# Neural Network Questions and Answers – Learning Laws – 1

This set of Neural Networks Multiple Choice Questions & Answers (MCQs) focuses on “Learning Laws-1″.

1. What is hebbian learning?
a) synaptic strength is proportional to correlation between firing of post & 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

Explanation: Folllows from basic definition of hebbian learning.

2. What is differential hebbian learning?
a) synaptic strength is proportional to correlation between firing of post & 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) synaptic strength is proportional to changes in correlation between firing of post & presynaptic neuron

Explanation: Differential hebbian learning is proportional to changes in correlation between firing of post & presynaptic neuron.

3. What is competitive learning?
a) learning laws which modulate difference between synaptic weight & output signal
b) learning laws which modulate difference between synaptic weight & activation value
c) learning laws which modulate difference between actual output & desired output
d) none of the mentioned

Explanation: Competitive learning laws modulate difference between synaptic weight & output signal.

4. What is differential competitive learning?
a) synaptic strength is proportional to changes of post & presynaptic neuron
b) synaptic strength is proportional to changes of postsynaptic neuron only
c) synaptic strength is proportional to changes of presynaptic neuron only
d) none of the mentioned

Explanation: Differential competitive learning is based on to changes of postsynaptic neuron only.

5. What is error correction learning?
a) learning laws which modulate difference between synaptic weight & output signal
b) learning laws which modulate difference between synaptic weight & activation value
c) learning laws which modulate difference between actual output & desired output
d) none of the mentioned

Explanation: Error correction learning is base on difference between actual output & desired output.
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6. Continuous perceptron learning is also known as delta learning?
a) yes
b) no

Explanation: Follows from basic definition of delta learning.

7. Widrows LMS algorithm is also based on error correction learning?
a) yes
b) no

Explanation: It uses the instantaneous squared error between desired & actual output of unit.

8. Error correction learning is type of?
a) supervised learning
b) unsupervised learning
c) can be supervised or unsupervised
d) none of the mentioned

Explanation: Since desired output for an input is known.

9. Error correction learning is like learning with teacher?
a) yes
b) no

Explanation: Since desired output for an input is known.

10. What is reinforcement learning?
a) learning is based on evaluative signal
b) learning is based o desired output for an input
c) learning is based on both desired output & evaluative signal
d) none of the mentioned

Explanation: Reinforcement learning is based on evaluative signal.

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