1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
|
# 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.
- Controlled pilots show that per-cell residualization can protect learning from soma-coupled and
endogenous top-down traffic; the latter remains a validation-only, single-seed result until C1 is
completed.
- Simultaneous batched perturbations make the inherited calibration practical in this codebase;
a hardware-independent query/FLOP advantage is not yet established.
## 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.
|
