Abstract Minimalistic robot swarms hold great promise for applications in healthcare, disaster response, and environmental monitoring. A key challenge lies in enabling these robots to rapidly and reliably reach consensus using limited communication, computation, and memory. In this talk, we explore how robot swarms can collectively identify the best among multiple discrete options in their

Abstract: Cognitive neuroscience seeks theories that jointly explain behavioral, neural, and mental states. The dominant approach is to use specialized tasks designed to optimally probe a concept of interest (e.g., episodic memory), and to disentangle behavioral, sensory, and mnemonic factors through design (e.g., by constraining gaze during image recognition). I will present an alternative framework

With the rise of modern deep learning, neural networks have become an essential part of virtually every artificial intelligence system, making it difficult to imagine different models for intelligent behavior. In contrast, nature provides us with many different mechanisms for intelligent behavior, most of which we have yet to utilize. One such underinvestigated aspect of

More details to follow. Image created with DALL-E by Maria Ott.