Consciousness remains one of the most profound and complex questions in neuroscience and philosophy. Understanding how subjective experience arises from neural activity is a central challenge for brain science. Computational neuroscience offers powerful tools to explore this mystery by modeling the neural dynamics, network structures, and information processing mechanisms thought to underlie conscious states. By simulating aspects of attention, perception, integration, and awareness, computational models provide testable frameworks that bridge empirical data and theoretical insights. Recent advances in brain imaging, neural decoding, and machine learning have enabled deeper investigations into the correlates and mechanisms of consciousness across different states—such as wakefulness, sleep, anesthesia, and altered states. This Research Topic, Computational Perspectives on Consciousness, aims to highlight interdisciplinary work that uses computational approaches to investigate the neural basis of conscious experience, offering new insights into how the brain gives rise to awareness and self-perception.
The mechanisms by which consciousness arises from neural activity remain unclear. While neuroscience has identified brain regions and activity patterns linked to conscious states, understanding the underlying computational principles is still a major challenge. Moving beyond correlation to causation requires models that capture how the brain integrates information and maintains awareness. Computational neuroscience provides valuable tools to address this, offering models of global workspace dynamics, recurrent processing, and attention. Recent advances in brain imaging, neural decoding, and machine learning have enabled deeper exploration of conscious states across wakefulness, sleep, and anesthesia.
This Research Topic invites contributions that use computational approaches to investigate the neural basis of consciousness. We welcome theoretical models, neural simulations, and data-constrained theoretical studies that link brain dynamics to conscious experience. The aim is to bridge empirical findings with computational insight and advance our understanding of how the brain gives rise to awareness.
This Research Topic papers focused on computational models of consciousness. We welcome studies exploring frameworks such as global workspace theory, integrated information theory, recurrent processing, and attention-based mechanisms. Contributions may include neural simulations, data-constrained theoretical models, or machine learning analyses across various states of consciousness (e.g., wakefulness, sleep, anesthesia). Interdisciplinary work bridging neuroscience, cognitive science, and philosophy is especially encouraged. The goal is to advance testable, mechanistic insights into how brain activity gives rise to conscious experience.
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