BMEB W4020 Computational Neuroscience: Circuits in the Brain
|||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 key concepts in theoretical neuroscience.|
|||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: 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. For related courses see also the optional MS Concentration in Systems Biology and Neuroengineering offered by the Department of Electrical Engineering.
|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|
|Office hours:||Fridays, 1:00 PM - 3:00 PM (Subject to Change), EST, @EE Student Lounge (1300 Mudd)|
|E-mail address:||tba "at" columbia.edu|
|Day and Time:||Tuesdays, 7:00 PM - 9:30 PM|
|Class Location:||627 Seeley W. Mudd Building|
|Credits for course:||3 points|
|Prerequisites||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:||The Biophysics of Computation
Modeling Biological Neurons, Biophysical Spike Generators: the Hudgkin-Huxley Neuron, Channel Conductances as Memristive Systems, Synaptic Models of Connectivity, I/O Equivalence and Spiking Neuron Models, Bursting Neurons and Central Pattern Generators.
Encoding with Neural Circuits
Modeling Dendritic Stimulus Processors, The Fundamental Problem of Neural Encoding, Stimulus Representation with Time Encoding Machines, Geometry of Time Encoding, Time Encoding with Neural Circuits with Feedback, 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
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
 Biophysics of Computation Project,  Encoding with Neural Circuits Project, and  Functional Identification of Neural Circuits Project.
|Recommended text(s):|| Course Notes will be made available.|
|Homework(s):||6, mostly writing or adapting simple Matlab code. Students with strong background in programming are encouraged to write code in Python or PyCUDA (instructor's permission required).|
|Project(s)||3 major projects (see above)|
|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 best two among project , project  and project . Weighting: 1/5 homework, 2/5 for each of the best 2 out of 3 projects.|
|Hardware requirements:||Laptop for demos|
|Homework submission:||Mondays at noon - strict deadline.|