4_9_09_InformationTheory

# 4_9_09_InformationTheory - Outline of the Lecture...

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Outline of the Lecture Outline of the Lecture Information Theory Information Theory Entropy                   Noise Entropy                   Mutual Information Entropy                   Noise Entropy                   Mutual Information

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Mutual Information  Mutual Information  Measures How Much  Measures How Much  Responses Tell about  Responses Tell about  Stimuli Stimuli
In a black-box model, we try to describe a system well enough to predict its responses without knowing what is inside the system.

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If the firing is different when one presents the same stimulus twice, then how does the brain know what is in the stimulus?
We can also ask how much a system can tell about the ensemble of stimuli in the world; the answer comes from information theory.

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Let’s represent the probability of each input, P(s), and each output, P(r), by color brightness. Input Variable (s) Output Variable (r)
Let’s represent the probability of an output given an input, P(r|s), by an arrow thickness. Input Variable (s) Output Variable (r)

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Each input variable has its own probabilities to lead to different outputs. Input Variable (s) Output Variable (r)
If the output distribution is sharp, then the output is not informative about the input.

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