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An input signal of unit amplitude is applied repeatedly to a synaptic connection whose initial value is also unity. Calculate the variation in the synaptic weight with time using the following two rules: (a) The simple form of Hebb's rule described in Eq. (2.9) assuming the learning-rate parameter n = 0.1. b) The covariance rule described in Eq. (2.10), assuming that the presynaptic activity r = 0 and the postsynaptic activity y = 1.0.

An input signal of unit amplitude is applied repeatedly to a synaptic connection whose initial value is also unity. Calculate the variation in the synaptic weight with time using the following two rules: (a) The simple form of Hebb's rule described in Eq. (2.9) assuming the learning-rate parameter n = 0.1. b) The covariance rule described in Eq. (2.10), assuming that the presynaptic activity r = 0 and the postsynaptic activity y = 1.0.

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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An input signal of unit amplitude is applied repeatedly to a synaptic connection whose
initial value is also unity. Calculate the variation in the synaptic weight with time using
the following two rules:

(a) The simple form of Hebb's rule described in Eq. (2.9) assuming the learning-rate
parameter n = 0.1.
b) The covariance rule described in Eq. (2.10), assuming that the presynaptic activity
r = 0 and the postsynaptic activity y = 1.0.

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