1. Chapter 4. Analysis of Brain-Like Structures and Dynamics (2/2) Creating Brain-Like Intelligence, Sendhoff et al. Course: Robots Learning from Humans 09/25 (Fri) JeHwan Ryu Biointelligence Laboratory School of Computer Science and Engineering Seoul National University http://bi.snu.ac.kr

2. Contents 1. Introduction 2. Structural Analysis 3. Dynamical States 4. Quantitative Assessments : Complexity Measures 5. Information Processing 6. Learning(Hebbian) Summary 2

3. 4. Quantitative Assessments : Complexity Measures We do not know what to look for Distinguish : subtle patterns or uncorrelated chaotic dynamics? Complexity measures Search for higher order correlations Theory of information geometry Complex system = more than the sum of its parts © 2015, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr 3

4. 5. Information Processing Question : Backward analysis : Where is the percept coming from? Forward analysis : Which percepts can an input generate? Backward analysis Several inputs may lead to the same percept Decoding is ambiguous © 2015, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr 4

5. Backward Analysis © 2015, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr 5

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7. Conceptual Problems © 2015, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr 7

8. Forward Analysis © 2015, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr 8

9. 6. Learning(Hebbian) © 2015, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr 9

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11. Learning(Hebbian) © 2015, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr 11

12. Summary Brains are assemblies of neurons connected in specific patterns and according to specific rules Relevant information is extracted from stimuli ↔ Inputs are selected based on hypotheses about the stimuli Internal dynamics reflect a long history of past inputs © 2015, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr 12

13. Discussion Can hebbian learning works well with artificial neural network? © 2015, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr 13