Memristive Spatial Computation and Neuromorphic Dynamics: A Unified Framework from Non-Local Readout to Continuous Field Generators Extended Edition: Memristive Flux-Induced Spatial Computation in Neural Terminals

We propose and develop a unified spatial computation framework (MSCM 3.0) that generalizes the attention mechanism of modern deep learning into a nonlocal, non-Markovian, continuous-field dynamical system. In this framework, compu tation is jointly accomplished by three core structures: (i) a fast state field (in stantaneous computation), (ii) a memristive field (history inscription), and (iii) a morphological modal field (structural/trajectory modulation). We first construct the discrete model Morpho-NMRA and prove that the intro duction of memristive writing and morphological modes yields measurably supe rior performance on path-dependent tasks relative to standard attention (≈ 0.94 vs. 0.32 accuracy). We then lift the model to a continuous spatial field-theoretic form, obtaining a system of partial differential equations driven by structured gen erators. Two- and three-dimensional numerical simulations demonstrate that the system spontaneously produces stable spatial structures (vortices, spirals, bifurca tion ridges), which participate in the computation loop via non-local readout. Building on this, we prove Theorem 8 (Memristive Spatial Computa tion Theorem), which establishes that neural terminal morphodynamics is a non Markovian process induced by a covariant memristive planning flux. We derive the exact dynamical equations coupling spacetime geometry to neural terminal mor phology, and present four strict falsifiable experimental criteria. This extended framework unifies fundamental physics and neuroscience, demonstrating that com putation emerges from the dynamic reconfiguration of memristive spacetime itself. Keywords: spatial computation, memristive field, non-local attention, mor phological dynamics, path-dependent learning, neural terminal morphodynamics, covariant nonlocal gravity, memristive planning flux