The current talk addresses the development of cognitive abilities in artificial organisms, a topic that has attracted many research efforts recently. We introduce a novel computational framework for modeling partial brain areas, following a coevolutionary agent-based approach. Specifically, self-organized agent structures are employed to represent distinct brain areas. In order to facilitate the design of agents, we introduce a Hierarchical Collaborative CoEvolutionary (HCCE) approach that specifies the structural details of autonomous, but cooperating system components. By utilizing a distributed model and a distributed design methodology, we are able to explicitly address the special characteristics of substructures representing brain areas, and additionally integrate them effectively formulating composite systems.

Overall, the proposed computational framework facilitates the design of brain-inspired cognitive systems because it:

• assigns distinct roles to the agents representing brain-areas,
• supports computational modeling of biological lesion findings, addressing explicitly the pre- and post- lesion performance of the model,
• alleviates the integration of partial models developing gradually more complex ones,
• re-designs (if necessary) existing substructures in order to support forthcoming modeling efforts.

The effectiveness of the proposed computational framework is demonstrated on a number of different experiments. The implemented models are successfully embedded in a simulated robotic platform, developing artificial organisms with advanced behavioral and cognitive abilities.