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LECTURE 8: EVOLUTION & DEVELOPMENT
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- 1Which species has the biggest Brain to Body size ratio?Homo Sapiens Sapiens
- 2Why is our brain te biggest?Because evolution made Hominids evolve their brain faster
- 3What is meant by arborization of dendrites?The tree like structures that denrites have
- 4Does more neurons mean more intelligent?NO! an elephant has same amount as us, its the limbic system and the complexity of its structure that defines our intelligence
- 5Radial Unit HypothesisAbout the division an migration of neurons
- 6What is Synaptogenesis?The formation of synapses between neurons
- 7Synapse eliminationUSE IT OR LOSE IT
- 8When does myelination occur?The sensory & motor neurons first, then frontal & parietal
- 9What is brain plasticity?The ability of the brain to change throughout life
- 10When is plasticity useful?When learning or after an accident(if you lose vision then the visual part of brain can reorganise to serve haring or smelling, eg. blind people hear better)
- 11USE IT OR LOSE ITthis is a free point
- 12Lets break it down for geneticsk
- 13Chromosomes make me anIndividual
- 14A chromosome consists of :Genes
- 15A gene consists of a code of Nucleide Acids which are these:Adenine + Thymine
Cytosine + Guanine - 16But why dont we look exactly like our parents?Recombination of genes + Mutation
- 17Better genes will live on, how is this called?Survival of the fittest
- 18GenotypeHow the genes are combined
- 19PhenotypeHow the genes are exprssed(how you look)
- 20Pls learn GAYes sir
- 21Roulette wheel selectionWheel of reproduction is turned, fittest individual has highest chance, least fir lowest. This is all based on chance
- 22ElitistTake top individuals and make em fuck, lots of information loss, ordered by fitness.
- 23Tournament SelectionTake a tournament(k) individuals and choose the fittest from those, make em fuck. Although if k is to big its basically random
- 24Advantages of GA's• Widely applicable
• Easy to implement
• Easy to parallelize - 25Cons to GA's• Same local minima problem as with back-prop, but GA are better
suited to escape local minima
• Lots of variation (eg. Selection, parameters)
• Sometimes difficult to find right encoding, fitness
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- Fichas sobre LECTURE 8: EVOLUTION & DEVELOPMENT, creado por Lucas Vanparijs el 11/12/2014.
- Creado por
- Lucas Vanparijs
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