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.

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

Explore more Topics under Neural Networks

01

Basics of Artificial Neural Networks

02

Learning Concepts of Neural Networks

03

Feedforward Neural Networks

04

Feedback Neural Networks

05

Competitive Learning Neural Networks

06

Applications of Artificial Neural Networks

Q21.

What was the goal of shunting activation model?

to make system dynamic

to keep operating range of activation value to a specified range

to make system static

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?

dynamic

static

deterministic

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?

xb(t)=deterministic model

xb(t)=deterministic model + noise component

xb(t)=deterministic model*noise component

none of the mentioned’

Discuss

Answer: (b).xb(t)=deterministic model + noise component

Q24.

What is equilibrium in neural systems?

deviation in present state, when small perturbations occur

settlement of network, when small perturbations occur

change in state, when small perturbations occur

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)?

xb(t)=0

xb(t)=1

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

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?

update to all units is done at the same time

change in state of any one unit drive the whole network

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

none of the mentioned

Discuss

Answer: (b).change in state of any one unit drive the whole network

Q27.

Learning is a?

slow process

fast process

can be slow or fast in general

can’t say

Discuss

Answer: (a).slow process

Q28.

What are the requirements of learning laws?

convergence of weights

learning time should be as small as possible

learning should use only local weights

all of the mentioned

Discuss

Answer: (d).all of the mentioned

Q29.

Memory decay affects what kind of memory?

short tem memory in general

older memory in general

can be short term or older

none of the mentioned

Discuss

Answer: (a).short tem memory in general

Q30.

How is pattern information distributed?

it is distributed all across the weights

it is distributed in localised weights

it is distributed in certain proctive weights only

none of the mentioned

Discuss

Answer: (a).it is distributed all across the weights

Page 3 of 8

Welcome to the Activation and Synaptic Dynamics MCQs Page

Activation and Synaptic Dynamics MCQs | Page 3 of 8

Explore more Topics under Neural Networks

Basics of Artificial Neural Networks

Learning Concepts of Neural Networks

Feedforward Neural Networks

Feedback Neural Networks

Competitive Learning Neural Networks

Applications of Artificial Neural Networks

Suggested Topics

Digital Communication

Computer Architecture

Python

JavaScript

Wireless and Mobile Communications

HTML

Cyber Security

Object Oriented Programming

Visual Basic