<p>Neural networks exhibit complex dynamical behaviors, encompassing multistability, neural firing patterns, and bifurcation phenomena. Investigating extreme multistability is crucial for advancing our understanding of the human brain and its associated disorders. To elucidate the underlying mechanisms and develop effective control strategies for neural models, this study employs a non-autonomous memristive Hopfield neural network (MHNN) with two neurons. The proposed model effectively demonstrates multistability and the coexistence of diverse spiking behaviors. To provide deeper insights into these phenomena, an innovative analytical approach is adopted, complemented by numerical validation. A discrete mapping structure is formulated, and the line equilibrium points of the proposed model are systematically analyzed. The coexistence of bifurcation behaviors is explored through numerical bifurcation diagrams and a semi-analytical bifurcation tree. Furthermore, the presence of infinitely many coexisting attractors and unstable periodic orbits within chaotic regions is revealed through initial condition bifurcation and local attraction basin analysis. The system exhibits a wide range of firing dynamics, including stable spiking patterns and transitions from unstable to stable firing states, as demonstrated through time series analysis. To achieve enhanced control over the system's dynamics, a rotational control strategy is introduced, facilitating the generation of an infinite number of coexisting attractors in any direction within the two-dimensional space. Finally, the theoretical findings are substantiated through the empirical observation of coexisting periodic orbits using a Field-Programmable Gate Array (FPGA).</p>