Neuromorphic Correlates of Artificial Consciousness

TL;DR

This paper proposes a theoretical framework called Neuromorphic Correlates of Artificial Consciousness (NCAC) that integrates neuromorphic architectures, brain simulations, and machine learning to explore the potential for artificial consciousness.

Contribution

It introduces the NCAC framework combining neuromorphic design, brain imaging insights, and AI advancements to advance understanding of artificial consciousness.

Findings

Proposes the NCAC theoretical framework.

Highlights the role of machine learning in artificial consciousness.

Connects neuromorphic systems with brain simulation research.

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.