This paper investigates respiratory odor navigation with minimal evolutionary robots. We introduce a novel agent tasked with locating a chemical source solely through the use of a respiratory sensor, a challenge inspired by the active sampling strategies observed in a wide variety of animals (e.g., sniffing, whisking). Prevailing hypotheses suggest that odor navigation serves predominantly to gate behaviors in response to information gleaned from different sensory modalities. We analyze the agent’s behavior and neural dynamics using dynamical systems theory, demonstrating the possibility of strategies that instead rely solely on the information obtained from their respiratory sensor. Our findings reveal that agents can successfully locate chemical sources through the synchronization of breathing rates with motor outputs, mirroring sensorimotor coupling strategies recently identified in the experimental literature. This research contributes to the theoretical understanding of sensory odor navigation and the role of physiology in agent-environment interactions.

The perceptual crossing task has been used to understand social interaction for over a decade. To what extent is the interaction between evolved perceptual crossers truly mutual? To address this question, we undertake a three-pronged examination of the mutuality of simulated social interaction. First, we construct a decoy object that moves at a set amplitude and frequency. This decoy object serves as a benchmark to systematically assess whether the agents can be deceived by non-social oscillatory movement - essentially, whether they mistake the simple, mechanical movement of the decoy for the behavior of another agent. Second, we use agents’ performance with the decoy and agents’ performance with each other to identify convincingly social agents for further analysis. This approach helped us identify that many agents, previously thought to be robust, did not meet our criteria for mutual interaction. However, it also importantly led to the identification of three agents that demonstrate the strongest potential for genuine mutual interaction. Finally, we delve into a detailed investigation of these three agents, focusing on their behavioral patterns and the dynamic strategies they employ.

In their most abstract form, we can understand tissues as being composed of three general cell types: stem cells, transit-amplifying cells, and differentiated cells. Additionally, we know that these cell types can secrete molecules or regulatory factors that can exert control over other cell populations. Recent work in theoretical biology examined several cell lineage control networks that result in tissue homeostasis. We develop an alternative mass action model that views developmental cell lineages as biological pathways. We demonstrate that three cell lineages are homeostatic irrespective of the implementation and that their control structures exhibit a degeneracy, containing solely negative feedback or negative resistance. We replicate and extend the homeostatic control architectures previously outlined and report on the relevant bifurcations and dynamics of these pathways.

We revisit the perceptual crossing simulation studies, which are aimed at challenging methodological individualism in the analysis of social cognition by studying multi-agent real-time interactions. To date, all of these simulation studies have reported that it is practically impossible to evolve artificially a robust behavioral strategy without introducing temporal delays into the simulation. Also, all of the studies report on a single strategy: a perpetually crossing agent pair. Here, we systematically report on the evolutionary success of neural circuits on the perceptual crossing task, with and without sensory delay. We also report on two different strategies in the ensemble of successful solutions, only one of which had been discussed in the literature previously.