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Man and Machine > Task 4 not as important > Flashcards

Task 4 not as important Flashcards

1
Q

describe the equation principle ?

A
  • Start: a1 -> w1 -> transfer function -> activation function -> output function
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2
Q

What is meant by the small a in the equation principle ?

A

Small a = Input (activity)

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3
Q

What is meant by the lare w in the equation principle ?

A
  • large W = weight
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4
Q

What is meant by the transfer function in the equation principle ?

A
  • All the inputs multiplied by the weights and then add each other up
  • a1xw1+a1xw2…
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5
Q

What is meant by the activation function in the equation principle ?

A
  • Different functions representing the activation function

- linear, threshold binary and sigmoid

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6
Q

What additional unit is presented in the equation principle ?

A
  • The bias
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7
Q

What is the formula of the hebbian learning rule ?

A

wij + Deltawij = new wij

  1. wij = current weight (given)
  2. Delta wij = the new wight change
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8
Q

How do we calculate the delta wij ?

A
  • activity of neuron I multiple by activity of neuron j multiple by a learning constant
  • everything is given
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9
Q

Out of how many cells does the Hippocmapus exist ?

A
    • 2:
  • > Cornu Ammonis (CA)
  • > Dentate gyrus
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10
Q

What is the main function of the Hippocampus ?

A
  • It is mainly active during episodic memory
  • memory which u can activly recall
  • mainly involved i formation of memory
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11
Q

How is episodic memory formed ?

A
  • The HC builds a formation
  • The formation is needed to create episodic memory
  • it needs a rapid combination of information from dif sources -> This means formation
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12
Q

How is procedural memory formed ?

A
  • gradual development over many related experiences

- skill learning

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13
Q

How does the information flow outside of the HC ?

A
  • Input zone for HC: is the Parahippocampal gyrus and entorhinal cortex
  • output zone: back projection to cortical areas where the input came from
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14
Q

What is meant by the enthorinal cortex ?

A
  • Cortex/network for memory, navigation-> input for HC
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15
Q

What is in general the internal path of the HC

A
    • Information processing in 3 stages: starts at the perfront path -> dentate gyrus -> CA3 -> CA1
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16
Q

Explain the general internal path of the HC a bit more in detail:

A
    1. Perfronth path synapse with cells in dentate gyrus
      1. output from dentate gyrus via mossy fibers toward cell in CA 3
      2. some cells might be directly connected from perforant path to CA3 cells
      3. Recurrent connections: cells in CA 3 send infomation back and forward
      4. Schaeffer collateral cells -> send the outpuf from CA3 to the CA 1
      5. CA 1 send its input to the neocortex/subiculum structure
17
Q

What is so special between the connection btw the dentate gyrus and CA 3 cells ?

A
  • Different input patterns activate different CA3 cells
  • Only some cells are active at a certain time (called sparse)
  • The sparse is need to activate the auto associator which makes it possible to store more memory
18
Q

How does the hippocampus learn ?

A
  • Also via the hebbian learning rule

- it works really quickly less then a second which is needed for episodic memory

19
Q

Where does compettive learning occur ?

A
  • In DG and CA1
  • the output from the DG system will be less correlated and more categorised than the inputs to it from the perforant path
20
Q

How does the hippocampus keeps out irrelevant information ?

A
  • Via threshold which is connected to Information about rewards and punishments by the amygdala
  • is in the enthorinal cortex 5%
21
Q

Give a short summary of what is going on in each stage of the hippocmapus:

A
    1. competitive learning in DG,
      1. autoassociation in CA3,
      2. competitive learning in C A l
      3. pattern association between CAl and entorhinal cortex.
22
Q

What happens if we bases a neuronal network on the HC ?

A
  • it can stores a large number of unrelated patterns with a single presentation
  • and it can retrieve the stored patterns from fragments of them
  • so it works just like the HC
23
Q

What is the sum of external and internal input in a autoassociator cell ?

A
  • netinput
24
Q

Learn something about the sparse input from the denatte gyrus:

A
  • sparse input is allways 5% for CA1 and DG
  • Only in CA 1 there it is 1%
  • sparse input is the reason why we can store so many information (SPARSNESS)
25
Q

How fast does the hippocampus work ?

A
  • Hebbian synaptic modification works within a second super fast
26
Q

What is important about the CA 3 ?

A
  • Hebbian learning occurs based on recurrent connections (15 times) -> Autoassociator

Man and Machine (12 decks)

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