Neural activity in quarks language: Lattice Field Theory for a network of real neurons

Brain-computer interfaces have undergone extraordinary development in recent years, and a significant discrepancy now exists between the abundance of available data and the limited headway made in achieving a general theoretical framework. This chasm becomes particularly pronounced when examining the collective neural activity at the micro and meso scale, where a cohesive theoretical formalization that adequately describes neural interactions is still lacking. Here, we introduce a general mathematical framework to study 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 formalism is tailored to data from chronic neural interfaces, especially measurements of single neurons activity, and generalizes the maximum entropy model for neural networks so that also the time evolution of the system is taken into account. This is obtained by bridging particle physics and neuroscience, paving the way to particle physics-inspired models of neocortex.