ECBM E6070 Fruit Fly Brain as NeuroInformation Processor

Course Benefits

    Provides a bridge between neuroscience and deep learning.
    Enables the further exploration of key concepts in systems/computational/theoretical neuroscience and deep learning.

Professor Lazar

    Interests in Computational, Systems and Theoretical Neuroscience
In Silico: Neural Computing Engines and NeuroInformation Processing Machines.
In Vivo: Reverse Engineering the Fruit Fly Brain.
In Silico: Fruit Fly Brain Observatory.
    Further information about the instructor is available under URL:

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 also 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: Tuesdays, 4:00 - 6:00 PM, EST, Room 819 Schapiro
  E-mail address: aurel "at"
  Class Web Site: Offered by CourseWorks
TA: Mehmet Kerem Turkcan
  Office hours: 11:00AM-12:00PM on Tuesdays and 3:00PM-4:00PM on Fridays, @EE Student Lounge (1300 Mudd)
  E-mail address: TBA "at"
Day and Time: Mondays, 7:00 PM - 9:00 PM
Class Location: 634 Seeley W. Mudd Building
Credits for course: 3 points
Prerequisites ECBM E4040 or the instructor's approval
Description: Modeling the brain of model organisms with an emphasis on the fruit fly. Drosophila connectomics. Detailed description of the fruit fly's early olfactory and vision circuits. Navigation and the central complex. The architecture of the Fruit Fly Brain Observatory. Canonical circuits and parallel programming models of local processing units of the fruit fly brain. Methods of Deep Learning. Projects in Python.
RCMD Text: Peter Sterling and Simon Laughlin, Principles of Neural Design, The MIT Press, 2015.

Homework(s): Reading research papers and book chapters
Paper(s): ---
Project(s) TBA
Midterm exam: Project I: TBA
Final Exam: Project II: TBA
Grading Classroom participation and projects
Hardware requirements: Laptop for demos.
Software requirements: TBA
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