chapter 1: neurons as the building blocks of behavior measuring behavior in a natural setting in a laboratory setting chapter 1: neurons as the building blocks of behavior measuring behavior in a natural setting in a laboratory setting #02: BEHAVIORAL ANALYSIS
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chapter 1: neurons as the building blocks of behavior
measuring behavior
in a natural setting
in a laboratory setting
chapter 1: neurons as the building blocks of behavior
measuring behavior
in a natural setting
in a laboratory setting
#02: BEHAVIORAL ANALYSIS
Pavlov & Thorndike
LABORATORY SETTING
learn temporal relationships value of CS changes, predicts occurrence of US
p.10 fig.1.4
food = unconditioned stimulus (US)... salivation = unconditioned response (UR) to US bell = conditioned stimulus (CS)... CS (naïve) 0 response CS + US pairing = training CS (trained) salivation = conditioned response (CR)
Pavlov: classical or Pavlovian conditioning, dogs stimulus “value” changes when paired with another
LABORATORY SETTING
cat associates own escape behavior with box features food in view outside box (motivation) levels of difficulty (e.g., pull string to excape) record time for escape
p.10 fig.1.4
Thorndike: instrumental or operant conditioning hungry cats, puzzle boxes
LABORATORY SETTING
in both, animals learn... existence of stimuli temporal relationships among stimuli
in operant only, animals learn... relationships between stimuli & their own behavior
in classical, animals receive... measured stimulus, controlled by experimenter
in operant, animals receive... stimulus determined by time to elicit behavior
learning is usually a combination of classical & operant
LABORATORY SETTING
test: previously unseen pairs able to transfer the “rule” to new situations did not simply learn pattern of cards learned that relationship between stimuli is critial
p.12 fig.1.5
train: food reward for turning right if top lighter left if top darker
what do animals associate in associative learning ? rats, radial arm maze (B)
left & right choices paired light & dark stimuli (A)
LABORATORY SETTING
other tests using the radial arm maze
p.12 fig.1.5
trained to retrieve food from each arm, no revisits remember which arms visited within each trial no need to remember info from trial to trial uses working memory
trained with food in some arms memory from trial to trial uses reference memory
LABORATORY SETTING
development physiology behavior
STRUCTURE ... ... FUNCTION
Neurobiology
LABORATORY SETTING
E1 E2
G1
G2
components of phenotypes
MEASURING BEHAVIOR – VARIATION
components of phenotypes (e.g., behavior)
P = G + E + G*E genotype (heredity) environment (experience) interaction
... for our purposes this could be ...
behavior = instinct + learning + ... ?
MEASURING BEHAVIOR – VARIATION
G1
G2
PHENOTYPE
G
ENVIRONMENT
E1
E2
E1
E2
G+E
E
E1
E2
E1
E2
G*E
MEASURING BEHAVIOR – VARIATION
components of phenotypes (e.g., behavior)
P = G + E + G*E genotype (heredity) environment (experience) interaction
where does E come from ?
MEASURING BEHAVIOR – VARIATION
GENESMESSAGESPEPTIDESPROTEINS
PROTEIN COMPLEXESORGANELLES
NEURONSASSEMBLIESSTRUCTURES
CIRCUITSNERVOUS SYSTEM
WHOLE ANIMALBEHAVIOR
EXPERIENCE
ENVIRONMENT
PLASTICITY
verticalintegration
INFORMATION FLOW
EN
VIR
ON
ME
NT
components of phenotypes (e.g., behavior)
P = G + E + G*E genotype (heredity) environment (experience) interaction
where does E come from ? what aspects of E would you try to control
in your behavior experiment ? what would you need to include ?
MEASURING BEHAVIOR – VARIATION
components of phenotypes (e.g., behavior)
P = G + E + G*E genotype (heredity) environment (experience) interaction
where does E come from ?
where does G come from ?
MEASURING BEHAVIOR – VARIATION
GENESMESSAGESPEPTIDESPROTEINS
PROTEIN COMPLEXESORGANELLES
NEURONSASSEMBLIESSTRUCTURES
CIRCUITSNERVOUS SYSTEM
WHOLE ANIMALBEHAVIOR
EXPERIENCE
ENVIRONMENT
PLASTICITY
verticalintegration
INFORMATION FLOW
GENESMESSAGESPEPTIDESPROTEINS
PROTEIN COMPLEXESORGANELLES
NEURONSASSEMBLIESSTRUCTURES
CIRCUITSNERVOUS SYSTEM
WHOLE ANIMALBEHAVIOR
EXPERIENCE
ENVIRONMENT
PLASTICITY
verticalintegration
INFORMATION FLOW
how to identify natural sources: gene # / influence from F2 phenotype ratios
SOURCES OF GENETIC VARIATION
0
1
FREQUENCY
PHENOTYPE
1 gene1 allele( = 0)
GENETIC PHENOTYPIC VARIATION
0.0
0.1
0.2
0.3
0.4
0.5
FREQUENCY
PHENOTYPE
1 gene2 allelesno dominance
GENETIC PHENOTYPIC VARIATION
0.0
0.1
0.2
0.3
0.4
FREQUENCY
PHENOTYPE
GENETIC PHENOTYPIC VARIATION
2 additive genes2 alleles eachno dominance
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
FREQUENCY
PHENOTYPE
164
3 genes
14n
GENETIC PHENOTYPIC VARIATION
3 additive genes2 alleles eachno dominance
how to identify natural sources: gene # / influence from F2 phenotype ratios
artificial selection
SOURCES OF GENETIC VARIATION
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
FREQUENCY
PHENOTYPE
GENETIC PHENOTYPIC VARIATION
n additive genes2 alleles eachno dominance
xx0.05
0.10
0.15
0.20
0.25
0.30
0.35
FREQUENCY
0.00
PHENOTYPE
MEASURING BEHAVIOR – ARTIFICIAL SELECTION
ARTIFICIAL SELECTION – LEARNING IN FLIES
relaxselection
10 15
fixed
not
ARTIFICIAL SELECTION – LEARNING IN FLIES
speed things up with induced sources: chemical mutagens – “point” mutations ionizing radiation – chromosome rearrangements transposon insertions – disrupt gene activity transgene expression – block / add / change gene function
– qualitative / quantitative – spatial / temporal control
how to identify natural sources: gene # / influence from F2 phenotype ratios
artificial selection
SOURCES OF GENETIC VARIATION
induced sources of genetic variation:+ : rapid gain toward understanding mechanism− : may find a subset of the genes evolution “designed” to
control behavior
natural sources of genetic variation: + : the genes evolution “designed” to control of behavior− : lots of effort, little gain toward understanding mechanism