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.
    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.
    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 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"
  Class Web Site: Offered by CourseWorks
TA: Shashwat Shukla
  Office hours: Mondays, 2:00 PM - 4:00 PM (Subject to Change), EST, Online
  E-mail address: ss6113 "at"
Day and Time: Tuesdays, 7:00 PM - 9:30 PM
Class Location: 1127 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 (Matlab or Python) or the instructor's approval. Biology students who do not have strong prior programming experience should consider taking COMS W1005 Introduction to Computer Science and Programming in MATLAB or ENGI E1006 Introduction to Computing for Engineers and Applied Scientists. Help to find and use Matlab resources is widely available.
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 Matlab or 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.
RCMD text(s): Course Notes (slides, chapters) and Jupyter Notebooks for computationally exploring the material covered in class will be available on CourseWorks.
Logistics: Students will complete homework assignments and class projects in teams of up to 2 members.
Homeworks: 6, mostly writing or adapting simple Matlab or Python code. Students with strong background in programming are encouraged to write code in Python.
Paper(s): ---
Project(s) 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 homeworks. The final grade has three components: homework and the two projects. Weighting: 1/5 homework, 2/5 for each of the 2 projects.
Hardware REQS: Laptop for demos
Software REQS: Matlab or Python
Homework submission: Mondays at noon - strict deadline.