Neuromorphic in Human Resources: Reconstructing AI Decision-Making Processes Inspired by the Octopus Brain
This study presents an innovative model for decision-making in human resource management, inspired by the neural structures of the octopus brain and neuromorphic AI systems. The proposed model offers optimizations to enhance decision-making processes based on a detailed comparative analysis of the similarities and differences between these two systems. The findings indicate that parallel and distributed processing architectures, adaptive independent decision-making, and continuous learning can help organizations navigate complex and dynamic environments. The octopus brain's characteristics, such as decentralized decision-making, rapid response to stressors, and independent error correction at the arm level, combined with neuromorphic capabilities, have led to the development of a model capable of simultaneously processing performance data, predicting employee behaviors, and optimizing processing resources. This model is particularly applicable in areas such as performance evaluation, talent management and succession planning, employee behavior prediction, smart recruitment, employee sentiment analysis, and career path optimization. The study’s recommendations emphasize the use of multitasking processing, self-regulating synaptic coordination, and energy-efficient processing optimization, which can improve decision-making and increase efficiency in human resource management. This research takes a significant step toward leveraging advanced technologies and biological structures to address complex management challenges, enabling organizations to substantially enhance their productivity. The proposed model, as an advanced operational framework, provides a new pathway for future research and managerial applications.
-
Cognitive Concepts of Employees in the Face of Artificial Intelligence
*
Journal of Police Artificial Intelligence, Summer 2025 -
Employing Artificial Intelligence in the Identification of Unidentified Faces in Criminal Investigations (Case Study: Victims with Severe Burns)
*, Matineh Moghaddam
Journal of Police Artificial Intelligence, Spring 2025