Neuromorphic Correlates of Artificial Consciousness

• URL: http://arxiv.org/abs/2405.02370v1
• Date: Fri, 3 May 2024 09:27:51 GMT
• Title: Neuromorphic Correlates of Artificial Consciousness
• Authors: Anwaar Ulhaq,
• Abstract summary: The concept of neural correlates of consciousness (NCC) suggests that specific neural activities are linked to conscious experiences. This paper explores the potential for artificial consciousness by merging neuromorphic design and architecture with brain simulations.
• Score: 1.4957306171002251
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Brain-like Functional Organization within Large Language Models [58.93629121400745]
  The human brain has long inspired the pursuit of artificial intelligence (AI) Recent neuroimaging studies provide compelling evidence of alignment between the computational representation of artificial neural networks (ANNs) and the neural responses of the human brain to stimuli. In this study, we bridge this gap by directly coupling sub-groups of artificial neurons with functional brain networks (FBNs) This framework links the AN sub-groups to FBNs, enabling the delineation of brain-like functional organization within large language models (LLMs)
  arXiv  Detail & Related papers  (2024-10-25T13:15:17Z)
• Neuropsychology of AI: Relationship Between Activation Proximity and Categorical Proximity Within Neural Categories of Synthetic Cognition [0.11235145048383502]
  This study focuses on synthetic neural cog nition as a new type of study object within cognitive psychology. The goal is to make artificial neural networks of language models more explainable. This approach involves transposing concepts from cognitive psychology to the interpretive construction of artificial neural cognition.
  arXiv  Detail & Related papers  (2024-10-08T12:34:13Z)
• Enhancing learning in spiking neural networks through neuronal heterogeneity and neuromodulatory signaling [52.06722364186432]
  We propose a biologically-informed framework for enhancing artificial neural networks (ANNs) Our proposed dual-framework approach highlights the potential of spiking neural networks (SNNs) for emulating diverse spiking behaviors. We outline how the proposed approach integrates brain-inspired compartmental models and task-driven SNNs, bioinspiration and complexity.
  arXiv  Detail & Related papers  (2024-07-05T14:11:28Z)
• A theory of neural emulators [0.0]
  A central goal in neuroscience is to provide explanations for how animal nervous systems can generate actions and cognitive states such as consciousness. We propose emulator theory (ET) and neural emulators as circuit- and scale-independent predictive models of biological brain activity.
  arXiv  Detail & Related papers  (2024-05-22T07:12:03Z)
• Is artificial consciousness achievable? Lessons from the human brain [0.0]
  We analyse the question of developing artificial consciousness from an evolutionary perspective. We take the evolution of the human brain and its relation with consciousness as a reference model. We propose to clearly specify what is common and what differs in AI conscious processing from full human conscious experience.
  arXiv  Detail & Related papers  (2024-04-18T12:59:44Z)
• A Review of Neuroscience-Inspired Machine Learning [58.72729525961739]
  Bio-plausible credit assignment is compatible with practically any learning condition and is energy-efficient. In this paper, we survey several vital algorithms that model bio-plausible rules of credit assignment in artificial neural networks. We conclude by discussing the future challenges that will need to be addressed in order to make such algorithms more useful in practical applications.
  arXiv  Detail & Related papers  (2024-02-16T18:05:09Z)
• Brain-Inspired Machine Intelligence: A Survey of Neurobiologically-Plausible Credit Assignment [65.268245109828]
  We examine algorithms for conducting credit assignment in artificial neural networks that are inspired or motivated by neurobiology. We organize the ever-growing set of brain-inspired learning schemes into six general families and consider these in the context of backpropagation of errors. The results of this review are meant to encourage future developments in neuro-mimetic systems and their constituent learning processes.
  arXiv  Detail & Related papers  (2023-12-01T05:20:57Z)
• A Neuro-mimetic Realization of the Common Model of Cognition via Hebbian Learning and Free Energy Minimization [55.11642177631929]
  Large neural generative models are capable of synthesizing semantically rich passages of text or producing complex images. We discuss the COGnitive Neural GENerative system, such an architecture that casts the Common Model of Cognition.
  arXiv  Detail & Related papers  (2023-10-14T23:28:48Z)
• Survey of Consciousness Theory from Computational Perspective [8.521492577054078]
  This paper surveys several main branches of consciousness theories originating from different subjects. It also discusses the existing evaluation metrics of consciousness and possibility for current computational models to be conscious.
  arXiv  Detail & Related papers  (2023-09-18T18:23:58Z)
• Constraints on the design of neuromorphic circuits set by the properties of neural population codes [61.15277741147157]
  In the brain, information is encoded, transmitted and used to inform behaviour. Neuromorphic circuits need to encode information in a way compatible to that used by populations of neuron in the brain.
  arXiv  Detail & Related papers  (2022-12-08T15:16:04Z)
• The problem with AI consciousness: A neurogenetic case against synthetic sentience [0.0]
  The paper argues against the plausibility of sentient AI based on the theory of neurogenetic structuralism. It claims that the physiology of biological neurons and their structural organization into complex brains are necessary prerequisites for true consciousness to emerge.
  arXiv  Detail & Related papers  (2022-12-07T14:46:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.