A review of neuroscience-inspired frameworks for machine consciousness

Abstract

Consciousness in humans is a state of awareness that encompasses both the self and the external environment, emerging from the intricate interplay of cortical and subcortical brain structures and neurotransmitter systems. The possibility that machines could possess consciousness has sparked ongoing debate. Proponents of strong artificial intelligence (AI) equate programmed computational processes with cognitive states, while advocates of weak AI argue that machines merely simulate thought without attaining genuine consciousness. This review critically examines neuroscience-inspired frameworks for artificial consciousness, exploring their alignment with prevailing theories of human consciousness. We investigate the fundamental cognitive functions associated with consciousness, including memory, awareness, prediction, learning, and experience, and their relevance to artificial systems. By analyzing neuroscience-based approaches to artificial consciousness, we identify key challenges and opportunities in the pursuit of machines capable of mimicking conscious states. Although present AI systems demonstrate advanced capabilities in intelligence and cognition, they fall short of achieving genuine consciousness, as defined in the context of human awareness. We discuss both the theoretical underpinnings and practical implications of creating artificial consciousness, addressing both weak and strong AI perspectives. Furthermore, we highlight the ethical and philosophical concerns that arise with the potential realization of machine consciousness. Our objective is to provide a comprehensive synthesis of the literature, fostering a deeper understanding of the interdisciplinary challenges involved in artificial consciousness and guiding future research directions. © 2025 Author(s).