IST4_lec17

IST4_lec17 - IST 4 Information and Logic IST 4: Planned...

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IST 4 Information and Logic
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1 M1 31 6 2 6 5 26 19 M2 5 4 12 4 5 3 M2 28 3 2 21 2 1 14 M1 7 fri thr wed tue mon IST 4: Planned Schedule – Spring 2008 x= hw#x out x= hw#x due = today T Mx= MQx out Mx= MQx due T MQ1 MQ2 Students’ presentations
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Biologically Inspired Functional Gates Warren McCulloch 1899 - 1969 Walter Pitts 1923 - 1969 Neurophysiologist, MD Their collaboration let to the 1943 seminal neural networks paper: A Logical Calculus of Ideas Immanent in Nervous Activity Logician, Autodidact Neural networks and Time Threshold Logic State Machines Logic Memory
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Associative Memory 1968 Alexander Luria 1902- 1977, Moscow Solomon Shereshevskii ( S ) 1886 – 1958, Moscow Luria studied the memory of S from the mid 1920s for about thirty years. .. This research account is documented in a nice little book
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An Amazing Memory Information Recall is Associative
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Linear Threshold Some Adjustments Linear Threshold (LT) gate 1 -1 -t -t t hreshold
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The AND function of two variables: -1-1 1-1 -11 11 -3 -1 -1 1 Linear Threshold Example ???
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Linear Threshold with Memory A memory nose Remembers the last f( X ) Elephants are symbols of wisdom in Asian cultures and are famed for their exceptional memory
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Feedback Networks Example -1 -1 0 0 weights thresholds The state of the network : the vector that corresponds to the states (noses…) of the gates
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Feedback Networks Example -1 -1 0 0 11 1 2 Label the gates
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Feedback Networks Example -1 -1 0 0 11 1 2 1
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Feedback Networks Example -1 -1 0 0 -1 1 1 2 1
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Feedback Networks Example -1 -1 0 0 -1 1 1 2 -1 -11 is a stable state
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Feedback Networks Example -1 -1 0 0 11 1 2
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Feedback Networks Example -1 -1 0 0 11 1 2 1
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Feedback Networks Example -1 -1 0 0 1- 1 1 2 1
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Feedback Networks Example -1 -1 0 0 1- 1 1 2 -1 1-1 is a stable state
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The node that computes Feedback Networks Example -1 -1 0 0 11 1 2 11 State transition diagram (state space) -11 -1-1 1-1 2 1 state Q: Is -1-1 a stable state?
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Feedback Networks Example -1 -1 0 0 -1 -1 1 2 -1 Answer: No
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Feedback Networks Example -1 -1 0 0 1- 1 1 2 -1 11 -11 -1-1 1-1 2 1 1
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Feedback Networks Example -1 -1 0 0 -1 -1 1 2 -1 11 -11 -1-1 1-1 2 1 1
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Feedback Networks Example -1 -1 0 0 -1 1 1 2 -1 11 -11 -1-1 1-1 2 1 1 2
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Feedback Networks Example -1 -1 0 0 -1 1 1 2 11 -11 -1-1 1-1 2 1 1 2 stable states Q: why care about stable states?
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Feedback Networks Computing with Dynamics 11 -11 -1-1 1-1 2 1 1 2 stable states Feedback Network Input: initial state Output: stable state 11 -11
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Feedback Networks Computing with Dynamics 11 -11 -1-1 1-1 2 1 1 2 stable states Feedback Network Input: initial state Output: stable state 11 1-1
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Feedback Networks Computing with Dynamics Feedback Network Input: initial state Output: stable state Associative Memory “The Leibniz-Boole Machine”
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Feedback Networks Computing with Dynamics Feedback Network Input: initial state Output: stable state Associative Memory “The Leibniz-Boole Machine”
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Feedback Networks Computing with Dynamics Feedback Network Input: initial state Output: stable state Associative Memory “The Leibniz-Boole Machine”
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Feedback Networks Computing with Dynamics Feedback Network Input: initial state Output: stable state Associative Memory “The Leibniz-Boole Machine”
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Feedback Networks
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This note was uploaded on 12/12/2009 for the course IST 4 taught by Professor Shuki during the Spring '09 term at Caltech.

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IST4_lec17 - IST 4 Information and Logic IST 4: Planned...

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