Quantum-like Modeling of Neuronal Networks Bridges the Knowledge Gap Between Brain Oscillations and Behavioral Patterns

The brain’s capacity for complex thought increasingly suggests processes beyond classical physics, and researchers are now exploring whether quantum-like principles underpin cognition. Andrei Khrennikov from Linnaeus University and Makiko Yamada from the Institute for Quantum Life Science, along with their colleagues, investigate how neuronal networks might generate phenomena analogous to quantum entanglement, a concept traditionally reserved for subatomic particles. This work represents a significant step towards bridging the gap between observed brain activity and quantum-like modelling, moving beyond purely physical explanations to consider how macroscopic neuronal structures process information. By applying a framework based on generalized probability and random field modelling, the team addresses the challenging problem of modelling “mental entanglement”, proposing a pathway towards potentially detecting such effects using established neuroimaging techniques like EEG and MEG.

Quantum Cognition, Brain Dynamics, and Quantum Models

This extensive collection of references details research exploring the potential for quantum-like models to explain brain function and cognitive processes. The work suggests that the brain may not strictly adhere to classical probability theory, and that concepts from quantum mechanics, such as superposition, entanglement, and contextuality, can offer valuable insights into how we think and perceive the world. Researchers are investigating how these quantum-like principles might underlie cognitive processes like decision-making, concept combination, and ambiguity resolution, as well as the complex dynamics of brain activity. The bibliography highlights investigations into brain dynamics and neural networks, specifically examining electrical activity measured through EEG and MEG. Scientists are exploring whether patterns of coherence, phase synchronization, and non-classical correlations within these signals suggest quantum-like behavior, employing advanced signal processing techniques like Fourier analysis and wavelet transforms to identify potential quantum features. This research also delves into the mathematical foundations connecting quantum information theory, probability theory, and cognitive modeling, seeking to establish a rigorous framework for understanding these relationships.

Mental Entanglement in Classical Neural Networks

Scientists are developing quantum-like modeling (QLM) to extend beyond physics into fields like cognition and decision-making. A key challenge remains bridging the gap between classical neuronal network function and quantum-like representations of mental states. This work addresses this challenge by focusing on generating quantum-like entanglement using classical networks, termed “mental entanglement. ” The study begins with an observational approach to entanglement, employing operator algebras to describe local observables and establish the tensor product structure within quantum-like states. To transition from oscillatory dynamics of neuronal networks to a quantum-like representation, scientists constructed a framework linking classical and quantum realizations of observables on these networks, treating entanglement not simply as a property of states, but as entanglement of observables themselves, applying this concept specifically to neuronal circuits.

Recognizing the importance of biological plausibility, the study also investigated the role of ephaptic coupling, the transmission of signals between neurons, in generating correlations between neuronal circuits. Crucially, the research extends beyond theoretical modeling, proposing concrete experimental tests for detecting mental entanglement using electroencephalogram (EEG) and magnetoencephalography (MEG) techniques. Scientists detail how these classical measurement techniques can be adapted to search for evidence of entanglement, including a comparative analysis with existing EEG-based approaches to functional connectivity in neuroscience. The team also defines quantitative measures of entanglement that can be used in these experimental tests, paving the way for future investigations into the nature of mental entanglement and its role in cognitive processes.

Neuronal Entanglement Detected Through Signal Covariance

Scientists have demonstrated a pathway to quantify mental entanglement, building upon a framework that links neuronal network oscillations to quantum-like modeling (QLM). This work establishes a method for detecting entanglement generated by classical neuronal networks, a crucial step toward understanding complex cognitive processes. The research centers on analyzing covariance structures between spatially separated brain areas, proposing that entanglement can be identified through statistical dependencies in neuronal signals. Experiments focused on establishing a framework for detecting entanglement using cross-covariance matrices, calculated from signals generated by neuronal networks.

The elements of these matrices represent the statistical relationship between random variables describing neuronal activity. By analyzing these matrices, scientists aim to determine whether the observed brain states represent entangled or separable systems, potentially achieving insights into the underlying mechanisms of cognition and memory formation. To facilitate experimental verification, the research details specific requirements for EEG/MEG implementation, prioritizing source-space analyses with leakage correction and detailing artifact handling, filtering, and spectral estimation. Statistical rigor is emphasized through the use of matched surrogates and corrections for multiple comparisons, ensuring the reproducibility of findings. The team highlights parallels between their QLM framework and established neurophysiological tools, such as functional connectivity analysis, offering a powerful approach to unraveling the complexities of brain function and cognition.

Neuronal Entanglement Modelled via Operator Algebras

Researchers have developed a theoretical framework linking classical neuronal network activity to quantum-like modeling (QLM), a mathematical approach increasingly used to understand cognition and decision-making. This work addresses a key challenge in the field, bridging the gap between observed brain oscillations and the patterns predicted by QLM. The team focused on generating entanglement, a quantum phenomenon, within classical brain networks, proposing a method to model ‘mental entanglement’ based on observable neuronal activity. The researchers achieved this by applying principles from operator algebras and standard state entanglement to spatially separated brain networks, effectively creating a mathematical description of how entanglement might arise from classical processes.

This formalism suggests that the brain may utilize quantum-like representations in cognitive processing, potentially explaining high-performance integrative processing observed in certain brain regions. While acknowledging the speculative nature of the work, the authors outline preliminary experimental designs using EEG and MEG techniques to indirectly detect signatures of mental entanglement by examining correlations in neural signals. The authors recognize limitations inherent in indirectly testing for entanglement and emphasize that these proposed experiments are intended to guide future empirical investigations. This research reinforces the broader hypothesis that the brain employs quantum-like representations, opening avenues for interdisciplinary collaboration integrating neurophysiological data, advanced mathematical tools, and philosophical inquiries into consciousness.