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ECEN 5831 - Brains, Minds, and Computers

3 credit hours

On-Line Course Materials

Catalog Description: Provides background for the design of artificially intelligent systems based upon our present knowledge of the human brain. Includes similarities and differences between the brain and computers, robots, and common computer models of brain and mind. Emphasizes the neuron as an information processor, and organization of natural as well as synthetic neural networks.

Prerequisite: ECEN 2260, Circuits/Electronics 2

Textbook: E. R. Kandel, J. H. Schwartz, and T. M. Jessel, Essentials of Neural Science and Behavior, Appleton-Lange, plus journal articles and reprints on reserve in the Math/Engineering library.

Course Objectives: To help elucidate what the brain and cognitive sciences can tell us about how to design better computers and what computing science can tell us about the workings, capacities, and limitations of the brain and mind, and to make CU students in Engineering and Neuroscience cognizant of the salient substance in their counterpart fields and of the possibilities for facilitative interaction between them.

Topics:

  1. Brains and natural intelligence: basic neurobiological mechanisms of information processing in single cells and networks. Representative brain systems: the visual and auditory systems. Biological basis of learning and memory. Overall organization, localization and operations of the brain. The emphasis is on understanding these neural circuits from an electrical engineering point of view.
  2. Comparisons of brains, minds, and computers: the relations of brain and mind. The relations of computers to brain and mind.
  3. Computers and artificial intelligence: basic computer hardware and software approaches to intelligent machines, pattern recognition and robots, game playing and "search" strategies, creativity and higher abilities, cognitive neuroscience.
  4. Synthetic neural networks as computational schemes and as possible models of brain function: Overview of artificial neural networks, McCullogh-Pitts model, perceptrons, multi-layer perceptrons, backpropagation, neural net simulators, spin glasses and Hopfield nets, optimization and simulated annealing.
  5. How computers can be used to realistically simulate, and thereby better understand, brain function.
Class Schedule: 3 hours of lecture per week

Contribution of course to meeting Criterion 4, the professional component: This course provides 3 semester hours of electrical engineering topics consisting of engineering sciences and engineering design.

Relationship of course to program outcomes: This course is not required and is not included in outcomes assessment.

Prepared by: H. Wachtel and V. Heuring
May 16, 2005