Brain plasticity

Question 1

neural plasticity

Answer 1

the brain´s ability to change as a result of experience. → experience-dependent changes in neural functioning.
Brain capability: being dynamic
Through growth → having more cells to enhance neural circuit/ new circuit
Through reorganization → existing neurons are rewired in another way → allow us to acquire more and more skills

Question 2

How can the synapse strength be changed?

Answer 2

• Strengthen/ intensify the synapse: Enlarge the synapse by store more neurotransmitters →receiving channels more sensitive → intensified connection = long-term potentiation (LTP)
• Weaken the synapse: shrink the sypnapse by using less neurotransmitter vesicles or using higher threshold to activate the postsynaptic channels → called long-term depression (LTD)

Question 3

What is LTP? long-term potentiation

Answer 3

Enlarge the synapse by store more neurotransmitters →receiving channels more sensitive → intensified connection = long-term potentiation (LTP)

Question 4

What is long-term depression (LTD)?

Answer 4

Weaken the synapse: shrink the sypnapse by using less neurotransmitter vesicles or using higher threshold to activate the postsynaptic channels → called long-term depression (LTD)

Question 5

In Multilayer perceptrons, how to adjust the model architecture to store new information or relationships?

Answer 5

Introduce new connections
Strengthen the existing one
Incorporation of novel units (add new born neuron - happening during growth)

Question 6

The different methods involving how the neural network is changed

Answer 6

• changing topology e.g. NEAT = Neuro Evolution of Augmenting Topologies, involves evolving neural network topologies along with weights, outperforms the best fixed-topology method on a challenging benchmark reinforcement learning task.
• changing weight/ synaptic strength

Question 7

Hebb’s Rule

Answer 7

‘neurons that fire together wire together’
That is, the simultaneous activation of nearby neurons leads to an increase in the strength of synaptic connection between them.
Hebb’s rule is a postulate proposed by Donald Hebb in 1949 [1]. It is a learning rule that describes how the neuronal activities influence the connection between neurons, i.e., the synaptic plasticity. It provides an algorithm to update weight of neuronal connection within neural network.

Question 8

What is the difference between structural plasticity and synaptic plasticity?

Answer 8

• structural plasticity: ‘neurons that fire together wire together’
• Synaptic plasticity: environment becomes intensified (when 2 neurons that fire together) → synaptic strength increases

Question 9

What is Spike timing-dependent plasticity (STDP)?

Answer 9

a biological process that adjusts the strength of connections between neurons in the brain. The process adjusts the connection strengths based on the relative timing of a particular neuron’s output and input action potentials (or spikes).

• Presynaptic neuron spike before the postsynaptic neuron within time frame < 20s –> postsynaptic cell pathway w increase –> LTP
• Presynaptic neuron spike after the postsynaptic neuron within timeframe < 20s –> postsynaptic cell pathway w decreases –> LTD

Question 10

What is the difference between the Hebb rule, BCM rule, and Oja rule?

Answer 10

• Hebb rule –> Hebbian learning suffers from instability due to a constant threshold of postsynaptic activity that determines if a synapse is strengthed or weakened
• BCM has the weight in Hebbian learning stabilized through the postsynaptic activity with an adapt threshold
• Oja rule is a modification of the standard Hebb’s Rule that, through multiplicative normalization, solves all stability problems and generates an algorithm for principal components analysis.