neuroscience-comparator-ladder-and-per-unit-predictionslisted

# Two-sided comparator ladder, the negative-control representation panel, and per-unit predictions Three deliverables a computational-neuroscience claim must carry; plan them before fitting. A two-sided comparator ladder. Horizontally, a bank of named alternative methods spanning the families in use (supervised, correlation based, variance based, single-modality). Vertically, variants of your own model that each delete one information source your thesis says is necessary - structure or connectivity, task optimisation, temporal order, one modality - plus a granularity sweep that coarsens the entity taxonomy (fine type, family, broad class) until performance collapses. Same metrics, same split, every rung. The paired representation panel. Show the low-dimensional projection twice on identical axes and colouring: once under the untreated, shuffled or degraded condition and once under the treated one, quantified with the same gap metric in both. The deliberately poor control panel is a required deliverable, not something the good panel excuses. Per-unit publication. Give the model's per-unit quantity for the whole population as a ranked table or figure, partitioned into units where an independent measurement exists (report agreement as k of n) and units where the model issues an untested prediction, labelled as novel predictions, with the coverage fraction stated. Pair it with a mechanism established by intervention - property P of upstream unit A sets property Q of downstrea