Collective Intelligence and Neurodynamics: Functional Homologies
A deep understanding of the dynamics of the human nervous system requires the simultaneous study of multiple spatiotemporal scales from the level of neurotransmitters up to the level of human cultures. This is likely impossible for technical and ethical reasons. Piecemeal analysis provides some understanding of the dynamics at single levels, but this does not illuminate the interactions between levels which are, at the very least, of great importance clinically. It would be useful to have an accessible biological system which could serve as a proxy for the nervous system and from which useful insights might be obtained. Functional homologies between the nervous system and collective intelligence systems, in particular social insect colonies, are described. It is proposed that social insects colonies could serve as functional proxies for nervous systems. Thus a multiscale study of social insect colonies may provide insights into the dynamics of nervous systems.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
AI-enhanced Collective Intelligence
Current societal challenges exceed the capacity of humans operating either alone or collectively. As AI evolves, its role within human collectives will vary from an assistive tool to a participatory member. Humans and AI…
DiversityIntelligent Collective Escape of Swarm Robots Based on a Novel Fish-inspired Self-adaptive Approach with Neurodynamic Models
Fish schools present high-efficiency group behaviors through simple individual interactions to collective migration and dynamic escape from the predator. The school behavior of fish is usually a good inspiration to desig…
Software Engineering for Collective Cyber-Physical Ecosystems
Today's distributed and pervasive computing addresses large-scale cyber-physical ecosystems, characterised by dense and large networks of devices capable of computation, communication and interaction with the environment…
Localized Persistent Homologies for more Effective Deep Learning
Persistent Homologies have been successfully used to increase the performance of deep networks trained to detect curvilinear structures and to improve the topological quality of the results. However, existing methods are…
Deep LearningNeurodynamics-Driven Coupled Neural P Systems for Multi-Focus Image Fusion
Multi-focus image fusion (MFIF) is a crucial technique in image processing, with a key challenge being the generation of decision maps with precise boundaries. However, traditional methods based on heuristic rules and de…