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Welcome to the Learning Concepts of Neural Networks MCQs Page

Dive deep into the fascinating world of Learning Concepts of Neural Networks with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Learning Concepts of Neural Networks, a crucial aspect of Neural Networks. In this section, you will encounter a diverse range of MCQs that cover various aspects of Learning Concepts of Neural Networks, 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.

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

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Learning Concepts of Neural Networks MCQs | Page 3 of 5

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Q21.
Connections across the layers in standard topologies and among the units within a layer can be organised?

a.

in feedforward manner

b.

in feedback manner

c.

both feedforward and feedback

d.

either feedforward and feedback

Discuss
Answer: (d).either feedforward and feedback
Q22.
What is an instar topology?

a.

when input is given to layer F1, the the jth(say) unit of other layer F2 will be activated to maximum extent

b.

when weight vector for connections from jth unit (say) in F2 approaches the activity pattern in F1(comprises of input vector)

c.

can be either way

d.

none of the mentioned

Discuss
Answer: (a).when input is given to layer F1, the the jth(say) unit of other layer F2 will be activated to maximum extent
Q23.
What is an outstar topology?

a.

when input is given to layer F1, the the jth(say) unit of other layer F2 will be activated to maximum extent

b.

when weight vector for connections from jth unit (say) in F2 approaches the activity pattern in F1(comprises of input vector)

c.

can be either way

d.

none of the mentioned

Discuss
Answer: (b).when weight vector for connections from jth unit (say) in F2 approaches the activity pattern in F1(comprises of input vector)
Q24.
The operation of instar can be viewed as?

a.

content addressing the memory

b.

memory addressing the content

c.

either content addressing or memory addressing

d.

both content and memory addressing

Discuss
Answer: (a).content addressing the memory
Q25.
The operation of outstar can be viewed as?

a.

content addressing the memory

b.

memory addressing the content

c.

either content addressing or memory addressing

d.

both content and memory addressing

Discuss
Answer: (b).memory addressing the content
Q26.
If two layers coincide and weights are symmetric(wij=wji), then what is that structure called?

a.

instar

b.

outstar

c.

autoassociative memory

d.

heteroassociative memory

Discuss
Answer: (c).autoassociative memory
Q27.
Heteroassociative memory can be an example of which type of network?

a.

group of instars

b.

group of oustar

c.

either group of instars or outstars

d.

both group of instars or outstars

Discuss
Answer: (c).either group of instars or outstars
Q28.
What is STM in neural network?

a.

short topology memory

b.

stimulated topology memory

c.

short term memory

d.

none of the mentioned

Discuss
Answer: (c).short term memory
Q29.
What does STM corresponds to?

a.

activation state of network

b.

encoded pattern information pattern in synaptic weights

c.

either way

d.

both way

Discuss
Answer: (a).activation state of network
Q30.
What LTM corresponds to?

a.

activation state of network

b.

encoded pattern information pattern in synaptic weights

c.

either way

d.

both way

Discuss
Answer: (b).encoded pattern information pattern in synaptic weights
Page 3 of 5

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