |
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| Homework(s): | 6, mostly writing or adapting simple Matlab code. | |
| Paper(s): | --- | |
| Project(s) | 2 major projects | |
| Midterm exam: | --- | |
| Final Exam: | Take-home exam is due on Monday, December 14, 2009, at 12 noon. | |
| Grading: | EITHER 1/5 homework, 2/5 for each project OR 1/5 homework, 2/5 for best project and 2/5 final. Students who take the Final Exam will be graded with the latter option. | |
| Hardware requirements: | Laptop for demos | |
| Software requirements: | Matlab (student version) | |
| Homework submission: | Mondays at noon - strict deadline. | |