We introduce Spatial Context Networks (SCN), a novelneural architecture that treats neurons as geometric entities in a learned semantic space. Unlike traditional neural networks that rely on weighted summations, SCN employs distance-based activation functions where each neuron operates as a point-mass with a learnable centroidin d-dimensional space. The architecture implementsthree key innovations: (1) geometric activation functionsbased on Euclidean distance, (2) semantic routing thatselectively activates neurons based on spatial proximity,and (3) connection density weighting with adaptive scaling. Our experiments demonstrate stable training dynamics, interpretable neuron specialization, and efficientsparse activation patterns. Notably, all experiments wereconducted on consumer-grade hardware (gaming laptop),demonstrating the accessibility and computational efficiency of this approach. The architecture achieves 91%network efficiency with only 32 hidden neurons whilemaintaining numerical stability through principled geometric constraints.