Neural activity in quarks language

Methods for recording brain activity surged extraordinary development in recent years, and a significant discrepancy now exists between the abundance of available data and the limited headway in achieving a shared theoretical framework. This chasm is evident at the micro and meso scale, where a cohesive formalization describing interactions of large neural populations is still lacking. Here, we introduce a general mathematical formalism to interpret empirical observations from systems of interacting neurons in terms of lattice field theory, an established paradigm from theoretical particle physics and statistical mechanics. Bridging particle physics and neurophysiology, we derive an energy functional that generalizes the maximum entropy model for neural networks and includes a traditional approach such as the Principal Component Analysis as a sub-case. This ascribes otherwise unrelated results to universal physical principles, paving the way to particle physics-inspired models of the brain.