docs: correct SDIL novelty boundary

1 files changed, 13 insertions, 11 deletions

diff --git a/sdil/core.py b/sdil/core.py
index 37c7165..cece194 100644
--- a/sdil/core.py
+++ b/sdil/core.py
@@ -4,11 +4,11 @@ SDIL -- Somato-Dendritic Innovation Learning.
 A non-backprop learning rule inspired by Harnett et al. 2026 (Nature),
 "Vectorized instructive signals in cortical dendrites".
 
-The load-bearing idea, faithful to the paper, is NOT "apical dendrites carry
-error". That is old (Guerguiev/Sacramento/Payeur/burst credit assignment). The
-new algorithmic object is the *somato-dendritic residual*: the teaching signal
-is the apical feedback MINUS the part of it that is predictable from the cell's
-own somatic activity. Only the innovation drives plasticity.
+The Harnett-specific object studied here is the *somato-dendritic residual*:
+the teaching signal is the apical feedback MINUS the part of it that is
+predictable from the cell's own somatic activity. Only the innovation drives
+plasticity. Apical credit signals and learned feedback from node perturbations
+both have substantial prior art; see NOVELTY.md for the exact claim boundary.
 
 For hidden layer l:
     u_l = W_l h_{l-1},  h_l = phi(u_l)              (somatic / basal forward)
@@ -21,10 +21,11 @@ Three learning rules, all local (no weight transport, no reverse-mode graph):
     dP_l = eta_P * (a_l - P_l h_l) h_l^T            (slow; only on neutral periods)  eta_P << eta
     dA_l = eta_A * (q_l - r_l) c_l^T                (on perturbation trials; q_l = causal node-pert estimate)
 
-q_l is an amortized node-perturbation estimate of the causal descent direction
+q_l is a node-perturbation estimate of the causal descent direction
 -grad_{h_l} L, obtained by occasionally perturbing h_l and watching the loss.
-Learning A_l this way (rather than fixing it random) is what keeps SDIL from
-degenerating into DFA.
+Learning A_l this way follows the learned synthetic-feedback approach of
+Lansdell, Prakash & Kording (ICLR 2020). Relative to fixed DFA it supplies an
+adaptive feedback backbone; it is not itself claimed as an SDIL novelty.
 
 This module deliberately does NOT use autograd for learning. Autograd is used
 ONLY inside probes.py to MEASURE the true gradient for alignment diagnostics.
@@ -92,9 +93,10 @@ class SDILNet:
     biologically unproblematic; only hidden layers use the SDIL innovation.
 
     feedback signal c: by default the output error e = softmax(logits) - onehot,
-    broadcast to every hidden layer (dim = n_classes). With A_l learned this is
-    "amortized node perturbation through apical dendrites"; with A_l fixed random
-    it reduces to DFA (used as an ablation).
+    broadcast to every hidden layer (dim = n_classes). With P=0 and no traffic,
+    learning A_l from node perturbations is the direct learned-feedback method
+    of Lansdell et al.; with A_l fixed random it reduces to DFA. SDIL's candidate
+    addition is the per-cell innovation under mixed apical traffic.
     """
 
     def __init__(self, sizes, act="tanh", device="cpu", seed=0,