Neural Activity in Quarks Language: Lattice Field Theory for a Network of Real Neurons

Area of Science:

• Computational Neuroscience
• Theoretical Physics
• Biophysics

Background:

• Rapid advancements in brain-computer interfaces (BCIs) have generated vast datasets.
• A lack of unified theoretical frameworks hinders the interpretation of neural data, especially at micro and meso scales.
• Existing models struggle to formalize neural interactions and temporal dynamics.

Purpose of the Study:

• To introduce a novel mathematical framework for analyzing natural neural systems.
• To interpret empirical neural data using principles from lattice field theory.
• To bridge the gap between particle physics and neuroscience for neocortical modeling.

Main Methods:

• Application of lattice field theory, a paradigm from theoretical particle physics and statistical mechanics.
• Tailoring methods to analyze chronic neural interface data, including spike rasters.
• Generalizing the maximum entropy model to incorporate system time evolution.

Main Results:

• A formal mathematical framework for analyzing collective neural activity is established.
• The framework successfully interprets empirical observations from neural recordings.
• Integration of temporal dynamics into neural network models is achieved.

Conclusions:

• The study presents a novel approach to understanding neural interactions by leveraging particle physics concepts.
• This framework offers a pathway towards developing particle physics-inspired models of the neocortex.
• It facilitates a more coherent interpretation of complex neural data from BCIs.