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Complex environments provide structured yet variable sensory inputs. To bestexploit information from these environments, organisms must evolve the abilityto anticipate consequences of unknown stimuli, and act on these predictions. Wepropose an evolutionary path for neural networks, leading an organism fromreactive behavior to simple proactive behavior and from simple proactivebehavior to induction-based behavior. Through in-vitro and in-silicoexperiments, we define the conditions necessary in a network with spike-timingdependent plasticity for the organism to go from reactive to proactivebehavior. Our results support the existence of specific evolutionary steps andfour conditions necessary for embodied neural networks to evolve predictive andinductive abilities from an initial reactive strategy. We extend theseconditions to more general structures.

Reactive, Proactive, and Inductive Agents: An evolutionary path for biological and artificial spiking networks