Bayesian Brain

Presented by Nguyen Duc Thang

Contents

Introduction Bottom-up approach Top-down approach Vision recognition, brain computer interface

(BCI), and artificial general intelligence (AGI)

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

Bayes rule

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

Bayes rule

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

Two approaches of Bayesian brain Bottom-up approach

How the brain works? Top-down approach

Machine intelligence When two approaches meet together?

Bottom-up approach

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

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

Blue brain project

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

Interesting results

Automatically discover structure form, ontology, causal relationships

Kemp C., Tenenbaum J. B. “The discovery of structural form”, PNAS 2008

Related researches

Vision recognition Brain computer interface (BCI) Artificial general intelligence (AGI)

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

Vision recognition

Classify animal and non-animal

Results

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

What is next: beyond the feedforward models

Hierarchy Bayesian inference

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

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

My opinion