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| -rw-r--r-- | NOVELTY.md | 16 |
1 files changed, 14 insertions, 2 deletions
@@ -72,6 +72,14 @@ paper. It addresses an identifiability problem: task-period regression can subtr part of `A_l c` whenever output error correlates with somatic state, while neutral-period regression identifies the traffic relationship without observing the teaching component. +The formal justification is an application of the standard conditional-expectation projection +identity, not a new mathematical theorem. Among all predictors measurable from the chosen somatic +statistic, the neutral conditional mean uniquely minimizes residual nuisance power and leaves an +innovation orthogonal to every such predictor. The implemented per-cell affine model realizes the +restricted projection onto `{1,h_i}`. Positive norm matching cannot change the raw signal's cosine +direction at a fixed state, so the matched-raw ablation specifically distinguishes subtraction from +gain control. `THEORY.md` states the assumptions and the executable finite-sample identities. + ## Claims currently allowed - The inherited learned-feedback backbone preserves performance with depth better than fixed DFA @@ -79,6 +87,9 @@ identifies the traffic relationship without observing the teaching component. - Frozen controls show that per-cell residualization protects learning from soma-coupled traffic. It mitigates but does not fully remove endogenous top-down traffic, so arbitrary contextual innovation is not an allowed teaching-signal claim. +- Neutral residualization is the minimum-power nuisance removal within the predictor's somatic + function class. For the implemented diagonal affine class this is a narrow, testable statement, + not a claim of optimal population-level denoising. - Simultaneous batched perturbations have a frozen hardware-independent advantage: K1/every4 retains 112.9% of the K16/every4 gain over DFA with 16x fewer logical loss queries, 11x less calibration work, and 5.3x less total forward-equivalent work on CIFAR-10 d20/w64. @@ -102,8 +113,9 @@ identifies the traffic relationship without observing the teaching component. raw and norm-matched raw feedback, while the no-traffic methods coincide. 2. Compare directly with Lansdell-style learned feedback and BurstCCN under validation-selected, disclosed compute/query budgets. -3. Prove the conditional-bias/SNR benefit of neutral residualization and test its predicted failure - when traffic depends on the teaching signal after conditioning on soma. +3. The conditional-projection/SNR result and task-period absorption prediction are now proved and + executable. A broader paper would still benefit from a separately frozen failure test where + traffic depends on the teaching signal after conditioning on the available soma statistic. 4. For a biological contribution, make and test a prediction not used by Harnett or BurstCCN. Raw event-level data and analysis code require author coordination; the paper exposes only plotted source data and states that full data/code are available on request. |