Fractional Gr\"onwall--Wendroff Inequalities for Implicit Systems with Distributed Memory

TL;DR

This paper develops new fractional integral inequalities for implicit systems with distributed memory, providing foundational estimates, existence, stability results, and applications to neural models with delays and fractional dynamics.

Contribution

It introduces novel Gr"onwall--Wendroff type inequalities for multivariate fractional systems with implicit dependencies, advancing analytical tools for complex memory-dependent models.

Findings

Established a priori estimates for fractional systems with delays.

Proved existence and uniqueness theorems using fixed-point methods.

Applied results to neural models, deriving stability and bifurcation conditions.

Abstract

This work establishes a comprehensive analytical framework for studying implicit fractional differential systems with distributed memory and time delays. We develop novel fractional integral inequalities of Gr\"onwall--Wendroff type that are specifically adapted to handle multivariate functions with singular kernels and implicit dependencies. These inequalities provide essential a priori estimates for analyzing complex memory-dependent systems. Building upon these results, we prove general existence and uniqueness theorems for implicit fractional differential equations using fixed-point theory in appropriately weighted Banach spaces. Furthermore, we establish Ulam--Hyers stability criteria, demonstrating that small perturbations in the governing equations lead to proportionally small deviations in solutions. The theoretical advances are applied to the fractional FitzHugh--Nagumo model with delay (FHN-$\alpha$-$\tau$), a neurodynamical system exhibiting both subdiffusive memory and discrete time delays. Our analysis yields rigorous conditions for the existence of limit cycles corresponding to action potentials, along with asymptotic stability criteria. The results reveal how fractional order $\alpha$ and delay $\tau$ jointly modulate neural excitability thresholds and dynamic regime transitions. This work bridges fundamental fractional calculus theory with applications in mathematical neuroscience, providing analytical tools for systems where present evolution depends non-trivially on historical states and intrinsic fractional rates of change.