Biologically Inspired Intelligent Systems

1. Biologically Inspired Intelligent Systems Lecture 1 Dr. Roger S. Gaborski Tuesday / Thursday: 10:00am-11:50am Room: GOL (70)- 1445

2. Where to Find Me • Office: 70 – 3647 • Office Hours:TBD • My lab 70-3400 • Email: rsg@cs.rit.edu

3. Teaching Assistant • Yuheng Wang E-Mail: wangyuheng.j@gmail.com • Office: 70-3659

4. How will the course be run? • Combination of Lecture and Discussion • No textbook. Lecture notes supplemented with material from the Internet (videos) • Reading Assignments -Internet websites and journal articles • Programming Homework Assignments in MATLAB • Quizzes, in-Class Assignments, Exam(s) and Final • Research Paper (4005- 753)

5. Major Topics • Brain Structure and Function • Optimization Algorithms inspired by biological systems • Artificial Life

6. Major Topics - 1 • Brain Structure and Function (lecture and videos) • How is the brain organized? • How are major functions implemented? • Development of algorithms inspired by brains operation • Optimization Algorithms inspired by biological systems • Artificial Life

7. Major Topics - 2 • Brain Structure and Function • Optimization Algorithms inspired by biological systems (mostly lectures) • Genetic Algorithms • Evolutionary Strategies • Artificial Life

8. Major Topics - 3 • Brain Structure and Function • Optimization Algorithms inspired by biological systems • Artificial Life (lectures and videos) • Goal is to understand complex information processing undertaken by living systems

9. Course Webpage • www.cs.rit.edu/~rsg • For MATLAB tutorial - Register at: http://www.mathworks.com/academia/student_center/tutorials/launchpad.html • Do I need to know • Biology? • Chemistry? • Electrical Engineering? • Differential Equations?

10. Why Study Biological Systems? • Evolution and the quest to survive • Humans, animals and insects excel at problem solving • Individually • Groups • Humans – raising children, hunting, business • Animals – raising young, hunting • Insects – food gathering by ants • Apply knowledge gained from studying biological systems to engineering problems

11. Brain Model (Paul MacLean) • Three major components have evolved: • Reptilian Complex • Limbic System • Neocortex • All layers interact • http://www.buffalostate.edu/orgs/bcp/brainbasics/triune.html

12. Brain Model (Paul MacLean) • Reptilian Complex • Brain stem and cerebellum • Physical survival and maintenance of the body • Automatic behaviors

13. Brain Model (Paul MacLean) • Limbic System • Centers for emotion • Memory

14. Brain Model (Paul MacLean) • Neocortex • Outer portion of the brain • Responsible for: • Language • Logical Thinking • Planning • Sensory processing

15. How are computations performed in the brain? • NEURONS :Basic Computational component of the brain

16. Real Neurons www.alanturing.net/

17. How Do Real Neurons ‘Operate’ ? http://ei.cs.vt.edu/~history/NEURLNET.HTML

18. Simple Model

19. Questions? • How does information get to the brain? • What are the functions performed by each region of the brain? • How are the functions performed?

20. How does information get to the brain? • Receptors • Modalities: Light, Smell, Tastes, Sounds, Touch • Cellular structures that transform physical information into electrical impulses

21. Functional Organization, continued • Information from sensors in the head are carried by the cranial nerves to the brain • Information from the body is carried by peripheral nerves to the spinal column and then by the axons in the spinal column

22. Cranial Nerves http://www.gwc.maricopa.edu/class/bio201/cn/cranial.htm

23. Cranial Nerves http://www.gwc.maricopa.edu/class/bio201/cn/cranial.htm

24. Primary Areas for Each Modality • There are primary areas in the cortex for each perception modality • Information from each modality is sent to a specific region of the cortex • Damage to these areas results in loss (partial or complete) of that modality

26. Example – Visual Cortex http://thebrain.mcgill.ca/

27. Other Functions • Memory • Planning • Problem Solving

28. Computational Neuroscience • Major focus  development and evaluation of models • Use computers because complexity of models make analytical analysis intractable, but analytical studies can provide deeper insight into features of models and the reasons behind numerical findings

29. Computational Neuroscience-2 • Develop and test hypotheses about functional mechanisms of the brain Speculate how The brain does Something Develop hypotheses Realize model Test against experimental data Evaluate analytically Or numerically

30. Levels of OrganizationWhich level do we want to investigate? Adapted from “The Computational Brain,” Churchland and Sejnowski

31. Levels of Implementation • Neuron level • Design individual neuron models • Design individual ‘circuits’ • Train networks of neurons • Evolve Networks • Function level • Algorithmic implementation of functions (hearing, vision, etc) • Abstract level • AI techniques

32. VIDEO • Brain Anatomy and Functions http://www.youtube.com/watch?v=HVGlfcP3ATI&feature=related • Be able to locate and describe the function of the following areas: • Cerebrum • Brain Stem • Cerebellum • Frontal lobe • Parietal lobe • Occipital lobe • Temporal lobe 

33. VIDEO • Carl Sagan on Human Brain http://www.youtube.com/watch?v=5SHc67Hep48&NR=1&feature=fvwp • Be able to locate and describe the function of the following areas: • Reptilian Complex • Limbic System • Neocortex • Left Hemisphere • Right Hemisphere (how does the function differ from the left?) • What structure connects the left and right hemispheres • Who is Carl Sagan?

34. VIDEO • How the Body Works : The Regions of the Brain http://www.youtube.com/watch?v=g6KpIrKCDwg&feature=related • What are the three major functional and anatomical parts of the brain? • Which components of the brain make up each part? • What are the functions of each component? • What is the Corpus Callosum

35. Study the material on the following website: • http://serendip.brynmawr.edu/bb/kinser/Home1.html