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Unformatted text preview: Preliminaries to BME 575L Preliminaries to BME 575L Teacher: Norberto Grzywacz We’ll post on blackboard.usc.edu: Preliminaries to BME 575L Preliminaries to BME 575L
Syllabus (“Course Information”) Calendar (“Calendar” under “Tools”) Readings (“Course Documents”) PowerPoint files before lectures (“Course Documents”) You better come prepared, since the lectures will be short and you will have to write laboratory reports. Posted lectures may not be identical to lectures given in class, as we often include “surprise” elements to make students think or pay attention in class. Laboratory Materials (“Course Documents”) Computer Projects (“Assignments”) Grading Policy (“Course Information”) Office Hours (“Staff Information”) “Discussion Board” Grading Policy Grading Policy Grades will be weighted as follows: Laboratory Reports: 20% Computer Modeling Projects: 20% Laboratory reports (< 300 words) will be due at the beginning of the next class We will post solutions on the class web page and will not accept late assignments We will assign two programming projects The design of the projects is such that they should take between 15 and 20 hours of work to complete Projects should be implemented using MATLAB with Simulink We will post solutions on the class web page and will not accept late assignments Exam 1: 20% (2/12; 9:3010:50 am) Exam 2: 20% (3/26; 9:3010:50 am) Exam 3: 20% (5/12; 810 am) Office Hours Office Hours Tuesday and Thursday from 10:50 to 11:30 am These are extensions to the class, allowing you to finish the lab with us present and helping you! Norberto will also have office hours on Mondays from 9:30 to 10:10 am (HNB 120H). TA will be Bardia Behabadi. His office hours will be on Wednesday in this lab. What is the best time for you? Textbook Textbook The textbook for this course is "Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems" (P. Dayan & L.F. Abbott; 2001; MIT Press, Cambridge, MA). You can get PDF files of the chapters of this book for free at http://cognet.mit.edu/library/books/view? isbn=0262041995, using the USC computer resources (on a campus or through VPN). Outline of this Lecture Outline of this Lecture Levels of Computation (Course Organization) Neurons Neural Networks Computational Theory One Must Understand One Must Understand Neural Computation at Multiple Levels An example of a recognition model postulates object representation as spatial arrangements among view-invariant parts (Geons). Is this level of understanding enough to grasp the brain? No! Neurons constrain computations and so, we want to understand neural implementation. Some cells in the Inferior Temporal cortex are selective to shapes that resemble geons! One finds sub-regions preferring different types of objects. How do they do it? Neurons have complex structures, and vary across brain areas and across functions. An example of a model of a dendritic tree considers membrane segments with timedependent conductances. Not only is the neural structure complex but the physiology, too, even in single neurons. Suppose you had recordings for all neurons, would you know how we perceive things? Would we even know how the nonlinear voltages in this neuron arose? David Marr’s three levels of understanding computations. Another level of complexity arises from circuits! This circuit’s function depend on nonlinear synaptic mechanisms. Different oscillations arise in this circuit, although individual neurons are simple. Organization of the Course Organization of the Course Single Neurons and Synapses Small Biological Neural Networks Large Neural Systems Summary To understand the brain, one must formulate computational theories and understand neural constraints. Marr expanded this notion to three levels of understanding, including representation as an intermediate step. Another level of complexity arises by interactions in neural networks. One Must Understand One Must Understand Neural Computation at Multiple Levels ...
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- Spring '09