Welcome to the Activation and Synaptic Dynamics MCQs Page

Dive deep into the fascinating world of Activation and Synaptic Dynamics with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Activation and Synaptic Dynamics, a crucial aspect of Neural Networks. In this section, you will encounter a diverse range of MCQs that cover various aspects of Activation and Synaptic Dynamics, from the basic principles to advanced topics. Each question is thoughtfully crafted to challenge your knowledge and deepen your understanding of this critical subcategory within Neural Networks.

Check out the MCQs below to embark on an enriching journey through Activation and Synaptic Dynamics. Test your knowledge, expand your horizons, and solidify your grasp on this vital area of Neural Networks.

Note: Each MCQ comes with multiple answer choices. Select the most appropriate option and test your understanding of Activation and Synaptic Dynamics. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Activation and Synaptic Dynamics MCQs | Page 7 of 8

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02

Learning Concepts of Neural Networks

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Feedforward Neural Networks

04

Feedback Neural Networks

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Competitive Learning Neural Networks

06

Applications of Artificial Neural Networks

Q61.

Convergence refers to equilibrium behaviour of activation state?

YES

May be YES or NO

Can't Say

Discuss

Answer: (b).NO

Q62.

What leads to minimization of error between the desired and actual outputs?

stability

convergence

either stability or convergence

none of the mentioned

Discuss

Answer: (b).convergence

Q63.

Stability is minimization of error between the desired and actual outputs?

YES

May be YES or NO

Can't Say

Discuss

Answer: (b).NO

Q64.

How many trajectories may terminate at same equilibrium state?

many

none

Discuss

Answer: (c).many

Q65.

If weights are not symmetric i.e cik =! cki, then what happens?

network may exhibit periodic oscillations of states

no oscillations as it doesn’t depend on it

system is stable

system in practical equilibrium

Discuss

Answer: (a).network may exhibit periodic oscillations of states

Q66.

Is pattern storage possible if system has chaotic stability?

YES

May be YES or NO

Can't Say

Discuss

Answer: (a).YES

Q67.

If states of system experience basins of attraction, then system may achieve what kind of stability?

fixed point stability

oscillatory stability

chaotic stability

none of the mentioned

Discuss

Answer: (c).chaotic stability

Q68.

What is an objective of a learning law?

to capture pattern information in training set data

to modify weights so as to achieve output close to desired output

it should lead to convergence of system or its weights

all of the mentioned

Discuss

Answer: (d).all of the mentioned

Q69.

A network will be useful only if, it leads to equilibrium state at which there is no change of state?

YES

May be YES or NO

Can't Say

Discuss

Answer: (a).YES

Q70.

Lyapunov function is vector in nature?

YES

May be YES or NO

Can't Say

Discuss

Answer: (b).NO

Page 7 of 8

Welcome to the Activation and Synaptic Dynamics MCQs Page

Activation and Synaptic Dynamics MCQs | Page 7 of 8

Explore more Topics under Neural Networks

Basics of Artificial Neural Networks

Learning Concepts of Neural Networks

Feedforward Neural Networks

Feedback Neural Networks

Competitive Learning Neural Networks

Applications of Artificial Neural Networks

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