The mammalian suprachiasmatic nucleus (SCN) comprises about 20,000 interconnected oscillatory neurons that create and maintain a robust circadian signal which matches to external light cues. Here, we use an evolutionary game theoretic framework to explore how evolutionary constraints can influence the synchronization of the system under various assumptions on the connection topology, contributing to the understanding of the structure of interneuron connectivity. Our basic model represents the SCN as a network of agents each with two properties—a phase and a flag that determines if it communicates with its neighbors or not. Communication comes at a cost to the agent, but synchronization of phases with its neighbors bears a benefit. Earlier work shows that when we have “all-to-all” connectivity, where every agent potentially communicates with every other agent, there is often a simple trade-off that leads to complete communication and synchronization of the system: the benefit must be greater than twice the cost. This trade-off for all-to-all connectivity gives us a baseline to compare to when looking at other topologies. Using simulations, we compare three plausible topologies to the all-to-all case, finding that convergence to synchronous dynamics occurs in all considered topologies under similar benefit and cost trade-offs. Consequently, sparser, lessmore »
On Emergent Communication in Competitive Multi-Agent Teams
Several recent works have found the emergence of grounded com-positional language in the communication protocols developed bymostly cooperative multi-agent systems when learned end-to-endto maximize performance on a downstream task. However, humanpopulations learn to solve complex tasks involving communicativebehaviors not only in fully cooperative settings but also in scenar-ios where competition acts as an additional external pressure forimprovement. In this work, we investigate whether competitionfor performance from an external, similar agent team could actas a social influence that encourages multi-agent populations todevelop better communication protocols for improved performance,compositionality, and convergence speed. We start fromTask &Talk, a previously proposed referential game between two coopera-tive agents as our testbed and extend it intoTask, Talk & Compete,a game involving two competitive teams each consisting of twoaforementioned cooperative agents. Using this new setting, we pro-vide an empirical study demonstrating the impact of competitiveinfluence on multi-agent teams. Our results show that an externalcompetitive influence leads to improved accuracy and generaliza-tion, as well as faster emergence of communicative languages thatare more informative and compositional.
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- Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems
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- National Science Foundation
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