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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 4 of 8

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Q31.
What is supervised learning?

a.

weight adjustment based on deviation of desired output from actual output

b.

weight adjustment based on desired output only

c.

weight adjustment based on actual output only

d.

none of the mentioned

Discuss
Answer: (a).weight adjustment based on deviation of desired output from actual output
Q32.
Supervised learning may be used for?

a.

temporal learning

b.

structural learning

c.

both temporal and structural learning

d.

none of the mentioned

Discuss
Answer: (c).both temporal and structural learning
Q33.
What is structural learning?

a.

concerned with capturing input-output relationship in patterns

b.

concerned with capturing weight relationships

c.

both weight and input-output relationships

d.

none of the mentioned

Discuss
Answer: (a).concerned with capturing input-output relationship in patterns
Q34.
What is temporal learning?

a.

concerned with capturing input-output relationship in patterns

b.

concerned with capturing weight relationships

c.

both weight and input-output relationships

d.

none of the mentioned

Discuss
Answer: (b).concerned with capturing weight relationships
Q35.
Learning methods can only be online?

a.

YES

b.

NO

c.

May be YES or NO

d.

Can't Say

Discuss
Answer: (b).NO
Q36.
Online learning allows network to incrementally adjust weights continuously?

a.

YES

b.

NO

c.

May be YES or NO

d.

Can't Say

Discuss
Answer: (a).YES
Q37.
What is nature of input in activation dynamics?

a.

static

b.

dynamic

c.

both static and dynamic

d.

none of the mentioned

Discuss
Answer: (a).static
Q38.
Adjustments in activation is slower than that of synaptic weights?

a.

YES

b.

NO

c.

May be YES or NO

d.

Can't Say

Discuss
Answer: (b).NO
Q39.
What does the term wij(0) represents in synaptic dynamic model?

a.

a prioi knowledge

b.

just a constant

c.

no strong significance

d.

future adjustments

Discuss
Answer: (a).a prioi knowledge
Q40.
What is hebbian learning?

a.

synaptic strength is proportional to correlation between firing of post and presynaptic neuron

b.

synaptic strength is proportional to correlation between firing of postsynaptic neuron only

c.

synaptic strength is proportional to correlation between firing of presynaptic neuron only

d.

none of the mentioned

Discuss
Answer: (a).synaptic strength is proportional to correlation between firing of post and presynaptic neuron
Page 4 of 8

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