Neural Network Questions and Answers – Topology

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This set of Neural Networks Multiple Choice Questions & Answers (MCQs) focuses on “Topology″.

1. In neural how can connectons between different layers be achieved?
a) interlayer
b) intralayer
c) both interlayer and intralayer
d) either interlayer or intralayer
View Answer

Answer: c
Explanation: Connections between layers can be made to one unit to another and within the units of a layer.

2. Connections across the layers in standard topologies & among the units within a layer can be organised?
a) in feedforward manner
b) in feedback manner
c) both feedforward & feedback
d) either feedforward & feedback
View Answer

Answer: d
Explanation: Connections across the layers in standard topologies can be in feedforward manner or in feedback manner but not both.

3. 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
View Answer

Answer: a
Explanation: Restatement of basic definition of instar.
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4. 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
View Answer

Answer: b
Explanation: Restatement of basic definition of outstar.

5. 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 & memory addressing
View Answer

Answer: a
Explanation: Because in instar, when input is given to layer F1, the the jth(say) unit of other layer F2 will be activated to maximum extent.

6. 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 & memory addressing
View Answer

Answer: b
Explanation: Because in outstar, when weight vector for connections from jth unit (say) in F2 approaches the activity pattern in F1(comprises of input vector).

7. If two layers coincide & weights are symmetric(wij=wji), then what is that structure called?
a) instar
b) outstar
c) autoassociative memory
d) heteroassociative memory
View Answer

Answer: c
Explanation: In autoassociative memory each unit is connected to every other unit & to itself.
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8. 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
View Answer

Answer: c
Explanation: Depending upon the flow, the memory can be of either of the type.

9. What is STM in neural network?
a) short topology memory
b) stimulated topology memory
c) short term memory
d) none of the mentioned
View Answer

Answer: c
Explanation: Full form of STM.
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10. What does STM corresponds to?
a) activation state of network
b) encoded pattern information pattern in synaptic weights
c) either way
d) both way
View Answer

Answer: a
Explanation: Short-term memory (STM) refers to the capacity-limited retention of information over a brief period of time,hence the option.

11. What LTM corresponds to?
a) activation state of network
b) encoded pattern information pattern in synaptic weights
c) either way
d) both way
View Answer

Answer: b
Explanation: Long-term memory (LTM-the encoding and retention of an effectively unlimited amount of information for a much longer period of time) & hence the option.

Sanfoundry Global Education & Learning Series – Neural Networks.

To practice all areas of Neural Networks, here is complete set on 1000+ Multiple Choice Questions and Answers.

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Manish Bhojasia, a technology veteran with 20+ years @ Cisco & Wipro, is Founder and CTO at Sanfoundry. He lives in Bangalore, and focuses on development of Linux Kernel, SAN Technologies, Advanced C, Data Structures & Alogrithms. Stay connected with him at LinkedIn.

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