BMEB W4020 Computational Neuroscience: Circuits in the Brain
Course Benefits
| • | Provides a straighforward theoretical foundation to computational neuroscience. | ||
| • | Focuses on the intuitive understanding of (i) Biophysics of Computation, (ii) Encoding with Neural Circuits, and (iii) Functional Identification of Neural Circuits. |
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| • | Enables the further exploration of the fields of neuroengineering and neurocomputing. |
Professor Lazar
| • | His primary research interests focus on the molecular architecture and functional logic of the brain of model organisms with a strong emphasis on the fruit fly brain. Leading projects in: 1. Building Interactive Computing Tools for the Fruit Fly Brain Observatory, 2. Computing with Fruit Fly Brain Circuits, 3. Creating NeuroInformation Processing Machines. |
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| • | Further information about the instructor is available under URL: https://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. For related courses see the optional MS Concentration in Systems Biology and Neuroengineering and Concentration in Data-Driven Analysis and Computation, both offered by the Department of Electrical Engineering.
| Lecturer: | Professor Aurel A. Lazar | |
| Office hours: | Mondays, 4:00 PM - 6:00 PM, EST, Online | |
| E-mail address: | aurel "at" ee.columbia.edu | |
| Class Web Site: | Offered by CourseWorks | |
| TA/CA: | Bruce Yi Bu, Pranav I. Deevi and Shashwat Shukla | |
| Office hours: | Thursdays, 7:00 PM - 8:00 PM (Subject to Change), EST, Online | |
| E-mail address: | yb2520 "at" columbia.edu | |
| Day and Time: | Tuesdays, 7:00 PM - 9:30 PM | |
| Class Location: | 833 Seeley W. Mudd Building | |
| Credits for course: | 3 points | |
| PREQ: | ELEN E3801 Signals and Systems or Biology W3004 Neurobiology I: Cellular and Molecular Biology plus Python or the instructor's approval. Biology students who do not have strong prior programming experience should consider taking ENGI E1006 Introduction to Computing for Engineers and Applied Scientists (Python). | |
| Description: | Biophysics of Computation Modeling Biological Neurons, Biophysical Spike Generators: the Hudgkin-Huxley Neuron, Channel Conductances as Memristive Systems, I/O Equivalence for Spiking Neuron Models, The Phase Response Curve and the Phase Shift Process of the Hodgkin-Huxley Neuron, The Connectome and Modeling Connectivity with Synapses. Encoding with Neural Circuits The Fundamental Problem of Neural Encoding, Encoding Stimuli in the Time Domain, Geometry of Time Encoding, Time Encoding with Neural Circuits with Feedback, Modeling Dendritic Stimulus Processors, Spectro- and Spatio-Temporal Receptive Fields, Audio and Video Time Encoding Machines, Encoding Visual Stimuli with a Population of Complex Cells, Sparse Decoding of Visual Stimuli. Functional Identification of Neural Circuits The Fundamental Problem of Functional Identification, Functional Identification of Dendritic Stimulus Processors, Channel Identification Machines, The Duality between Neural Decoding and Functional Identification, Multisensory Channel Identification Machines, Identifying Spectro- and Spatio-Temporal Receptive Fields, Identifying Biophysical Spike Generators, Channel Identification Machines with Noisy Observations. Projects in Python Two projects based on material covered in [1] Biophysics of Computation/Encoding with Neural Circuits, and [2] Encoding with Neural Circuits/Functional Identification of Neural Circuits. |
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| RCMD text(s): | Course Notes (slides, chapters) and Jupyter Notebooks for computationally exploring the material covered in class will be available on CourseWorks. To extensively explore the Course Notes and Jupyter Notebooks students are strongly encouraged to write code in Python. | |
| Logistics: | Students will complete homework assignments and class projects in teams of up to 2 members. | |
| Homework: | 6 assignments, mostly writing or adapting simple Python code. | |
| Paper(s): | --- | |
| Projects: | 2 major projects (see above) | |
| Midterm exam: | --- | |
| Final Exam: | --- | |
| Grading: | The homework grade is based on the average of the best 5 out of 6 assignments. The final grade has three components: 1/5 homework, 2/5 project #1 and 2/5 project #2. | |
| Hardware REQS: | Laptop for demos | |
| Software REQS: | Python | |
| Homework submission: | Mondays at noon - strict deadline. | |