Frontiers in Computational Neuroscience
January 1, 2021
Edmund T Rolls
19 citations
Mental states and brain states are linked by a non-causal supervenient relationship, not by direct causation. Events at sub-neuronal, neuronal, and network levels occur simultaneously to perform computations describable as mental states with content about the world. Causality operates within levels of explanation but not between them, allowing mental properties to be emergent yet mechanistically expected. This theory avoids dualism and reductive physicalism, rooted in computational processes. For arithmetic, mental-level algorithmic descriptions are useful; for psychiatric disorders, understanding neural mechanisms aids treatment.
Frontiers in Computational Neuroscience
January 1, 2021
Edmund T Rolls
17 citations
A computational neuroscience theory of mind-brain relations proposes that mental states are high-level descriptions of simultaneous sub-neuronal, neuronal, and network-level computations. These levels are linked by non-causal supervenience, not causation; causality operates only within levels, not between them. The theory requires causality to satisfy three conditions: interventionist tests, same-level events, and a temporal order with a timescale of about 10 ms. While mental-level causal descriptions can be useful, brain-level accounts may be more accurate because mental-level accounts can involve confabulation. Cases of apparent downward causation are reinterpreted as within-level causation. This approach offers a path beyond Cartesian dualism and physical reductionism.
Frontiers in Computational Neuroscience
January 1, 2023
Diana Stanciu
5 citations
The new approach in cognitive science known as 4E cognition (embodied, embedded, enactive, extended) may revive certain Aristotelian concepts, particularly his notion of nature as an inner impulse to movement that is neither fully corporeal nor incorporeal, and his distinction between active and passive intellect. By analyzing Aristotle's definitions in Physics, On the Parts of Animals, and On the Soul, the author argues that the mind-body problem central to explaining consciousness can be partially eluded or transcended through a subtle account of causation. Recent neuroscience findings on consciousness could be better understood within a 4E cognition paradigm inspired by these Aristotelian views.
Frontiers in Computational Neuroscience
May 12, 2023
Pratik Purohit, Prasun Dutta, Prasun K. Roy
A quantitative model describes how visual-spatial perception changes under agents that hyperactivate or hypoactivate the sympathetic or parasympathetic nervous system. The model uses a Hill equation to relate neuromodulator concentration to perceptual alteration, quantified via a metric tensor. Simulations of psilocybin (hyperactivation) and chlorpromazine (hypoactivation) in brain tissue matched behavioral experiments: for psilocybin, a Hill coefficient of 14.8 and constant of 1.39 produced theoretical predictions that robustly fit experimental data (χ² test, p > 0.99). Neural tracts between cortical area V2 and the entorhinal cortex were identified, and grid-cell network simulations also followed the Hill equation. The approach could serve as a screening tool for perceptual misjudgment in stressed workers.
Frontiers in Computational Neuroscience
January 1, 2026
Joseph Bodenheimer, Paul Bogdan, Sérgio Pequito et al.
Brain dynamics change in distinct ways across levels of consciousness—awake, light sedation, deep sedation, and recovery—as measured by fMRI. Using linear time-invariant dynamical systems with unknown inputs, the authors show that the stability and frequency of the brain's oscillatory modes shift during transitions between consciousness states. The same models identify external drivers that influence large-scale brain activity during naturalistic auditory stimulation, and these drivers differ across consciousness states. The approach captures brain-wide dynamic changes not amenable to conventional analysis, suggesting potential biomarkers for consciousness recovery in disorders of consciousness.
Frontiers in Computational Neuroscience
January 1, 2026
Tam Hunt
The binding problem—how distributed neural processes create unified conscious experience—and the criticality problem—how the brain maintains optimal information processing at the boundary between order and chaos—can both be resolved by shifting from a discrete, computational view of the brain to an electromagnetic field perspective. Drawing on Alan Watts' distinction between "prickles" (particles) and "goo" (continuous waves), the paper argues that electromagnetic field interactions naturally account for spatial and temporal binding through cross-frequency coupling and explain neural criticality through volumetric field propagation. Evidence shows electromagnetic fields entrain neural spike timing at thresholds as low as 0.
Frontiers in Computational Neuroscience
January 1, 2025
Darren J Edwards
A new theoretical model called the N-Frame integrates predictive coding, quantum Bayesianism, and evolutionary dynamics to explain how conscious observers update beliefs and interact within a quantum cognitive system. The model suggests that consciousness actively participates in wavefunction collapse, bridging quantum potentiality and classical outcomes through internal states and contextual interactions. It frames human cognitive biases not as errors but as evolutionarily stable quantum heuristic strategies that optimize predictive accuracy under uncertainty. The framework offers testable predictions about AI consciousness by specifying informational boundaries, contextual parameters, and a conscious-time dimension derived from AdS/CFT correspondence, providing a unified account of consciousness, decision-making, behavior, and quantum mechanics.
Frontiers in Computational Neuroscience
January 1, 2024
Rousslan Fernand Julien Dossa, Kai Arulkumaran, Arthur Juliani et al.
An embodied agent with a structure based on global workspace theory, trained on realistic audiovisual inputs to navigate 3D environments, performs better and more robustly at smaller working memory sizes compared to a standard recurrent architecture. Task complexity and regularization are essential for feature learning and the development of meaningful attentional patterns within the workspace.
Frontiers in Computational Neuroscience
January 1, 2020
Patrick Krauss, Andreas Maier
Whether machines could become self-aware is a longstanding philosophical question, but self-awareness cannot be observed externally, and distinguishing genuine consciousness from clever imitation requires access to inner workings. Examining common machine learning approaches reveals that many important algorithmic steps toward machines with a core consciousness have already been taken.