neural-mechanism-research

Neural Mechanism Research

Explain what the computation actually makes possible, how learning discovers it, and how firmly the explanation is established. The reader should be able to reconstruct a small example and predict what would change if the design changed.

This is a neural-computation variant of deep-architecture-research combined with the reporting principles of understanding-first-report. The essential guidance is self-contained here; neither skill has to be loaded again. Use their deeper references only if the task also requires a substantial source-code audit or a separate reporting review.

Start with the real question

Quote the relevant original user wording before substantive analysis. For a long prompt, preserve the central questions and mark omissions. Reconstruct the underlying puzzle, including the user's proposed explanation, rather than replacing it with a generic survey.

Map the few causal relationships that matter. For example:

architecture / parameterization
          │
          ├── forward: what functions become possible?
          ├── geometry: what changes, mixes, or becomes separable?
          └── backward: which errors change which parameters?
                         │
data / loss / optimizer ─┘
                         ▼
              learned behavior and cost
                         │
              evidence and alternatives

If the user has provided clear scope and requested research, state the interpretation and proceed. Clarify only material ambiguity. Do not introduce a repeated confirmation gate or reopen accepted scope.

Treat meaning as a testable explanation

For the main mechanism, connect these perspectives rather than filling six disconnected sections:

• Forward computation: concrete inputs, shapes, parameters, intermediate states, outputs, and what the next component can do with them.
• Geometry and information: projections, rank, basis changes, nonlinear boundaries, mixing, routing, compression, invariances, and the distinction between coordinate count and independent information.
• Backward computation: start at the actual objective, follow the chain rule to the relevant parameters, and explain what an update rewards or suppresses.
• Learning dynamics: initialization, symmetry breaking, feature learning, gradient conflicts, saturation, conditioning, optimization noise, and dependence on the parameterization.
• Physical or dynamical interpretations: use energy, attractors, diffusion, discrete dynamics, or other physical language only where the equations justify the mapping. State which assumptions make it valid and where it breaks.
• Engineering and history: distinguish a useful inductive bias from an empirically selected size, implementation convenience, resource tradeoff, or historical convention.