Structural transition in the collective behavior of cognitive agents

Hornischer, Hannes; Herminghaus, Stephan; Mazza, Marco G.

doi:10.1038/s41598-019-48638-8

Cited by 16 publications

(19 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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.

View full text Add to dashboard Cite

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.

show abstract

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.

View full text Add to dashboard Cite

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

View full text Add to dashboard Cite

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.

show abstract

“…[3] proposed a conceptual framework named experience-oriented intelligent things (EOIT) to extract driving behavior fingerprints which needs enough experience obtained by catching driving data in advance. At the same time, Hornischer et al [4] believed the spatial information is minimal definition of a cognitive map and developed a minimal model of agents to explore environment by means of sampling trajectories. That means the formation of internal cognition are related to the spatial overlap of cognitive maps, so the introduction of geographic information can effectively support the cognitive process.…”

Section: Introductionmentioning

confidence: 99%

Communication Emitter Motion Behavior’s Cognition Based on Deep Reinforcement Learning

Wang

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Considering the successful application of deep reinforcement learning (DRL) on tasks of moving objects, this paper innovatively applies deep deterministic policy gradient algorithm (DDPG) to complete the cognition task on multi-dimension and continuous communication emitter motion behavior. First, we propose a DDPG-based behavior cognition algorithm (DDPG-BC). It chooses direction, velocity, and communication frequency as state space, gains experience from interaction between network and environment and outputs deterministic cognition results. Second, under the condition of sufficient prior information such as geographic information, we further propose a novel algorithm named DDPG-based behavior cognition with Attention algorithm (DDPG+A-BC). It introduces attention mechanism into DDPG-BC which limits exploration scope and the randomness of initial state and improves the exploration efficiency and accuracy. The simulation experiments verify that DDPG-BC and DDPG+A-BC show good cognition ability on two different data set. And the algorithms are all superior to other DRL algorithm and existing cognition method with higher cognition accuracy and less time. In addition, we also discuss the influence of episode, reward function, and added attention mechanism on algorithm performance.

show abstract

Structural transition in the collective behavior of cognitive agents

Cited by 16 publications

References 48 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

Communication Emitter Motion Behavior’s Cognition Based on Deep Reinforcement Learning

Contact Info

Product

Resources

About