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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.

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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 6 of 8

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Q51.
How many types of reinforcement learning exist?

a.

2

b.

3

c.

4

d.

5

Discuss
Answer: (b).3
Q52.
What is fixed credit assignment?

a.

reinforcement signal given to input-output pair don’t change with time

b.

input-output pair determine probability of postive reinforcement

c.

input pattern depends on past history

d.

none of the mentioned

Discuss
Answer: (a).reinforcement signal given to input-output pair don’t change with time
Q53.
What is probablistic credit assignment?

a.

reinforcement signal given to input-output pair don’t change with time

b.

input-output pair determine probability of postive reinforcement

c.

input pattern depends on past history

d.

none of the mentioned

Discuss
Answer: (b).input-output pair determine probability of postive reinforcement
Q54.
What is temporal credit assignment?

a.

reinforcement signal given to input-output pair don’t change with time

b.

input-output pair determine probability of postive reinforcement

c.

input pattern depends on past history

d.

none of the mentioned

Discuss
Answer: (c).input pattern depends on past history
Q55.
Boltzman learning uses what kind of learning?

a.

deterministic

b.

stochastic

c.

either deterministic or stochastic

d.

none of the mentioned

Discuss
Answer: (b).stochastic
Q56.
Whats true for sparse encoding learning?

a.

logical And & Or operations are used for input output relations

b.

weight corresponds to minimum and maximum of units are connected

c.

weights are expressed as linear combination of orthogonal basis vectors

d.

change in weight uses a weighted sum of changes in past input values

Discuss
Answer: (a).logical And & Or operations are used for input output relations
Q57.
Whats true for Drive reinforcement learning?

a.

logical And & Or operations are used for input output relations

b.

weight corresponds to minimum and maximum of units are connected

c.

weights are expressed as linear combination of orthogonal basis vectors

d.

change in weight uses a weighted sum of changes in past input values

Discuss
Answer: (d).change in weight uses a weighted sum of changes in past input values
Q58.
Whats true for Min-max learning?

a.

logical And & Or operations are used for input output relations

b.

weight corresponds to minimum and maximum of units are connected

c.

weights are expressed as linear combination of orthogonal basis vectors

d.

change in weight uses a weighted sum of changes in past input values

Discuss
Answer: (b).weight corresponds to minimum and maximum of units are connected
Q59.
Whats true for principal component learning?

a.

logical And & Or operations are used for input output relations

b.

weight corresponds to minimum and maximum of units are connected

c.

weights are expressed as linear combination of orthogonal basis vectors

d.

change in weight uses a weighted sum of changes in past input values

Discuss
Answer: (c).weights are expressed as linear combination of orthogonal basis vectors
Q60.
Stability refers to adjustment in behaviour of weights during learning?

a.

YES

b.

NO

c.

May be YES or NO

d.

Can't Say

Discuss
Answer: (b).NO
Page 6 of 8

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