1. What is generalization?
a) the ability of a pattern recognition system to approximate the desired output values for pattern vectors which are not in the test set.
b) the ability of a pattern recognition system to approximate the desired output values for pattern vectors which are not in the training set.
c) can be either way
d) none of the mentioned
Explanation: Follows from basic definition of generalization
2. What are models in neural networks?
a) mathematical representation of our understanding
b) representation of biological neural networks
c) both way
d) none of the mentioned
Explanation: Model should be close to our biological neural systems, so that we can have high efficiency in machines too.
3. What kind of dynamics leads to learning laws?
a) synaptic
b) neural
c) activation
d) both synaptic & neural
Explanation: Since weights are dependent on synaptic dynamics, hence learning laws
4. Changing inputs affects what kind of dynamics directly?
a) synaptic
b) neural
c) activation
d) both synaptic & neural
Explanation: Activation dynamics depends on input pattern, hence any change in input pattern will affect activation dynamics of neural networks.
5. Activation value is associated with?
a) potential at synapses
b) cell membrane potential
c) all of the mentioned
d) none of the mentioned
Explanation:Cell membrane potential determines the activation value in neural nets
6. In activation dynamics is output function bounded?
a) yes
b) no
Explanation: It is the nature of output function in activation dynamics.
7. What’s the actual reason behind the boundedness of the output function in activation dynamics?
a) limited neural fluid
b) limited fan in capacity of inputs
c) both limited neural fluid & fan in capacity
d) none of the mentioned
Explanation: It is due to the limited current carrying capacity of cell membrane
8. What is noise saturation dilemma?
a) at saturation state neuron will stop working, while biologically it’s not feasible
b) how can a neuron with limited operating range be made sensitive to nearly unlimited range of inputs
c) can be either way
d) none of the mentioned
Explanation: Threshold value setting has to be adjusted properly
9. Broadly how many kinds of stability can be defined in neural networks?
a) 1
b) 3
c) 2
d) 4
Explanation: There exist broadly structural & global stability in neural.
10.What is structural stability?
a) when both synaptic & activation dynamics are simultaneously used & are in equilibrium
b) when only synaptic dynamics in equilibrium
c) when only synaptic dynamics in equilibrium
d) none of the mentioned
Explanation: Refers to state equilibrium situation where small perturbations brings network back to equilibrium