1. Learning methods can only be online?
a) yes
b) no
Explanation: Learning can be offline too
2. Online learning allows network to incrementally adjust weights continuously?
a) yes
b) no
Explanation: Follows from basic definition of online learning
3. What is nature of input in activation dynamics?
a) static
b) dynamic
c) both static & dynamic
d) none of the mentioned
Explanation: Input is fixed throughout the dynamics.
4. Adjustments in activation is slower than that of synaptic weights?
a) yes
b) no
Explanation: Adjustments in activation is faster than that of synaptic weights
5. what does the term wij(0) represents in synaptic dynamic model?
a) a prior knowledge
b) just a constant
c) no strong significance
d) future adjustments
Explanation: Refer to weight equation of synaptic dynamic model
6. 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.
7. 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
8. 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.
9. 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.
10. 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.