Neuromorphic Correlates of Artificial Consciousness

A theoretical framework called Neuromorphic Correlates of Artificial Consciousness (NCAC) is proposed, merging neuromorphic design and brain simulations to explore artificial consciousness. The paper draws on evidence from neural correlates of consciousness, brain imaging (fMRI, EEG), integrated information theory, and advances from the Human Brain Project, AI, and quantum computing. It outlines how machine learning could contribute to creating machine consciousness, though the debate remains contentious due to incomplete understanding of consciousness.

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Abstract

The concept of neural correlates of consciousness (NCC), which suggests that specific neural activities are linked to conscious experiences, has gained widespread acceptance. This acceptance is based on a wealth of evidence from experimental studies, brain imaging techniques such as fMRI and EEG, and theoretical frameworks like integrated information theory (IIT) within neuroscience and the philosophy of mind. This paper explores the potential for artificial consciousness by merging neuromorphic design and architecture with brain simulations. It proposes the Neuromorphic Correlates of Artificial Consciousness (NCAC) as a theoretical framework. While the debate on artificial consciousness remains contentious due to our incomplete grasp of consciousness, this work may raise eyebrows and invite criticism. Nevertheless, this optimistic and forward-thinking approach is fueled by insights from the Human Brain Project, advancements in brain imaging like EEG and fMRI, and recent strides in AI and computing, including quantum and neuromorphic designs. Additionally, this paper outlines how machine learning can play a role in crafting artificial consciousness, aiming to realise machine consciousness and awareness in the future.