Functional Building Blocks for Neural Computation

Adaptive sensorimotor behavior relies on neural systems that reuse stable computational functions across tasks and contexts, rather than constructing control structures de novo for each behavioral demand. Biological nervous systems exhibit this organization through partially genetically specified architectures composed of reusable microcircuits that implement core computations and are refined through development and experience.With a focus on sensorimotor control, we propose a computational-level framework in which neural systems are constructed from abstract functional building blocks corresponding to reusable components such as state estimation, prediction, coordinate transformation, error mapping, memory-based control, and low-dimensional synergies. These components are task-agnostic by design yet adaptable, supporting reuse and recombination across behaviors while preserving stable computational roles.Making such functional components explicit provides a principled account of inductive bias, learning efficiency, transfer, and robustness in adaptive systems. From this perspective, persistent limitations in transfer and continual learning in contemporary artificial neural networks can be understood as consequences of treating parameters rather than computational functions as the primary units of learning. The framework therefore motivates a shift from parameter-centric optimization toward learning and recombination of reusable computational functions.