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The The PerceptronPerceptron: A Probabilistic Model : A Probabilistic Model for Information Storage and for Information Storage and Organization in the brainOrganization in the brain(F. Rosenblatt)(F. Rosenblatt)
Artificial Intelligence2005-21534
Heo, Min-Oh
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(C) 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/
OutlineOutline
IntroductionProbabilistic model on biological Perceptron♦ Predominant phase♦ Postdominant phase
Two helpful approach♦ Bivalent systems♦ Temporal organizations
Conclusion♦ 3 major points
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(C) 2005, SNU Biointelligence Lab, http://bi.snu.ac.kr/
Two Main QuestionsTwo Main Questions
Q1 : In what form is information stored, or remembered?
Q2 : How does information contained in storage, or in memory, influence recognition and behavior?
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Answers: 2 positions (1)Answers: 2 positions (1)
The 1st position ♦ coded representations or images
1-to-1 mapping between sensory stimulus and the stored pattern
♦ Able to discover exactly what an organism remembers by reconstructing the original sensory patterns
♦ Recognition : matching or systematic comparison of the contents of storage with incoming sensory patterns
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Answers: 2 positions (2)Answers: 2 positions (2)
The 2nd position♦ The images of stimuli may never be recorded at all.♦ There is never any simple mapping of the stimulus into memory.
♦ The information is contained in connections or associations.
♦ Recognition : Automatically activating the response without requiring any separate process for their recognition or identification.
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What is the writer going to do?What is the writer going to do?
The need for a suitable language for the mathematical analysis of events.
Formulate the current model of perceptron in terms of probability theory.
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Assumptions for perceptron(1)Assumptions for perceptron(1)
5 Assumptions ♦ 1. At birth, the construction of the most important networks is
largely random, subject to a minimum number of genetic constraints.
♦ 2. The probability is likely to change, due to some relatively long-lasting changes in the neurons.
♦ 3. Exposure to a large sample of stimuli,Similar thing will tend to form pathways to the same sets of responding cells.Dissimilar thing will tend to develop connections to different sets of responding cells.
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Assumptions for perceptron(2)Assumptions for perceptron(2)
♦ 4. The application of positive or negative reinforcement may facilitate or hinder whatever formation of connections is currently in progress.
♦ 5. Similarity depends on physical organization of the perceivingsystem.
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ModellingModelling
Connection♦ Excitatory ♦ Inhibitory
Level♦ S-Points : stimuli impinge on a retina of sensory units♦ A-Units : impulse are transmitted to a set of association cells♦ R-Units : the Response cells which respond as the A-units.
Two phase♦ Predominant Phase♦ Postdominant Phase
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Models of systemsModels of systems
3 types of systems.♦ Alpha system : an active cell simply gains an increment of value
for every impulse, and hold this gain indefinitely.
♦ Beta system : the increments being apportioned among the cells of the source-set in proportion to their activity.
♦ Gamma system : active cells gain in value, so that the total value of source-set is always constant.
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Predominant Phase(1)Predominant Phase(1)
Probability : the Expected proportion of A-units activated by a stimulus of a given size
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Predominant Phase(2)Predominant Phase(2)
Conditional Probability : ♦ an A-unit which responds
to a given stimulus will also respond to another given stimulus
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Predominant Phase(3) : stabilityPredominant Phase(3) : stability
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Predominant Phase(4) : learnablePredominant Phase(4) : learnable
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PostdominantPostdominant Phase(1)Phase(1)
Mathematical Model for learning♦ Reinforcement learning
Two Probabilities♦ Pr : the perceptron will show a bias towards the correct response
in preference to any given alternative response.♦ Pg : the Probability of correct generalization.
The probability that the correct response will be preferred over all alternatives is designated Pr or Pg.
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PostdominantPostdominant Phase(2) Phase(2) : able to find probability Pr, Pg to unity.: able to find probability Pr, Pg to unity.
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Two helpful approachTwo helpful approach
Bivalent systems♦ Two types of reinforcement are possible
( positive and negative )
Temporal organizations♦ If the values of the A-units are allowed to decay at a rate
proportional to their magnitude, The perceptron becomes capable of “spontaneous” concept formation.
♦ Spontaneously recognize the difference between the two classes.
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ConclusionConclusion
3 major points to behaviorism♦ Parsimony
characteristics can clearly be stated.Have potentially measurable physical correlate.
♦ VerifiabilityWe can be considerably more confident of its validity and of itsgenerality than in the case of a theory which must be hand-tailored to meet each situation
♦ Explanatory power and generality