Search arXivSearch

arXiv · 1809.03490

Can quantum physics help solve the hard problem of consciousness? A hypothesis based on entangled spins and photons

Abstract

The hard problem of consciousness is the question how subjective experience arises from brain matter. I suggest exploring the possibility that quantum physics could be part of the answer. The simultaneous unity and complexity of subjective experience is difficult to understand from a classical physics perspective. In contrast, quantum entanglement is naturally both complex and unified. Moreover the concept of matter is much more subtle in quantum physics compared to classical physics, and quantum computing shows that quantum effects can be useful for information processing. Building on recent progress in quantum technology and neuroscience, I propose a concrete hypothesis as a basis for further investigation, namely that subjective experience is related to the dynamics of a complex entangled state of spins, which is continuously generated and updated through the exchange of photons. Spins in condensed matter systems at room or body temperature can have coherence times in the relevant range for subjective experience (milliseconds to seconds). Photons are well suited for distributing entanglement over macroscopic distances. Neurons emit photons, reactive oxygen species in the mitochondria being likely sources. Opsins, light-sensitive proteins that are plausible single-photon detectors, exist in the brain and are evolutionarily conserved, suggesting that they serve a function. We have recently shown by detailed numerical modeling that axons can plausibly act as photonic waveguides. The oxygen molecule, which has non-zero electronic spin and emits photons, might serve as an interface between photons and spins. The achievable photon rates seem to be more than sufficient to support the bandwidth of subjective experience. The proposed hypothesis raises many interesting experimental and theoretical questions in neuroscience, quantum physics, evolutionary biology, psychophysics, and philosophy.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Christoph Simon. 2018-09-08. Can quantum physics help solve the hard problem of consciousness? A hypothesis based on entangled spins and photons. https://arxiv.org/abs/1809.03490

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Identifying Neural State Changes due to Gain versus Off-Manifold Displacement

Memory segmentation is thought to arise from rapid decorrelation in neural activity, often quantified by Euclidean distance or cosine angle. Although these metrics detect a transition, they do not reveal how the new state relates to the repertoire represented by the neural manifold. This matters because neuromodulators that drive state transitions also alter excitability, and learning may repurpose existing representations or create new ones. Here, I introduce a geometric decomposition that separates changes attributable to gain modulation of a nearby manifold state from movement within the manifold and genuine off-manifold displacement. The approach uses the radial axis of neural population activity to partition the normal space of a local manifold region. A central challenge is identifiability: given only a static reference manifold and a single test state, neither the state from which a perturbation began nor its gain magnitude and mechanistic decomposition can generally be recovered uniquely. I therefore formulate identifiability as a cascade of geometric gates specifying when each component can be interpreted. The gates distinguish structural failures, including the absence of a local chart or incorrect intrinsic dimensionality, from estimation error and systematic bias caused by reference sampling, tangent-frame error, gain-axis misalignment, anchor displacement, and poor ratio conditioning. Simulations show that neighborhood size, curvature, sampling density, ambient dimension, and noise act through a small set of geometric quantities. The framework specifies when assignments to gain or novelty are identifiable, how they become biased, and which diagnostics reveal the relevant failure regime. By quantifying the nature rather than only the magnitude of neural state change, it provides a clear, readily interpretable framework for evaluating mechanisms of neural state transitions.

q-bio.NC

Neural Langevin Machine: a local asymmetric learning rule can be creative

Fixed points of recurrent neural networks can be leveraged to store and generate information. These fixed points are captured by the Boltzmann-Gibbs measure, which leads to neural Langevin dynamics that relax to those fixed points for generative learning of a real dataset. We call this type of generative model a neural Langevin machine, which derives an asymmetric and firing-rate-speed-adjusted learning rule requiring only local neural signals, thereby bearing biological relevance in terms of local predictive learning. An out-of-equilibrium regime of the generative process is revealed, together with a memorization-to-generalization transition with increasing training data size. The neuro-inspired machine can also realize a continuous exploration of the phase space for different kinds of generative images and can denoise a corrupted image as well.

q-bio.NC

A Mathematical Model of Motivated Emotional Mind - Cognitive Embodied System

This article presents a mathematical model of the Motivated Emotional Mind cognitive architecture developed for embodied intelligent systems. Such a system learns to maintain its homeostasis through a generalized form of reinforcement learning based on its internal motivations, termed motivated learning (ML). The principal contribution of this article is a rigorous formalization of the re-entrant loop integrating feedforward processing, lateral interactions, and feedback pathways, together with the representational selection mechanisms that govern adaptive system responses. The model specifies how ongoing exteroceptive and interoceptive signals, bodily-motivational context, and memory traces are bound into associative memory structures termed semblions, which compete for access to further processing and top-down reconstruction. The formalization encompasses secondary perception, representational competition, curiosity, procedural gaps, and action selection directed toward limiting allostatic violations. Within this framework, motivated learning is tailored to embodied systems whose dynamics are shaped by needs, affect, and the current regulatory state. Unlike standard reinforcement-learning models, the proposed approach incorporates need thresholds, goal generation and shifting goals, bodily state, resource constraints, and action uncertainty, thereby providing a more adequate account of response selection under regulatory pressure. Global affect functions as a central control signal, modulating the learning rate, representational valence, and the balance between exploration and exploitation. The model presented here is a step toward a more rigorous formalization of cognitive phenomena and may provide a basis for further theoretical analysis, computer simulation, and implementation in artificial-intelligence systems inspired by biological processes.

q-bio.NC