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Bayesian Brain

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Bayesian Brain Presented by Nguyen Duc Thang – PowerPoint PPT presentation

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Title: Bayesian Brain


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, NIPS07 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
9
(No Transcript)
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
Sum of two population codes is equivalent to
taking the product of their encoded distributions
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
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 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 braincomputer interface (BCI), sometimes
    called a direct neural interface or a
    brainmachine 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 minds 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
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