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In recent years, the field of neuroscience has gone through rapid experimental advances and a significant increase in the use of quantitative and computational methods. This growth has created a need for clearer analyses of the theory and modeling approaches used in the field. This issue is particularly complex in neuroscience because the field studies phenomena that cross a wide range of scales and often require consideration at varying degrees of abstraction, from precise biophysical interactions to the computations they implement. We argue that a pragmatic perspective of science, in which descriptive, mechanistic, and normative models and theories each play a distinct role in defining and bridging levels of abstraction, will facilitate neuroscientific practice. This analysis leads to methodological suggestions, including selecting a level of abstraction that is appropriate for a given problem, identifying transfer functions to connect models and data, and the use of models themselves as a form of experiment. 

This article discusses how to create an interactive virtual training program at the intersection of neuroscience, robotics, and computer science for high school students with equity of access. A four-day microseminar, titled Swarming Powered by Neuroscience (SPN), was conducted virtually through a combination of presentations and interactive computer game simulations. The SPN microseminar was delivered by subject matter experts in neuroscience, mathematics, multi-agent swarm robotics, and education. The objective of this research was to determine if taking an interdisciplinary approach to high school education would enhance the students learning experiences in fields such as neuroscience, robotics, or computer science. This study found an improvement in student engagement for neuroscience by 16.6%, while interest in robotics and computer science improved respectively by 2.7% and 1.8%. The majority of students (64%) strongly agreed that they enjoyed learning from an interdisciplinary team of experts and 70% strongly agreed that the microseminar emphasized the need to have instruction teams with diverse disciplinary backgrounds. The curriculum materials, developed for the SPN microseminar, can be used by high school teachers to further evaluate interdisciplinary instructions across life and physical sciences and computer science. 

In the NeuroSwarms framework, a team including researchers from the Johns Hopkins University Applied Physics Laboratory (APL) and the Johns Hopkins University School of Medicine (JHM) applied key theoretical concepts from neuroscience to models of distributed multi-agent autonomous systems and found that complex swarming behaviors arise from simple learning rules used by the mammalian brain. 

Neurobiological theories of spatial cognition developed with respect to recording data from relatively small and/or simplistic environments compared to animals’ natural habitats. It has been unclear how to extend theoretical models to large or complex spaces. Complementarily, in autonomous systems technology, applications have been growing for distributed control methods that scale to large numbers of low-footprint mobile platforms. Animals and many-robot groups must solve common problems of navigating complex and uncertain environments. Here, we introduce the NeuroSwarms control framework to investigate whether adaptive, autonomous swarm control of minimal artificial agents can be achieved by direct analogy to neural circuits of rodent spatial cognition. NeuroSwarms analogizes agents to neurons and swarming groups to recurrent networks. We implemented neuron-like agent interactions in which mutually visible agents operate as if they were reciprocally connected place cells in an attractor network. We attributed a phase state to agents to enable patterns of oscillatory synchronization similar to hippocampal models of theta-rhythmic (5–12 Hz) sequence generation. We demonstrate that multi-agent swarming and reward-approach dynamics can be expressed as a mobile form of Hebbian learning and that NeuroSwarms supports a single-entity paradigm that directly informs theoretical models of animal cognition. We present emergent behaviors including phase-organized rings and trajectory sequences that interact with environmental cues and geometry in large, fragmented mazes. Thus, NeuroSwarms is a model artificial spatial system that integrates autonomous control and theoretical neuroscience to potentially uncover common principles to advance both domains. 

The rise of mobile multi-agent robotic platforms is outpacing control paradigms for tasks that require operating in complex, realistic environments. To leverage inertial, energetic, and cost bene fits of small-scale robots, critical future applications may depend on coordinating large numbers of agents with minimal onboard sensing and communication resources. In this article, we present the perspective that adaptive and resilient autonomous control of swarms of minimal agents might follow from a direct analogy with the neural circuits of spatial cognition in rodents. We focus on spatial neurons such as place cells found in the hippocampus. Two major emergent hippocampal phenomena, self-stabilizing attractor maps and temporal organization by shared oscillations, reveal theoretical solutions for decentralized self-organization and distributed communication in the brain. We consider that autonomous swarms of minimal agents with low-bandwidth communication are analogous to brain circuits of oscillatory neurons with spike-based propagation of information. The resulting notion of `neural swarm control' has the potential to be scalable, adaptive to dynamic environments, and resilient to communication failures and agent attrition. We illustrate a path toward extending this analogy into multi-agent systems applications and discuss implications for advances in decentralized swarm control.