Credit Assignment Under Constraint: Why Learning Must Be Local in Neural Systems

Credit assignment in neural systems is commonly framed as the problem of computing or approximating gradients of a global objective. Under realistic constraints of partial observability, noise, delay, and high dimensionality, global credit assignment is computationally demanding and structurally ill-posed. Locality, modularity, and hierarchy can therefore be interpreted as structural responses that improve identifiability under these constraints. Biological learning does not solve the global inverse problem; it avoids it by restricting credit to locally identifiable subsystems.