ArticleEntropy (Basel, Switzerland)2024
Neural Activity in Quarks Language: Lattice Field Theory for a Network of Real Neurons.
Article in Entropy (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed.
- Can Information and Entropic Dynamics Bridge the Gap Between Biology and the Physical Sciences?Entropy (Basel, Switzerland) · 2026Article
- Quantum-like architecture of the social atom: mental marker coherence and the triple-resonance mechanism of cognitive activation.Frontiers in human neuroscience · 2026Article
- SAGEFusionNet: An Auxiliary Supervised Graph Neural Network for Brain Age Prediction as a Neurodegenerative Biomarker.Brain sciences · 2025Article
- Inhibition of return emerges with non-predictive spatial cueing of the stop-signal.Frontiers in human neuroscience · 2025Article
- Quantum-like representation of neuronal networks' activity: modeling "mental entanglement".Frontiers in human neuroscience · 2025Article
- Lattice physics approaches for neural networks.iScience · 2024Review
- Entropy of Neuronal Spike Patterns.Entropy (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Brain-computer interfaces have seen extraordinary surges in developments in recent years, and a significant discrepancy now exists between the abundance of available data and the limited headway made in achieving a unified theoretical framework. This discrepancy becomes particularly pronounced when examining the collective neural activity at the micro and meso scale, where a coherent formalization that adequately describes neural interactions is still lacking. Here, we introduce a mathematical framework to analyze systems of natural neurons and interpret the related empirical observations in terms of lattice field theory, an established paradigm from theoretical particle physics and statistical mechanics. Our methods are tailored to interpret data from chronic neural interfaces, especially spike rasters from measurements of single neuron activity, and generalize the maximum entropy model for neural networks so that the time evolution of the system is also taken into account. This is obtained by bridging particle physics and neuroscience, paving the way for particle physics-inspired models of the neocortex.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.