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Home / Research / Neuromorphic Correlates of Artificial Consciousness in TCAI

Neuromorphic Correlates of Artificial Consciousness in TCAI

20 Jan 2025

How can neuromorphic design contribute to artificial consciousness? This paper by Anwaar Ulhaq proposes the Neuromorphic Correlates of Artificial Consciousness (NCAC), a framework inspired by the neural correlates of consciousness (NCC) in neuroscience. Neuromorphic design also survives conditions classical hardware does not, and TechEdSat-13 flight data shows spiking networks with STDP self-repair absorbing the bit flips that cosmic rays cause.

Neuromorphic Correlates of Artificial Consciousness, authored by Anwaar Ulhaq, explores the intersection of neuromorphic architecture and brain simulations for advancing AI consciousness. Xu He and colleagues later built a working case at that intersection, pairing hippocampal path integration with human mission protocols to navigate GPS-denied and high-dynamics environments.

Key Highlights

  • Neuromorphic Design Introduces NCAC as a theoretical framework linking neuromorphic architecture to conscious-like behaviors in AI.
  • Neural Correlates of Consciousness Draws parallels between biological and artificial neural systems to simulate consciousness.
  • Practical Applications Highlights advancements in AI and computing, including quantum and neuromorphic designs, as pathways to artificial consciousness.

Connection to TCAI

The Consciousness AI (TCAI) does not directly align with the neuromorphic principles in this paper, as TCAI focuses on layered simulations and multimodal agent designs rather than hardware-level architectures. However, the concept of correlating computational structures to conscious behaviors may provide conceptual value for TCAI’s theoretical framework.

For a detailed exploration of NCAC and its implications, access the full paper here.

The neuromorphic correlates programme now has a concrete spiking instance. Lahoz-Beltra’s DMN pacemaker model ties a self-representation to tonic drive in a 10,000-neuron network and names Loihi-class hardware as its testbed.

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