Biological interpretation and limitations

Synaptic-pruning mechanisms are relevant to neurodevelopmental research, but the direction, anatomical setting, and developmental stage of an observed change are essential. Tang and colleagues link impaired mTOR-dependent autophagy to pruning deficits in their studied human tissue and animal models Tang. Adult PET findings concern an in-vivo synaptic-marker measure Matuskey, whereas longitudinal cortical-thickness measurements concern a different, macroscopic observable Zielinski. These observations must not be identified with one another or with a neural network's weight-mask density.

Cheung's pruning study provides the direct computational motivation CheungPruning. His polygenic-dissection preprint provides a proposed pathway-level comparison between ASD and ADHD CheungPolygenic; it is not an assumption or validation of the network theorems. His developmental pharmacology hypothesis CheungTiming motivates attention to timing, but the present mathematical model cannot establish its therapeutic claims. The separate pruning-cliff simulation is likewise computational motivation, not biological proof CheungCliff.

The labels “moderate” and “heavy” pruning are sufficient for the mathematical experiment. Calling the branches “Normal” and “ASD-like” is an illustrative convention, not a causal identification. In particular,

No drug, age-specific intervention, diagnosis, or individual developmental prediction follows from the proofs.

There are also mathematical limitations. The architecture is finite and feed-forward; training is not characterized by a proven optimization law; magnitude rankings are coordinate dependent; the Gaussian comparison surface is conditional; and stress certificates can be conservative when many activation gates lie near zero. The existing reported data do not supply all quantities required for the checkpoint-specific certificates.

The useful conclusion is narrower: phased training and pruning can be studied rigorously once connection counts, weight geometry, margins, noise covariance, and observation semantics are kept separate. Some proposed universal claims are false, while several conditional claims admit explicit proofs and verifiable sufficient conditions.