Statistical Field Theory and Neural Structures Dynamics V: Synthesis and extensions

TL;DR

This paper develops a comprehensive field-theoretic model for neuronal activity and connectivity, capturing hierarchical structures, stability, and transitions in collective neural states, advancing understanding of emergent brain assemblies.

Contribution

It synthesizes and extends previous models to include hierarchical substructures and interaction landscapes, offering a unified framework for neural collective dynamics.

Findings

Model captures cascading transitions between neural states

Defines activation classes for activity pattern compatibility

Describes formation of hierarchical neuronal assemblies

Abstract

We present a unified field-theoretic framework for the dynamics of activity and connectivity in interacting neuronal systems. Building upon previous works, where a field approach to activity--connectivity dynamics, formation of collective states and effective fields of collective states were successively introduced, the present paper synthesizes and extends these results toward a general description of multiple hierarchical collective structures. Starting with the dynamical system representing collective states in terms of connections, activity levels, and internal frequencies, we analyze its stability, emphasizing the possibility of transitions between configurations. Then, turning to the field formalism of collective states, we extend this framework to include substructures (subobjects) participating in larger assemblies while retaining intrinsic properties. We define activation classes describing compatible or independent activity patterns between objects and subobjects, and study stability conditions arising from their alignment or mismatch. The global system is described as the collection of landscapes of coexisting and interacting collective states, each characterized both by continuous (activity, frequency) and discrete (class) variables. A corresponding field formalism is developed, with an action functional incorporating both internal dynamics and interaction terms. This nonlinear field model captures cascading transitions between collective states and the formation of composite structures, providing a coherent theoretical basis for emergent neuronal assemblies and their mutual couplings.