Novel Directions for Neuromorphic Machine Intelligence Guided by Functional Connectivity: A Review DOI Creative Commons
Mindula Illeperuma, Rafael Pina, De Silva

и другие.

Machines, Год журнала: 2024, Номер 12(8), С. 574 - 574

Опубликована: Авг. 20, 2024

As we move into the next stages of technological revolution, artificial intelligence (AI) that is explainable and sustainable becoming a key goal for researchers across multiple domains. Leveraging concept functional connectivity (FC) in human brain, this paper provides novel research directions neuromorphic machine (NMI) systems are energy-efficient human-compatible. This review serves as an accessible multidisciplinary introducing range concepts inspired by neuroscience analogous learning research. These include possibilities to facilitate network integration segregation architectures, representation framework two FC networks utilised learning, explore underlying task prioritisation humans propose machines improve their task-prioritisation decision-making capabilities. Finally, provide application domains such autonomous driverless vehicles, swarm intelligence, augmentation, name few. Guided how regional brain interact cognition behaviour ones discussed review, toward blueprint creating NMI mirrors these processes.

Язык: Английский

Novel Directions for Neuromorphic Machine Intelligence Guided by Functional Connectivity: A Review DOI Creative Commons
Mindula Illeperuma, Rafael Pina, De Silva

и другие.

Machines, Год журнала: 2024, Номер 12(8), С. 574 - 574

Опубликована: Авг. 20, 2024

As we move into the next stages of technological revolution, artificial intelligence (AI) that is explainable and sustainable becoming a key goal for researchers across multiple domains. Leveraging concept functional connectivity (FC) in human brain, this paper provides novel research directions neuromorphic machine (NMI) systems are energy-efficient human-compatible. This review serves as an accessible multidisciplinary introducing range concepts inspired by neuroscience analogous learning research. These include possibilities to facilitate network integration segregation architectures, representation framework two FC networks utilised learning, explore underlying task prioritisation humans propose machines improve their task-prioritisation decision-making capabilities. Finally, provide application domains such autonomous driverless vehicles, swarm intelligence, augmentation, name few. Guided how regional brain interact cognition behaviour ones discussed review, toward blueprint creating NMI mirrors these processes.

Язык: Английский

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