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Supervised Hebbian Learning. Hebb’s Postulate “When an axon of cell A is near enough to excite a cell B and repeatedly or persistently takes part in firing.

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Presentation on theme: "Supervised Hebbian Learning. Hebb’s Postulate “When an axon of cell A is near enough to excite a cell B and repeatedly or persistently takes part in firing."— Presentation transcript:

1 Supervised Hebbian Learning

2 Hebb’s Postulate “When an axon of cell A is near enough to excite a cell B and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that A’s efficiency, as one of the cells firing B, is increased.” D. O. Hebb, 1949 A B

3 Linear Associator

4 Hebb Rule

5 Batch Operation

6 Performance Analysis

7 Example

8 Pseudoinverse Rule - (1)

9 Pseudoinverse Rule - (2)

10 Relationship to the Hebb Rule

11 Example

12 Autoassociative Memory

13 Tests

14 Variations of Hebbian Learning

15 MATLAB Neural Network Tool box

16

17 Batch mode (Train) full propagation

18

19 On Line (learn) back propagation or incremental

20 Newff

21 Example

22 ADAPT Using [net,Y,E]=adapt(net,P,T) You can find more in neural network tool box


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