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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.
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Evolution of Embodied Agents on a Numerical Cognition Task: Sequential Counting
This paper investigates the capability of embodied agents to perform a sequential counting task. Drawing inspiration from honeybee studies, we present a minimal numerical cognition task wherein an agent navigates a 1D world marked with landmarks to locate a previously encountered food source. We evolved embodied artificial agents controlled by dynamical recurrent neural networks to be capable of associating a food reward with encountering a number of landmarks sequentially. To eliminate the possibility of the evolved agents relying on distance to locate the target landmark, we varied the positions of the landmarks across trials. Our experiments demonstrate that embodied agents equipped with relatively small neural networks can accurately enumerate and remember up to five landmarks when encountered sequentially. Counter to the intuitive notion that numerical cognition is a complex, higher cortical function, our findings support the idea that numerical discrimination can be achieved in relatively compact neural circuits.
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- Award ID(s):
- 2345446
- PAR ID:
- 10529285
- Editor(s):
- Faíña, A; Risi, S; Medvet, E; Stoy, K; Chan, B; Miras, K; Zahadat, P; Grbic, D; Nadizar, G
- Publisher / Repository:
- Artificial Life Conference Proceedings. MIT Press.
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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