Analysis of push-forward model for swarm-like collective motions

Sueoka, Yuichiro; Sato, Yuto; Ishitani, Makihiko; Osuka, Koichi

doi:10.1007/s10015-019-00548-8

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…Another parallel between scale-free flocks and brains is the presence of decentralized signal processing. 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.…”

Section: Importance Of Scale-free Correlations For Brain Functionmentioning

confidence: 99%

Scale-Free Dynamics in Animal Groups and Brain Networks

Ribeiro

Chialvo

Plenz

2021

Front. Syst. Neurosci.

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Collective phenomena fascinate by the emergence of order in systems composed of a myriad of small entities. They are ubiquitous in nature and can be found over a vast range of scales in physical and biological systems. Their key feature is the seemingly effortless emergence of adaptive collective behavior that cannot be trivially explained by the properties of the system's individual components. This perspective focuses on recent insights into the similarities of correlations for two apparently disparate phenomena: flocking in animal groups and neuronal ensemble activity in the brain. We first will summarize findings on the spontaneous organization in bird flocks and macro-scale human brain activity utilizing correlation functions and insights from critical dynamics. We then will discuss recent experimental findings that apply these approaches to the collective response of neurons to visual and motor processing, i.e., to local perturbations of neuronal networks at the meso- and microscale. We show how scale-free correlation functions capture the collective organization of neuronal avalanches in evoked neuronal populations in nonhuman primates and between neurons during visual processing in rodents. These experimental findings suggest that the coherent collective neural activity observed at scales much larger than the length of the direct neuronal interactions is demonstrative of a phase transition and we discuss the experimental support for either discontinuous or continuous phase transitions. We conclude that at or near a phase-transition neuronal information can propagate in the brain with similar efficiency as proposed to occur in the collective adaptive response observed in some animal groups.

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Section: Importance Of Scale-free Correlations For Brain Functionmentioning

confidence: 99%

Scale-Free Dynamics in Animal Groups and Brain Networks

Ribeiro

Chialvo

Plenz

2021

Front. Syst. Neurosci.

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show abstract

“…Another parallel between scale-free flocks and brains is the presence of decentralized signal processing. 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 and colleagues (Monaco et al, 2019) 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.…”

Section: Importance Of Scale-free Correlations For Brain Functionmentioning

confidence: 99%

Scale-free dynamics in animal groups and brain networks

Ribeiro

Chialvo

Plenz

2020

Preprint

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Collective phenomena fascinate by the emergence of order in systems composed of a myriad of small entities. They are ubiquitous in nature and can be found over a vast range of scales in physical and biological systems. Their key feature is the seemingly effortless emergence of adaptive collective behavior that cannot be trivially explained by the properties of the system’s individual components. This perspective focuses on recent insights into the similarities of correlations for two apparently disparate phenomena: flocking in animal groups and neuronal ensemble activity in the brain. We first will summarize findings on the spontaneous organization in bird flocks and macro-scale human brain activity utilizing correlation functions and insights from critical dynamics. We then will discuss recent experimental findings that apply these approaches to the collective response of neurons to visual and motor processing, i.e. to local perturbations of neuronal networks at the meso- and microscale. We show how scale-free correlation functions capture the collective organization of neuronal avalanches in evoked neuronal populations in nonhuman primates and between neurons during visual processing in rodents. These experimental findings suggest that the coherent collective neural activity observed at scales much larger than the length of the direct neuronal interactions is demonstrative of a phase transition. We discuss the experimental support for either discontinuous or continuous phase transitions. We conclude that at or near a phase-transition neuronal information can propagate in the brain with the same efficiency as proposed to occur in the collective adaptive response observed in some animal groups.

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“…The problem of controlling a swarm of drones is considered in a large number of articles today. Thus, an algorithm for organizing a collective movement based on the movement of a swarm "behind the leader", which is identified quite randomly, is considered [3,4]. In several works, for example, the attention of researchers is focused on the development of collision avoidance models in a moving swarm [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Approach to conception and modeling for distributed hierarchical control for autonomous drone swarm

2024

Adv Mach Lear Art Inte

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Control of a drone swarm as a unity requires decentralization and hierarchy. Decentralizing control of drone swarms is necessary to free the human operator from having to constantly control the behavior of the drones within the swarm. Hierarchical control of a drone swarm is necessary so that human operators can adjust the activity of the swarm as a unit. The following separate roles have been identified for the implementation of decentralized hierarchical control of swarm activity: the activity of a separate drone, the activity of a drone- coordinator, and the activity of a human operator. The control hierarchy consists of a human operator who controls the change in the behavior of the drone coordinator. The drone coordinator controls the changes in the programmed behavior of individual drones. This approach is an analog of the management of human workers who perform assigned work, which opens up several possibilities. First, it is possible to use formal models of performance people’s behavior in social teams. Second, formal models can be used for decision-making and optimization for controlling a drone-coordinator in a swarm. Thirdly, computer modeling can be applied to the behavior of a drone swarm, which will allow choosing the optimal behavior of the swarm for different conditions of its activity

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Analysis of push-forward model for swarm-like collective motions

Cited by 5 publications

References 15 publications

Scale-Free Dynamics in Animal Groups and Brain Networks

Scale-Free Dynamics in Animal Groups and Brain Networks

Scale-free dynamics in animal groups and brain networks

Approach to conception and modeling for distributed hierarchical control for autonomous drone swarm

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