Syllabus - THEORETICAL AND COMPUTATIONAL NEUROSCIENCE B (76909)

Syllabus THEORETICAL AND COMPUTATIONAL NEUROSCIENCE B - 76909
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Last update 19-02-2014
HU Credits: 4 Degree/Cycle: 2nd degree (Master) Responsible Department: Brain Science: Computation & Information Proc. Semester: 1st Semester Teaching Languages: English Campus: E. Safra Course/Module Coordinator: Haim Sompolinsky Coordinator Email: haim@fiz.huji.ac.il Coordinator Office Hours: By appointment Teaching Staff: Prof Yoram Burak Mr. Haggai Agmon Ms. Gal Vishne Course/Module description: Course/Module aims: Learning outcomes - On successful completion of this module, students should be able to: Attendance requirements(%): 0 Teaching arrangement and method of instruction: Lecture and Recitation sessions Course/Module Content: I. Introduction to Computational Neuroscience II. Principles of sensory processing: * Receptive fields and Efficient Coding * Information, Mutual Information, Entropy, and MaxEntropy * The Gaussian Ensemble * Infomax * Vision: Natural image statistics and Infomax predictions * Beyond Gaussianity * ICA * Compressed sensing and sparse coding III. Neural Population Codes * Statistical characterization of neuronal population codes. * Fisher information theory and ML estimation/decision * Correlated population coding * Biological readouts * Spike based neural code * Bayesian computation in neural systems IV: Network dynamics and computation * Rate based Models, Linear networks: Fixed Points and Stability, Symmetric networks, energy functions * Non-linear networks: Attractors, Lyapunov functions, symmetric networks * Spatial computation: spatial working memory, head direction, place cells and grid cells * Spatial computation: models * Asymmetric networks: linear networks and temporal working memory. * Dynamics and computation in chaotic neuronal networks * Balanced networks Required Reading: none Additional Reading Material: Course/Module evaluation: End of year written/oral examination 70 % Presentation 0 % Participation in Tutorials 0 % Project work 0 % Assignments 30 % Reports 0 % Research project 0 % Quizzes 0 % Other 0 % Additional information: Home assignment grades will constitute at least 30% of the final course grade.

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