Cognitive swarming in complex environments with attractor dynamics and oscillatory computing

Abstract

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 lowfootprint 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 rewardapproach 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, Neu-roSwarms is a model artificial spatial system that integrates autonomous control and theoretical neuroscience to potentially uncover common principles to advance both domains.

From spatial navigation via visual construction to episodic memory and imagination

“…The concluding subsection, then, offers a brief sketch of research challenges that may exercise the imagination of the spatial navigation community-to imagine imagination. Monaco et al (2020) stress, as we do, the need to extend models from simple environments to animals' natural habitats. However, where we stress the role of diverse WGs, their concern is with autonomous systems technology, and so they 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 by analogizing agents to neurons and swarming groups to recurrent networks.…”

Scale-Free Dynamics in Animal Groups and Brain Networks

“…This aspect has gained increased attention in the context of artificial intelligence, with many studies proposing the usage of artificial swarm systems (Hornischer et al, 2019 ; Sueoka et al, 2019 ). The brain also provides inspiration for these systems: Monaco et al ( 2020 ) proposed an analogy between these multi-agent robotic platforms and place cells in the hippocampus, suggesting improvements to current models that follow solutions found by brain circuits. Startle responses in animal populations can trigger escape waves (Herbert-Read et al, 2015 ; Sosna et al, 2019 ), in the latter case yielding heavy-tail cascade size distributions and involve distributed repositioning of in the swarm beyond an individual's sensitivity changes to perturbation.…”

Online learning for orientation estimation during translation in an insect ring attractor network

“…Mammalian systems are among the most studied in neuroscience in relation to navigation with research breakthroughs over the past decades identifying several specialized cell types including head direction cells (28), grid cells (29), and place cells (30). There have been several approaches utilizing these representations and additional specialized mammalian cell types (31,32) to propose algorithms for neuromorphic robotic navigation (33)(34)(35)(36)(37)(38). All of these neurons represent space in an allocentric coordinate system, which is in reference to a world frame, rather than in an egocentric coordinate system, centered to the animal's body or the sensors as commonly seen in low-level sensory processing.…”