Course Announcement: Fall 2004

                           Course Title:  Applied System Neuroscience 

                  Instructor: Gerhard Werner, M.D.                            Course Number:    BME 385J
                           Adj. Professor, BME                                           Unique Number:    12815

                                                Time and Place:    M  F  9:30-11,  ENS 145

The Course  addresses  conceptual foundations  of the Neurobehavioral Sciences, relevant computational models, and applications bridging Neural Science and Engineering.  

The following topics will be covered in introductory presentations by the instructor, and through reading assignments for student  presentations and group discussions: the latter forming the principal format of the course.

1) Information processing in the NS:   Theories of Neural Coding: Signal Processing, Predictive Coding,
                                                                 
Neurophysics:  the Nonlinear Dynamics of Neural Processes
                                                                
Bayesian Interpretation of Sensory Messages
                                                                 Processing of Space and Time in the Nervous System
                                                                 Contributions  from Brain Imaging
 

2)  Processing decisions for action:     Perception - Action Coupling  (including Kalman Filter)
                                                               Reinforcement learning in the NS:  Adaptive Critic Engineering
                                                               Inverse Dynamics models (including Markovian)
                                                              
Ecological Robotics , Schema Theory                                                                                 

3)  Neural control of motor behavior:   Neural Circuitry  for Sensory-Motor Transformations
                                                               
Neural Science based Theories of  dynamic/embodied Cognition 
                                                                Synergetic Theories of  Phase Transitions in  Neural Systems  
                                                               
Brain-Machine Interfacing     

In the context of these topics, neuroscience-relevant apects of  Theories of Computation, System-Control and Complexity Theory,  and Self-Organization  will be discussed

Prerequisites: elementary knowledge in Neural Sciences and basic experience with computation. The course is primarily designed for graduates students in the Neurobehavioral Sciences, Cognitive Psychology, Computer Science, Biomedical Engineering, and Physics. The course is also suitable for Undergraduate Students with appropriate background.  Material for bridging gaps in background knowledge and tutorial assistance is  available <see Course Material>.

Course Format: Brief conceptual introductions and overviews by the instructor, followed by discussion of reading assignments and problem solving exercises ( within the objectives of the course, participants also have the opportunity of selecting articles and problems of their own choice).

Course Materials:
        Journal publications will be distributed or, if  available on line, URL's will be announced  in preparation for assigned reading and group discussion.
In addition, the following books are on reserve in the respective libraries . Their purpose is to assist with securing background knowledge,  and to enable students to deepen their understanding of issues of special interest to them.  I will  refer to these sources at the appropriate occasions in the course.  These resources are also intended to  assist with the design and conduct of  individual  projects for presentation to the class.

A: Basic Neuroanatomy and Neurophysiology: (Life Sciences Library)
                D.E. Haines, Fundamental Neuroscience  
                J.G. Nicholls et al 4th edit. From Neuron to Brain
                D. Purves, G.J. Augustine, D. Fitzpatrick, L.C. Katz, A.S. LaMantia,etc.  Neuroscience, 2nd edit, 2001.
                         (notably Ch. 1 & 2 for filling gaps in neurophysiology background knowledge)
B:   Nonlinear Dynamics  and Systems
                S.H. Strogatz: Nonlinear dynamics and chaos.
(PMA Library)
                P. Berge: order within chaos  (PMA Library)
                S.H. Zak: Systems and Control  <with MATLAB  Coded examples  (Engin.Libr)
C: Cellular Biophysics  (Life Science Librray)
                D.Johnston & S.M. Wu:  Foundations of cellular Neurrophysiology
                T. Fischer Weiss : Cellular Biophysics  (with MATLAB sofware for neural and biophysical modeling)
D:  Computational modeling of neurons/neural systems:  (Life Sciences library)

               H.R. Wilson, 
: Spikes, decisions and actions (with MATLAB software)
               L. Abbott & T.J. Sejnowski: Neural Codes and distributed processing.
               P. Dayan &  L. Abbott:  
Theoretical neuroscience.
           A. Weizenfeld, M.A. Arbib, A. Alexander:  The neural Simulation Language  (Models and Code)
               C. Eliasmith & Anderson: Neural Engineering 
(with MATLAB software).
               Sheu & Choi, Neural information processing and VLSI  <presently on order>

E:  Neuroimaging  (Life Science Library)
               J.C. Mazziotta
et al: Brain Mapping Handbk : the disorders 
               R.A. Zimmerman: Neuroimaging : clinical and physical principles

F: Additional Reading:
             N. Osaka: Neural basis of consciousness  (Life Science Librray)
             G.M. Edelman A universe of consciousness : how matter becomes imagination
 (PCL>

Course Grading:  primarliy based on the participation in group discussion and the oral reports on reading assignments. There will be one final written examen paper in which the participant is required to discuss and evaluate an assigned publication  in the light of the criteria explored in the course. Special credits will be given for individual elective projects on course-related  topics.

For information, please contact: gwer1@mail.utexas.edu
                                                      http://www.ece.utexas.edu/~werner/gwerner.html