# SDIL novelty audit This file records the closest-prior-art boundary as of 2026-07-22. It is a claim constraint, not a literature-survey completeness claim. New evidence may narrow the allowed claims further. ## Exact overlap with learned node-perturbation feedback Lansdell, Prakash, and Kording, [Learning to solve the credit assignment problem](https://arxiv.org/abs/1906.00889) (ICLR 2020), already train synthetic feedback matrices from node-perturbation estimates. Their direct-feedback form maps the output error to every hidden layer and was evaluated in a CIFAR convolutional network. Their public implementation is [`benlansdell/synthfeedback`](https://github.com/benlansdell/synthfeedback). With no ordinary apical traffic and `P=0`, SDIL reduces exactly to this idea (up to sign and tensor conventions): ```text c = output error r_l = A_l c q_l = node-perturbation estimate of -dL/dh_l A_l <- A_l + eta_A (q_l - A_l c) c^T ``` Independent noise at multiple hidden layers during a shared noisy forward pass is also present in the earlier method. SDIL's antithetic Rademacher directions, vectorized direction batching, and explicit cost accounting are useful estimator/implementation choices, but are not the core algorithmic novelty. The existing no-traffic depth results therefore establish the strength of a learned-node-perturbation backbone; by themselves they do not establish a Harnett-specific contribution. The closest exact baseline is consequently the same implementation with `traffic_mode=none`, `learn_P=0`, and `use_residual=0`. It should be named **learned direct feedback by node perturbation (Lansdell et al.)**, not SDIL, in comparisons intended to isolate novelty. ## Overlap with dendritic credit assignment Apical/basal segregation, neuron-specific dendritic teaching signals, and local three-factor or burst-dependent plasticity substantially predate SDIL. In addition, Greedy et al., [Cell-type-specific cortical feedback coordinates hierarchical credit assignment](https://www.biorxiv.org/content/10.64898/2026.06.16.732595v1) (BurstCCN, 2026), explicitly connects a scalable dendritic credit-assignment model to the Francioni/Harnett data. BurstCCN already: - models deviations from balanced dendritic feedback as neuron-specific error signals; - reproduces the opposite residual signs of the P+ and P- BCI populations and their disruption by NDNF activation; - trains convolutional models on CIFAR-10 and ImageNet with plastic feedback; and - provides an [author implementation](https://github.com/neuralml/BurstCCN-journal). Thus neither "apical dendrites carry vector errors", "the model reproduces Harnett Fig. 5", nor "a dendritic local rule scales to ImageNet" is a sufficient novelty claim. ## Candidate SDIL contribution The candidate contribution is the combination ```text a_l = A_l c + t_l mixed apical traffic P_l(h_l) ~= E[t_l | h_l, neutral] per-cell neutral predictor r_l = a_l - P_l(h_l) somato-dendritic innovation Delta W_l proportional to r_l * eligibility_l ``` where the subtractive per-cell innovation, rather than raw apical activity, is the teaching variable. The feedback vectorizer `A_l` may be learned using Lansdell-style causal perturbations; that component is inherited, not claimed as new. The new empirical question is whether residualization is load-bearing when teaching signals share an apical channel with ordinary somatic/contextual feedback. Neutral-period fitting is an algorithmic adaptation rather than a direct statement of the Harnett paper. It addresses an identifiability problem: task-period regression can subtract the predictable 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. ## Claims currently allowed - The inherited learned-feedback backbone preserves performance with depth better than fixed DFA in the audited flattened-CIFAR MLP setting. - 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. - 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. - Direct node perturbation solves the frozen useful-depth task while the amortized context vectorizer does not. This identifies feedback amortization as the current engineering bottleneck; it is not a novelty claim and the direct estimator's 68.4x work precludes presenting it as the scalable algorithm. ## Claims currently forbidden - Node perturbation training of `A_l` is novel. - Simultaneously perturbing all hidden layers is novel. - The no-traffic scaling panel demonstrates the necessity of somato-dendritic residuals. - SDIL is the first dendritic method to scale to deep vision tasks. - Reproducing the already-published Harnett/BurstCCN qualitative signatures is an oral-level biological contribution. ## Evidence that can restore a strong paper 1. Show across independently generated, endogenous traffic families that residualization beats 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. 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. 5. For a scaling contribution, target standard architectures and a simplicity/cost regime not already occupied by BurstCCN, rather than treating ImageNet alone as novelty.