CMSC 452 Elementary Theory of Computation (3).CMSC 436 Programming Handheld Systems (3).CMSC 421 Intro to Artificial Intelligence (3).CMSC 414 Computer and Network Security (3).PHYS 4XX Most 400-level Physics courses (3)Ĭategory B: Computer Science Theory and Applicationsįor the latest CMSC course syllabi, please visit the CS Class Web Pages.PHYS 499I Physical Foundations of Information Technology (3).PHYS 420 Principles of Modern Physics (3).PHYS 270/271 General Physics III: Electrodynamics, Light, Relativity and Modern Physics (4).MATH 4XX Most 400-level Math courses, except for MATH 416 (3).MATH 464 Transform Methods for Scientists and Engineers (3).MATH 463 Complex Variables for Scientists and Engineers (3).MATH 462 Partial Differential Equations (3).
MATH 461 Linear Algebra for Scientists and Engineers (3). MATH 406 Introduction to Number Theory (3). MATH 401 Applications of Linear Algebra (3). CMSC/AMSC 475 Combinatorics and Graph Theory (3). CMSC/AMSC 466 Introduction to Numerical Analysis I (3). CMSC/AMSC 460 Computational Methods (3). CMSC/MATH456/ENEE456 Cryptography (3). (minimum 6 credits, of which 3 must be 400-level)įor the latest course syllabi/information please visit the following sites: Math courses, Physics courses, CS courses. Courses listed in the prohibited course list may not be used for any technical elective categories.Ĭategory A: Mathematics and Basic Science Electives. CMSC412, ENEE447, CMSC330, CMSC351, ENEE303, ENEE307, ENEE350, and ENEE446) may not be used to fulfill the categories below. Disciplinary foundation CMSC or ENEE courses (i.e. Courses listed below may not be counted for two different categories. Be sure to check both in the Testudo website and with your advisor to make sure the course is available. Please note that the some courses listed below may not be offered every single semester. The categories and links to approved courses are listed below. These electives must be selected from among six categories, each of which has minimum credit requirements. The authors have declared no competing interest.The following requirements apply to students who matriculated on Fall 2019 or any semesters after.Ĭomputer Engineering majors are required to complete twenty-six (26) credits of computer engineering technical electives. We believe our approach provides a powerful framework for visualizing, analyzing, and discovering dynamic spatially distributed brain representations during naturalistic conditions. The spatiotemporal saliency maps revealed dynamic but consistent changes in fMRI activation data. Finally, we employed saliency maps to characterize spatiotemporally-varying brain-region importance. We propose a dimensionality reduction approach that uncovers low-dimensional trajectories and captures essential informational properties of brain dynamics. LSTMs were also superior to existing methods in predicting behavior and personality traits of individuals. #ENDNOTE UMD MOVIE#
We show that movie clips result in complex but distinct spatiotemporal patterns in brain data that can be classified using LSTMs (≈ 90% for 15-way classification), demonstrating that learned representations generalized to unseen participants. We demonstrate the potential of the approach using naturalistic movie-watching fMRI data. To capture and characterize spatiotemporal properties of brain events, we propose an architecture based on long short-term memory (LSTM) networks to uncover distributed spatiotemporal signatures during dynamic experimental conditions 1. Insights from functional Magnetic Resonance Imaging (fMRI), and more recently from recordings of large numbers of neurons through calcium imaging, reveal that many cognitive, emotional, and motor functions depend on the multivariate interactions of neuronal populations.
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