1. Bayesian Brain Presented by Nguyen Duc Thang

2. Contents Introduction Bottom-up approach Top-down approach Vision recognition, brain computer interface (BCI), and artificial general intelligence (AGI)

3. Introduction Old dream of all philosophers and more recently of AI:  understand how the brain works  make intelligent machines T. Poggio “Visual recognition in primates and machines”, NIPS’07 tutorial

4. Bayes rule K. Kording “Decision Theory: What "Should" the Nervous System Do?”, Science 26 Oct. 2007

5. Bayes rule

6. Free energy and brain Any adaptive change in the brain will minimize the free- energy, this is correspondent to Bayesian inference process: make prediction about the world and update based on what it senses Friston K., Stephan KE. “Free energy and the brain”, Synthese, 2007

7. Two approaches of Bayesian brain Bottom-up approach  How the brain works? Top-down approach  Machine intelligence When two approaches meet together?

8. Bottom-up approach

10. Bayesian population code - Single neural: the spike counts satisfy the Poisson distribution - A group of neural: decode the stimulus by Gaussian distribution Ma W.J.,Beck J., Latham P., Pouget A. “Bayesian inference with probabilistic population codes”, Nature Neuroscience, 2006

11. Bayesian inference Beck J., Ma W.J., Kiani R., Hanks T., Churchland A.K., Roitman L., Shadlen M.N., Latham P., Pouget A. “Probabilistic population codes for Bayesian decision making ”, Neuron, 2008 Sum of two population codes is equivalent to taking the product of their encoded distributions

12. Blue brain project

13. Top-down approach Machine intelligence Is based on the Bayes theorem, build a probabilistic framework for one specific problem, and apply Bayesian inference to find solutions Bayesian inference: belief propagation, variational method, and non-parametric method Some journals: IJCV, PAMI, CVIU, JMLR

14. Interesting results Automatically discover structure form, ontology, causal relationships Kemp C., Tenenbaum J. B. “The discovery of structural form”, PNAS 2008

15. Related researches Vision recognition Brain computer interface (BCI) Artificial general intelligence (AGI)

16. David Hunter Hubel (born February 27, 1926) was co-recipient with Torsten Wiesel of the 1981 Nobel Prize in Physiology or Medicine, for their discoveries concerning information processing in the visual systemFebruary 271926Torsten WieselNobel Prize in Physiology or Medicinevisual system

17. Vision recognition

18. Classify animal and non-animal

19. Results Serre T., Oliva A., Poggio T. “A feedforward architecture accounts for rapid categorization”, PNAS 2007

20. What is next: beyond the feedforward models

21. Hierarchy Bayesian inference

22. Brain-Computer interface (BCI) A brain–computer interface (BCI), sometimes called a direct neural interface or a brain–machine interface, is a direct communication pathway between a brain and an external devices Invasive BCI: direct brain implants restore sight for blindness, hand-control for persons with paralysis Non-invasive BCI: EEG, MEG, MRI Interesting results: research developed in the Advanced Telecommunications (ATR) Computational Neuroscience LAB in Kyoto, Japan allowed the scientists to reconstruct images directly from the brain and display them on a computer. Miyawaki Y., “Decoding the mind’s eye-visual image reconstruction from human brain activity using a combination of multiscale local image decoders”, Neuron Dec.2008

23. Artificial General Intelligence (Strong AI) Weak AI only claims that machines can act intelligently. Strong AI claims that a machine that acts intelligently also has mind and understands in the same sense people do More information on the AGI conference 2009 Prediction: singularity in 2045 Two different opinions  I, robot (2004) Eagle eye (2008)  Cyborg girl (2008) Doraemon

24. My opinion