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# Somato-Dendritic Innovation Learning (SDIL)
SDIL is a non-backpropagation learning project motivated by the neuron-specific
somato-dendritic residuals reported by Harnett and colleagues. Its central
algorithmic hypothesis is narrow: a mixed apical compartment should teach from
the component that is unexpected given the same neuron's ordinary somatic
state, rather than from raw apical activity.
For hidden population `l`, the implemented rule is
```text
a_l = A_l c + ordinary apical traffic
a_hat_l = P_l(h_l), fitted during neutral periods
r_l = a_l - a_hat_l
Delta W_l = eta (r_l * local postsynaptic gain) h_(l-1)^T.
```
`A_l` is calibrated from antithetic node perturbations without reverse-mode
differentiation or weight transport. That learned-feedback mechanism is
inherited from Lansdell, Prakash, and Kording (2020); it is not claimed as
novel. The candidate contribution is the neutral-period, per-cell innovation
operation under mixed apical traffic, together with its theory, causal audit,
and scaling behavior. See `NOVELTY.md` for the exact prior-art boundary.
## Current audited evidence
- On flattened CIFAR-10, SDIL loses only `0.214 +/- 0.349` accuracy points from
hidden depth 5 to 60, while DFA's early-layer teaching alignment falls from
`0.514` to `0.047`. This is depth preservation on a depth-flat task, not yet
evidence that added depth is useful.
- With strong soma-predictable apical traffic, raw and norm-matched raw
learning fall to `10.38%` and `10.31%`, while innovation learning retains
`97.35%`. The paired innovation gain over norm-matched raw is
`87.038 +/- 0.607` points across five seeds.
- Direct, unamortized node perturbation passes the controlled useful-depth
diagnosis, but costs `68.4x` ordinary forward-equivalent work. The learned
apical vectorizer, rather than the local eligibility rule, is the current
useful-depth bottleneck.
- A low-query K1/every-4 calibration protocol retains `112.9%` of the K16 gain
over DFA with 16x fewer logical queries, 11x less calibration work, and 5.3x
less total forward-equivalent work in the frozen MLP protocol.
- The frozen standard-ResNet funnel passed its BP reference and short screen:
BP reached `91.62%`, and short-run channel-gated SDIL reached `41.98%`
versus tuned DFA's `37.16%`. The full A3 run then failed decisively: SDIL
became nonfinite at epoch 90 and ended at chance, while DFA remained finite
at `33.06%`. A4 was therefore not opened and no confirmation test seed was
touched.
- Native author-code fidelity is complete. BurstCCN reaches `80.10%` at its
validation-selected epoch versus published `82.97 +/- 0.21%`; Dual Prop
reaches `92.46%` versus published `92.41 +/- 0.07%`. Their audited walls are
`15451.3 s` and `23119.8 s`, respectively, and they are not presented as
matched-compute points.
The broad endogenous-traffic gate and the Harnett desired-velocity/online-control
screen failed. Those results are retained and explicitly constrain the paper:
SDIL does not currently explain the reported error-derivative signature, and
the residual mechanism is supported for traffic predictable from the chosen
somatic statistic rather than arbitrary top-down context. `ROADMAP.md` and
`ORAL_B.md` contain the frozen gates and complete negative branches.
## Publication-facing artifacts
- `RESULTS.md`: audited positive and negative results;
- `THEORY.md`: estimator variance, descent conditions, conditional innovation,
timescales, and hardware-independent cost;
- `BASELINES.md`: matched and native-author baseline ledger;
- `ROADMAP.md`: accept/oral evidence gates and their state;
- `ORAL_A.md`: frozen standard CIFAR ResNet funnel;
- `REVIEW_SCORECARD.md`: adversarial ICLR-style score trajectory;
- `results/figs/`: deterministic PDF/PNG main figures, captions, and a source
hash manifest.
The three current main figures show the local-method Pareto frontier, credit
assignment versus depth, and the load-bearing innovation ablation. Every final
cell has exactly seeds 0--4 and clean git provenance; the strict renderer
refuses missing or protocol-mixed cells.
## Verification
The lab environment currently used for audited CPU checks is
`/home/yurenh2/miniconda3/envs/ep_pascal`. Run:
```bash
experiments/finalize_claims.sh
```
This regenerates the figures and manifest, enforces the frozen Pareto/scaling
gates, verifies baseline and protocol mechanics, checks the local-rule smoke
tests, evaluates the theory identities, and rebuilds the audited tables.
The convolutional infrastructure can be checked independently:
```bash
/home/yurenh2/miniconda3/envs/ep_pascal/bin/python3 experiments/cifar_image_smoke.py
/home/yurenh2/miniconda3/envs/ep_pascal/bin/python3 experiments/conv_local_smoke.py
```
These checks cover deterministic CIFAR splitting/augmentation, the standard
CIFAR `6n+2` ResNet topology, exact local convolution and BatchNorm
eligibilities, the BatchNorm-coupled perturbation objective, predictor fitting,
translation-shared feedback, and parameter/cost accounting.
`experiments/finalize_accept.sh` additionally requires strict imports of the
frozen BurstCCN and Dual Propagation author-code runs. Both records now pass;
the accept-bar mechanical audit is green. The standard ResNet branch stopped
at its failed A3 validation gate, and the untouched A4 test panel remains
sealed.
## Result discipline
Every publication-facing run records the source revision, tracked-tree state,
full arguments, split identity, seed, evaluation count, wall time, query/MAC
accounting, and final finiteness. Development, validation, and untouched
confirmation results are never pooled. Failed gates close their branch instead
of triggering seed deletion or post-hoc threshold changes.
The current strict reviewer estimate is `5/10` (borderline reject, confidence
`4/5`): the mechanism and controlled depth-preservation results are strong,
but the frozen standard-useful-scale attempt failed. The score changes only
after an audited frozen stage, not after a pilot or a presentation improvement.
|
