Biologically Inspired Intelligent Systems

1. Biologically Inspired Intelligent Systems Lecture 1 Dr. Roger S. Gaborski

2. Where to Find Me • Office: 70 – 3647 • Office Hours: • Tuesday and Thursday, 11am-noon in my lab or office • My appointment • My lab 70-3400 • Email: rsg@cs.rit.edu

3. How will the course be run? • Combination of Lectures, Videos and Student Discussions/Presentations • Lecture notes supplemented with material from the Internet • Downloadable textbook for computational algorithms. • Reading Assignments -Internet websites and journal articles • Programming Homework Assignments in MATLAB – individual and team assignments • Quizzesand Exams

4. Required Background • Do I need to know • MATLAB? • Biology? • Chemistry? • Electrical Engineering? • Differential Equations?

5. Course Webpage • www.cs.rit.edu/~rsg • For MATLAB tutorial - Register at: http://www.mathworks.com/academia/student_center/tutorials/launchpad.html • http://etools.fernuni.ch/matlab/matlab1/en/html/startpage.html

6. 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

7. Biology  Algorithms • Brain  Artificial Neural Networks • Feed forward neural networks • Self organizing networks • etc • Evolutionary biology  Evolutionary Algorithms • Immune system  Artificial Immune systems • Collective Social Interactions  Swarm Intelligence • Annealing Metals – Simulated Annealing • Synthesize life-like behaviors and creatures Artificial Life • Model Structures Cellular Automata, L-systems Roger S. Gaborski

8. What can we learn from biological systems? • Consider • primate brains • insect anatomy and behavior, such as, ants, bees, fireflies) • Can we gain insight for designing systems? • Vision systems • Speech systems • Computational models • Search algorithms

9. Major Topics • Overview of Brain Structure and Function • Neural Networks – standard learning algorithms • Model of Visual System • Evolutionary Algorithms inspired by biological systems • Evolving neural Networks using Evolutionary Algorithms • Cellular Automata models

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

11. For example – vision systems • Our visual system is the result of evolution • One approach would be to study the anatomy of a visual system (primate, insect) and try to understand how it functions – see next slide • Develop a software visual system that has some of the characteristics of the visual system’s anatomy

12. Visual System http://scien.stanford.edu/ pages/labsite/2006/psych221/ projects/06/cukur/intro_files/ image021.jpg Roger S. Gaborski

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

14. Auditory System:Auditory Cortex Roger S. Gaborski

15. Auditory Pathway http://products.cochlearamericas.com/ sites/default/files/images/ auditory-pathwway.img_ assist_custom-366x471.png Roger S. Gaborski

16. Other examples • Use biological processes as a model for developing computational systems • Apply biological genetic processes to develop algorithms, such as, computational genetic algorithms and evolutionary strategies • Use biological behavior to develop algorithms to solve problems • Search algorithms based on firefly or bee behavior • Develop artificial life algorithms • Gain insight into complex information processing in living organisms • Gain a better understanding of structural form that could be applied in robotics

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

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

19. Abstract level-Consciousness • From Wikipedia: “Consciousness is the quality or state of being aware of an external object or something within oneself” • “Examine the relationship between the experiences reported by subjects and the activity that simultaneously takes place in their brains”

20. Abstract level-Consciousnes • Study the neural correlates of consciousness. • Find that activity in a particular part of the brain, or a particular pattern of global brain activity, will be strongly predictive of conscious awareness. • Several brain imaging techniques, such as EEG and fMRI, have been used for physical measures of brain activity in these studies

21. REF: Christof Koch (2004)

22. Function Level- Brain Structure and Function • How did the brain evolve? • How is the brain organized? • How are major functions implemented? • How is information processed? • How can we develop algorithms inspired by brain processes and operation

23. A Few Basic Ideas:Brain Model (Paul MacLean-1960) • An early model of the brain • Three major components have evolved: • Reptilian Complex (first) • Limbic System (second) • Neocortex (last) • All layers interact • http://www.buffalostate.edu/orgs/bcp/brainbasics/triune.html

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

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

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

27. Four Major Lobes http://www.scientificpsychic.com/workbook/chapter5.htm

28. What Each Lobe Does (partial list) • Frontal Lobe • Higher mental processes • Longer term memories that are not task related • Parietal Lobe • Integrates sensory information from different modalities

29. What Each Lobe Does,continued • Occipital Lobe • Visual processing • Temporal Lobe • Auditory perception (hearing, speech)

30. Question? • How does information get to the brain?

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

32. 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

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

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

35. 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

37. Are there locations in the brain that are more responsive to face images or images of houses?

38. Where Do We Recognize Faces? • Fusiform Gyrus (located in temporal lobe) is responsible for: • Face recognition • More responsive to faces, less responsive to stimuli containing objects, scenes and houses http://labnic.unige.ch/nic/htms/fmri.html

39. Recognition of Scenes • Parahippocampal place area (PPA) • Subregion of the parahippocampal cortex • Encoding and Recognition of scenes • Area becomes active when individual views images of landscapes, cityscapes or places http://labnic.unige.ch/nic/htms/fmri.html

40. Also, Fusiform Gyrus • Fusiform Gyrus also responsible for: • Word recognition • “Visual Word Formation Area” – groups of letters are joined into integrated visual percepts • Within 250 ms we can recognize a word in script of print, regardless of font and size • With-in category classification • Processing color information

41. REFERENCES: • “The visual word form area: expertise for reading in the fusiform gyrus” • Bruce D. McCandliss, Laurent Cohen and Stanislas Dehaene • TRENDS in Cognitive SciencesVol.7 No.7 July 2003 • “Culture differences in neural processing of faces and houses in the ventral visual cortex” • Joshua O. S. Goh, Eric D. Leshikar, Bradley P. Sutton, Jiat Chow Tan Sam K. Y. Sim, Andrew C. Hebrank, and Denise C. Park • Social Cognitive and Affective Neuroscience, 2010 • “The Parahippocampal Place Area Responds Preferentially to High Spatial Frequencies in Humans and Monkeys” • Reza Rajimehr, Kathryn J. Devaney, Natalia Y. Bilenko, Jeremy C. Young, Roger B. H. Tootell • PLOS Biology (open access journal)

42. Other Functions • Memory • Planning • Problem Solving

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

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

45. Real Neurons www.alanturing.net/

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

47. 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

48. 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

49. Simple Model

50. But to build even the simple neuron model we need data • How do we get information about the brain? • In humans, imaging, effects of brain damage • Primates and other creatures – imaging, electrodes