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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 3 of 8

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Q21.
What was the goal of shunting activation model?

a.

to make system dynamic

b.

to keep operating range of activation value to a specified range

c.

to make system static

d.

can be either for dynamic or static, depending on inputs

Discuss
Answer: (b).to keep operating range of activation value to a specified range
Q22.
Activation models are?

a.

dynamic

b.

static

c.

deterministic

d.

none of the mentioned

Discuss
Answer: (c).deterministic
Q23.
If xb(t) represents differentiation of state x(t), then a stochastic model can be represented by?

a.

xb(t)=deterministic model

b.

xb(t)=deterministic model + noise component

c.

xb(t)=deterministic model*noise component

d.

none of the mentioned’

Discuss
Answer: (b).xb(t)=deterministic model + noise component
Q24.
What is equilibrium in neural systems?

a.

deviation in present state, when small perturbations occur

b.

settlement of network, when small perturbations occur

c.

change in state, when small perturbations occur

d.

none of the mentioned

Discuss
Answer: (b).settlement of network, when small perturbations occur
Q25.
What is the condition in Stochastic models, if xb(t) represents differentiation of state x(t)?

a.

xb(t)=0

b.

xb(t)=1

c.

xb(t)=n(t), where n is noise component

d.

xb(t)=n(t)+1

Discuss
Answer: (c).xb(t)=n(t), where n is noise component
Q26.
What is asynchronous update in a network?

a.

update to all units is done at the same time

b.

change in state of any one unit drive the whole network

c.

change in state of any number of units drive the whole network

d.

none of the mentioned

Discuss
Answer: (b).change in state of any one unit drive the whole network
Q27.
Learning is a?

a.

slow process

b.

fast process

c.

can be slow or fast in general

d.

can’t say

Discuss
Answer: (a).slow process
Q28.
What are the requirements of learning laws?

a.

convergence of weights

b.

learning time should be as small as possible

c.

learning should use only local weights

d.

all of the mentioned

Discuss
Answer: (d).all of the mentioned
Q29.
Memory decay affects what kind of memory?

a.

short tem memory in general

b.

older memory in general

c.

can be short term or older

d.

none of the mentioned

Discuss
Answer: (a).short tem memory in general
Q30.
How is pattern information distributed?

a.

it is distributed all across the weights

b.

it is distributed in localised weights

c.

it is distributed in certain proctive weights only

d.

none of the mentioned

Discuss
Answer: (a).it is distributed all across the weights
Page 3 of 8

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