  Complex environments provide structured yet variable sensory inputs. To best
exploit information from these environments, organisms must evolve the ability
to anticipate consequences of unknown stimuli, and act on these predictions. We
propose an evolutionary path for neural networks, leading an organism from
reactive behavior to simple proactive behavior and from simple proactive
behavior to induction-based behavior. Through in-vitro and in-silico
experiments, we define the conditions necessary in a network with spike-timing
dependent plasticity for the organism to go from reactive to proactive
behavior. Our results support the existence of specific evolutionary steps and
four conditions necessary for embodied neural networks to evolve predictive and
inductive abilities from an initial reactive strategy. We extend these
conditions to more general structures.
