Artificial Neural Networks have been crowned with tremendous successes in recent years, with ever wider and more complex ranges of applications. However, they, too often, result from a costly human design process relying as much on expertise as on trial and error. While the field of NeuroEvolution provides a complementary view point through emergent, self-designing ANNs, the “black-box” properties of the resulting networks is further magnified. Still, by once more taking inspiration from biology, we may extract meaningful information from ANNs by using similar approaches as those used for biological brains. In this work, we study the emergence and functional allocation of neurons in a light communication task. By having a robot transmit visual information, through vocal channels, we enrich the existing literature with new types of stimuli, namely those related to role (emitter/ receiver). Through Virtual functional Magnetic Resonance Imaging (VfMRI), we observe that evolution only favored specific kind of input-processing modules. Combined with a strong presence of jack-of-alltrades modules, this demonstrates the balancing act between specialization and generalization in Artificial Neural Networks with emergent topologies.

Understanding the neurological implementation of emotions is a major research subject from biology to computer sciences that, in the latter case, takes many shapes: from accurate detection of human emotions to the emulation of plausible responses to stimuli. There is, however, room for a more bottom-up approach in which we would thrive to recreate emotions from undifferentiated elementary building blocks. In this article, we used virtual creatures that interact with their environment through a low-level perception/cognition/action loop to demonstrate their potential for fear responses. Embedded in a physical environment in a typical prey/predator setting, they develop strategies for foraging while minimizing their exposure to danger. By monitoring the neural activities of these subjects, we were able to highlight the regularities induced by an ES-HyperNEAT encoding and their eventual mapping into “mental states”. We further emphasize the potential of this approach by clustering these ANNs and showing their resulting complexity in terms of conspecific identification, communication, and functional modularity. Indeed, through functional equivalence across numerous topologies, we identify a fear-related neural cluster that serves as a primitive defensive survival circuit.

Progress in molecular genetics allowed taxonomists to better understand the relationships between species without the bias of morphological similarities. However, access to data from times past is limited to the fossil archives which, being far from complete, can only provide limited information. To address this problem through the field of Artificial Life, we devised a polyvalent sexual reproduction scheme and an automated phylogenetic tool capable of producing, from a stream of genomes, hierarchical species trees with relatively low memory footprint. We assert that these apparatus perform well under reasonable stress by embedding them into 2D simulations of unsupervised plant evolution in textbook cases of geographical speciation. After thousands of generations and millions of plants, the extracted phylogenetic data not only showed the expected results in terms of branching pattern (anagenesis, cladogenesis) but also exhibited complex interactions between species both in space and time.