Network Geometry of Neural Processing Components for Interpretable AI in Sparse Neuromorphic Dendritic Circuits

Neuromorphic AI increasingly draws on sparse dendritic computation, yet a network-science framework linking sub-neuronal circuit organization to function remains underdeveloped. Here we introduce the neural processing component (NPC) paradigm, representing synapses, dendrites, somata and axons as network elements whose sparse topology shapes computation. We implement this paradigm in a dendritic reservoir-based network for trajectory detection, demonstrating in device-informed simulations that real-time motion trajectories can be detected through fixed NPC design rather than training. Using hyperbolic network geometry, we reveal that NPC neuromorphic circuits with dendrites possess latent hierarchical and modular organization that is not visible from circuit diagrams alone. We further show that this geometric representation identifies vulnerable modules and critical routing nodes that can be selectively redesigned, reducing detection error under router-node failure by 50.9%. These results establish NPC network geometry as a method for circuit-level interpretability androbustness-oriented design of sparse neuromorphic AI.