EEBM E6095 Computing with Brain Circuits
|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, 7:00 PM - 9:00 PM|
|Class Location:||XXX Mudd Building|
|Credits for course:||3 points|
|Prerequisites||Familiarity with linear algebra and signal processing. Python programming experience is a requirement. Prior exposure to interactive computing (e.g., JupyterLab) is a plus. Background in computational neuroscience (e.g., BMEB W4020) and/or deep learning (e.g., ECBM E4040) is recommended but it is not a prerequisite. Students are expected to extensively explore the function of local processing units of the fruit fly brain.|
|Description:||The Functional Map of the Fruit Fly Brain
Modeling the brain of model organisms with an emphasis on the fruit fly. The Fruit Fly Brain Observatory. Structural modeling of the Drosophila brain using cell-type, connectome (connectivity matrix), synaptome (set of all synapses) and activity (electrophysiology) maps. Building the functional map of the fruit fly brain with canonical circuits and parallel programming models of local processing units.
The Brain as a Molecular Computing Machine
Information processing in molecular circuits. Molecular transduction and combinatorial encoding in the Drosophila antennae. Molecular transduction and spatio-temporal encoding in the Drosophila retina. Reduced Drosophila photoreceptor models and contrast encoding in the retina. Predictive coding.
2 Functional Units of the Sensory System
Detailed description of the fruit fly's early olfactory and vision circuits. Normalization as a canonical computation. Odorant classification and motion detection algorithms.
Memory and Navigation Control
Modeling associative and innate memory: the mushroom body and the lateral horn. Navigation and the central complex. Bursting Neurons and Central Pattern Generators, Ring Attractor Networks. Activity bumps (bursting) in ring attractor networks.
Projects in Python
|RCMD Text:|| Lectures Notes will be made available.
|Midterm exam:||Project I: TBA|
|Final Exam:||Project II: TBA|
|Grading||Midterm Project (50%) and Final Project (50%)|
|Hardware requirements:||Laptop for demos.|