gs gs127 Activation and Synaptic Dynamics - Learning Laws - Quiz No.1
gs gs127 SQLite Quiz
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Question 1: What is hebbian learning?
synaptic strength is proportional to correlation between firing of post & presynaptic neuron
synaptic strength is proportional to correlation between firing of postsynaptic neuron only
synaptic strength is proportional to correlation between firing of presynaptic neuron only
none of the mentioned
Question 2: What is differential hebbian learning?
synaptic strength is proportional to correlation between firing of post & presynaptic neuron
synaptic strength is proportional to correlation between firing of postsynaptic neuron only
synaptic strength is proportional to correlation between firing of presynaptic neuron only
synaptic strength is proportional to changes in correlation between firing of post & presynaptic neuron
Question 3: What is competitive learning?
learning laws which modulate difference between synaptic weight & output signal
learning laws which modulate difference between synaptic weight & activation value
learning laws which modulate difference between actual output & desired output
none of the mentioned
Question 4: What is differential competitive learning?
synaptic strength is proportional to changes of post & presynaptic neuron
synaptic strength is proportional to changes of postsynaptic neuron only
synaptic strength is proportional to changes of presynaptic neuron only
none of the mentioned
Question 5: What is error correction learning?
learning laws which modulate difference between synaptic weight & output signal
learning laws which modulate difference between synaptic weight & activation value
learning laws which modulate difference between actual output & desired output
none of the mentioned
Question 8: Error correction learning is type of?
supervised learning
unsupervised learning
can be supervised or unsupervised
none of the mentioned
Question 10: What is reinforcement learning?
learning is based on evaluative signal
learning is based o desired output for an input
learning is based on both desired output & evaluative signal
none of the mentioned
Question 13: What is fixed credit assignment?
reinforcement signal given to input-output pair don’t change with time
input-output pair determine probability of postive reinforcement
input pattern depends on past history
none of the mentioned
Question 14: What is probablistic credit assignment?
reinforcement signal given to input-output pair don’t change with time
input-output pair determine probability of postive reinforcement
input pattern depends on past history
none of the mentioned
Question 15: What is temporal credit assignment?
reinforcement signal given to input-output pair don’t change with time
input-output pair determine probability of postive reinforcement
input pattern depends on past history
none of the mentioned
Question 16: Boltzman learning uses what kind of learning?
deterministic
stochastic
either deterministic or stochastic
none of the mentioned
Question 17: Whats true for sparse encoding learning?
logical And & Or operations are used for input output relations
weight corresponds to minimum & maximum of units are connected
weights are expressed as linear combination of orthogonal basis vectors
change in weight uses a weighted sum of changes in past input values
Question 18: Whats true for Drive reinforcement learning?
logical And & Or operations are used for input output relations
weight corresponds to minimum & maximum of units are connected
weights are expressed as linear combination of orthogonal basis vectors
change in weight uses a weighted sum of changes in past input values
Question 19: Whats true for Min-max learning?
logical And & Or operations are used for input output relations
weight corresponds to minimum & maximum of units are connected
weights are expressed as linear combination of orthogonal basis vectors
change in weight uses a weighted sum of changes in past input values
Question 20: Whats true for principal component learning?
logical And & Or operations are used for input output relations
weight corresponds to minimum & maximum of units are connected
weights are expressed as linear combination of orthogonal basis vectors
change in weight uses a weighted sum of changes in past input values
