1. Evolution and Learning
Braitenberg 6: Selection, the Impersonal Engineer
Braitenberg 7: Concepts
EXAMPLES:
Modeling evolution: genetic algorithms
Modeling learning: reinforcement learning
(bee foraging)

2. Genetic Algorithms genetic coding (chromosome/genes)
genotype  phenotype (development)
evaluation, of individuals in a population
selection, based on fitness
variation, genetic modification of selected
repeat

4. sensorL
sensorR
Parameters to encode:
Input gains: 2
Time constants: 2
Thresholds: 4
Injection current: 4
Injection noise: 4
Synaptic weights: 4
Syn. time constants: 4
Output gains: 2
TOTAL: 26
Assume 4 bit resolution per parameter (16 possible values): 26 * 4 = 104 bits
2104 possibilities !!!
motorL
motorR

5. Learning vs. evolution

7. Functional organization (bee brain)

8. Reinforcement Learning
Montague PR, Dayan P, Person C, Sejnowski TJ (1995) Bee foraging in uncertain environments using predictive Hebbian learning. Nature 377, S R P N B
r(t)
Sensory
Input
Nectar
Action
δ(t) WB WN WY Y
Diagram from: