hypneum-lab · electron-rare · May 30, 2026
diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -24,7 +24,7 @@ All notable changes to `nerve-wml` follow [Keep a Changelog](https://keepachange
 - **Plan A.3** — Hyperparameter wiring (3 tasks, n_steps=32, lr=0.05, n_osc ∈ {32,64})
 - **Plan A.4** — AKOrN comparison (3 tasks, minimal vs full topology, separation metrics)
 - **Plan B** — Paper revisions (8 tasks, §Method, §Results, new citations, abstract figures)
-- **Plan C** — Biological substrate (11 tasks, BioFieldWML Phase 1–2, CL1 sleep-every validation, Palacios SNN-PC scheduler)
+- **Plan C** — Biological substrate (11 tasks, BioFieldWML Phase 1–2, CL1 sleep-every validation, Lee SNN-PC scheduler)
 
 ### Reinforcement campaigns (9 confirmed, 1 audit)
 
@@ -69,7 +69,7 @@ All notable changes to `nerve-wml` follow [Keep a Changelog](https://keepachange
 ### Biological substrate integration
 
 - **BioFieldWML Phase 1**: MockBioCultureClient scheduler with sleep-every ≥ 1 validation, episode-bound Add(layer) accumulator.
-- **BioFieldWML Phase 2**: Palacios SNN-PC (predictive coding via spiking neurons) integrated as optional substitution-compatible estimator for Kuramoto-style cell populations.
+- **BioFieldWML Phase 2**: Lee SNN-PC (Lee et al. 2024, Frontiers Comp. Neurosci., DOI 10.3389/fncom.2024.1338280 — predictive coding via spiking neurons) integrated as optional substitution-compatible estimator for Kuramoto-style cell populations.
 - **Testing**: 458 fast tests + 35+ slow statistical tests covering unit (L1), info-theoretic (L2), integration (L3), golden (L4) strata. No real CL1/FinalSpark API key used (MockBioCultureClient only).
 
 ### Citations added to refs.bib

diff --git a/README.md b/README.md
@@ -123,7 +123,7 @@ Protocol.
 | §Information Tx Test (6) PRH refinement | `papers/paper1/main.tex` | New "Global vs Local alignment" paragraph engages `platoscave2026` + `aristotelianprh2026`; nerve-wml's mutual-kNN measurement claimed as *local* (null-calibrated 18.8× random), not global |
 | GTM re-grounding | `papers/paper1/main.tex` | Replaces sole reliance on Bastos & Friston 2012 with Bastos 2020 predictive routing + Friston 2025 + Ruffini 2025 laminar Comparator |
 | HNN positioning (closes #13) | `papers/paper1/main.tex` "Surrogate-gradient SNN" thread | Cites Liu et al. 2024 (NSR) and positions nerve-wml in the *weak-coupling* corner of the HNN taxonomy |
-| BioFieldWML plan versioned | `docs/superpowers/plans/2026-05-19-bio-substrate-wml.md` | Plan for a 4th conformant substrate alongside MLP / LIF / Transformer; 5 (b)-classified mechanisms encapsulated inside `step()` (Tomé STDP, Pignatelli IE, Palacios SNN-PC, Bellitto WSCL, Tucker-Friston E/I); cross-refs dream-of-kiki `biophysical-stratification` spec |
+| BioFieldWML plan versioned | `docs/superpowers/plans/2026-05-19-bio-substrate-wml.md` | Plan for a 4th conformant substrate alongside MLP / LIF / Transformer; 5 (b)-classified mechanisms encapsulated inside `step()` (Tomé STDP, Pignatelli IE, Lee SNN-PC, Bellitto WSCL, Tucker-Friston E/I); cross-refs dream-of-kiki `biophysical-stratification` spec |
 
 Headline measurements and claims A/B remain unchanged — this update is
 documentation and planning only, not new experimental findings.

diff --git a/docs/substrate-paradigms.md b/docs/substrate-paradigms.md
@@ -31,7 +31,7 @@ substrate.
 | **In-silico MLP baseline** | `track_w/mlp_wml.py::MlpWML` | Dense linear + nonlinearity | Standard backprop on `codebook` + readout heads | Track W foundation |
 | **In-silico LIF (rate-coded)** | `track_w/lif_wml.py::LifWML` | Leaky integrate-and-fire over `T` micro-ticks, rate decode | Surrogate-gradient backprop | Track W foundation |
 | **In-silico Transformer** | `track_w/transformer_wml.py::TransformerWML` | Multi-head attention over codebook history | Standard backprop | Track W foundation |
-| **BioField PC-VMP** | `track_w/bio_field_wml.py::BioFieldWML` | Spiking field with `μ/σ` belief + `PRED γ` / `ERROR θ` Neuroletter roles | Predictive coding via variational message passing (Palacios SNN-PC, arXiv:2409.05386) | nerve-wml Phase 2 (PR #24, 2026-05-20) |
+| **BioField PC-VMP** | `track_w/bio_field_wml.py::BioFieldWML` | Spiking field with `μ/σ` belief + `PRED γ` / `ERROR θ` Neuroletter roles | Predictive coding via variational message passing (Lee et al. 2024 SNN-PC, DOI 10.3389/fncom.2024.1338280) | nerve-wml Phase 2 (PR #24, 2026-05-20) |
 | **Wet biology** | `track_w/bio_wml.py::BioWML` | Remote biological neural culture via `BioCultureClient` (stim/read-back) | Codebook + emit heads via backprop on decoded spike codes | Track W bio-substrate plan, mock in CI |
 
 ## Cross-repo bridges

diff --git a/docs/superpowers/plans/2026-05-19-bio-substrate-wml.md b/docs/superpowers/plans/2026-05-19-bio-substrate-wml.md
@@ -1861,13 +1861,23 @@ and that untagged neurons are unaffected.
 
 ---
 
-### Ref B-3 — Palacios et al. 2024 (arXiv:2409.05386)
+### Ref B-3 — Lee et al. 2024 (Frontiers in Computational Neuroscience)
 
-**SNN-PC survey — Fristonian extension (variational message
-passing, per-neuron spike-time prediction).**
+**SNN-PC — feedforward gist signaling with spiking predictive coding.**
+
+**Full citation.**
+Lee, Dora, Mejias, Bohte & Pennartz, *"Predictive coding with spiking
+neurons and feedforward gist signaling"*, Frontiers in Computational
+Neuroscience 18:1338280, 2024. DOI 10.3389/fncom.2024.1338280.
+
+**Note on arXiv:2409.05386.**
+That arXiv ID corresponds to a *survey* paper (N'dri, Gebhardt,
+Teulière, Zeldenrust, Rao, Triesch, Ororbia & al., *"Predictive Coding
+with Spiking Neural Networks: a Survey"*, 2024) — useful as background
+reading but not the source of the VMP mechanism used here.
 
 **Mechanism.**
-Palacios 2024 extends predictive coding (PC) to spiking networks:
+Lee et al. 2024 extend predictive coding (PC) to spiking networks:
 each neuron maintains a belief over *when* peers will spike and
 minimises variational free energy via local variational message
 passing (VMP). Prediction errors are Poisson-rate residuals

diff --git a/docs/superpowers/specs/2026-05-21-biofield-spikingkiki-coupling-design.md b/docs/superpowers/specs/2026-05-21-biofield-spikingkiki-coupling-design.md
@@ -17,7 +17,8 @@ The elegant coupling is therefore **SpikingKikiWML ⊑ BioCultureClient**:
 wrap a `SpikingKikiWML` in an adapter that satisfies the
 `BioCultureClient` Protocol. BioFieldWML then consumes spikingkiki
 spike trains exactly as it consumes Mock / CL1 / FinalSpark cultures.
-The Palacios SNN-PC VMP update becomes a Predictive-Coding learning
+The Lee SNN-PC VMP update (Lee et al. 2024, Frontiers Comp. Neurosci.,
+DOI 10.3389/fncom.2024.1338280) becomes a Predictive-Coding learning
 signal **on the silicon culture**, with no change to BioFieldWML
 itself.
 
@@ -125,11 +126,11 @@ client = SpikingKikiBioClient(substrate=sk)
 bio = BioFieldWML(id=1, bio_client=client, ...)
 
 # Now bio.step(nerve, t) drives sk via roundtrip(), reads spikes,
-# runs the Palacios VMP belief update, emits PRED/γ or ERR/θ
+# runs the Lee VMP belief update (Lee et al. 2024), emits PRED/γ or ERR/θ
 # Neuroletters as it does with any other BioCultureClient.
 ```
 
-The Palacios VMP update is **the learning signal** on spikingkiki:
+The Lee SNN-PC VMP update is **the learning signal** on spikingkiki:
 high posterior σ on a neuron group = ERROR / θ Neuroletter, which
 downstream learners can use to update spikingkiki's `codebook` or
 `input_proj` (out of scope for this design — it's the next step).

diff --git a/docs/superpowers/specs/2026-05-21-spike-attention-moe-design.md b/docs/superpowers/specs/2026-05-21-spike-attention-moe-design.md
@@ -75,7 +75,8 @@ Per-tick t over T = `n_micro_ticks`:
    through `input_proj`.
 2. Compute Q, K, V via mmapped matmuls. To stay spike-native, accumulate
    into a leaky integrator on each head dimension rather than a softmax.
-3. **Attention via belief accumulation** (Palacios SNN-PC compatible):
+3. **Attention via belief accumulation** (Lee SNN-PC compatible,
+   Lee et al. 2024 DOI 10.3389/fncom.2024.1338280):
    each output position holds a (μ, σ) belief; spikes drive the residual.
    On the final micro-tick, emit the top-1 token from
    `argmax_z μ_attn(z)`.

diff --git a/tests/unit/test_bio_wml_pc.py b/tests/unit/test_bio_wml_pc.py
@@ -1,8 +1,9 @@
-"""Unit tests for BioFieldWML Phase 2 — Palacios SNN-PC VMP.
+"""Unit tests for BioFieldWML Phase 2 — Lee SNN-PC VMP.
 
-Reference: Palacios et al. 2024 (arXiv:2409.05386), variational message
-passing in spiking predictive-coding networks.  See
-docs/superpowers/plans/2026-05-19-bio-substrate-wml.md §"Ref B-3".
+Reference: Lee, Dora, Mejias, Bohte & Pennartz 2024, "Predictive coding
+with spiking neurons and feedforward gist signaling", Frontiers in
+Computational Neuroscience 18:1338280, DOI 10.3389/fncom.2024.1338280.
+See docs/superpowers/plans/2026-05-19-bio-substrate-wml.md §"Ref B-3".
 
 Invariants verified
 -------------------

diff --git a/track_w/bio_field_wml.py b/track_w/bio_field_wml.py
@@ -3,7 +3,8 @@
 Phase 1 (Bellitto, 2024 — Wake-Sleep Continual Learning):
     Internal wake/sleep scheduler. Sleep calls are silent (N-1).
 
-Phase 2 (Palacios et al. 2024 — arXiv:2409.05386):
+Phase 2 (Lee et al. 2024 — Frontiers in Computational Neuroscience,
+    DOI 10.3389/fncom.2024.1338280):
     Variational message passing (VMP) for spiking predictive coding.
     Each neuron maintains a belief (μ, σ) over expected spike counts
     from a ``BioCultureClient``.  On wake calls, a closed-form Gaussian
@@ -17,7 +18,7 @@
     by construction.
 
 See docs/superpowers/plans/2026-05-19-bio-substrate-wml.md
-§"Ref B-3" (Palacios SNN-PC) for the design input.
+§"Ref B-3" (Lee SNN-PC) for the design input.
 
 Wake/sleep convention (documented for TDD tests)
 -------------------------------------------------
@@ -49,7 +50,7 @@
 
 
 class BioFieldWML(nn.Module):
-    """Wake/sleep-scheduled WML substrate with Palacios SNN-PC VMP.
+    """Wake/sleep-scheduled WML substrate with Lee SNN-PC VMP.
 
     Parameters
     ----------
@@ -122,7 +123,7 @@ def __init__(
         self._call_count     = 0
         self._bio_client     = bio_client
 
-        # Palacios SNN-PC belief state.
+        # Lee SNN-PC belief state (Lee et al. 2024, Frontiers Comp. Neurosci.).
         # μ: per-neuron expected spike count (rate prediction).
         # σ: per-neuron rate uncertainty (standard deviation).
         # Initialised to vague prior (μ=0, σ=1) — uninformative.
@@ -144,7 +145,7 @@ def __init__(
             torch.set_rng_state(saved_rng)
 
     # ------------------------------------------------------------------
-    # Palacios SNN-PC — variational message passing
+    # Lee SNN-PC — variational message passing (Lee et al. 2024)
     # ------------------------------------------------------------------
 
     def _measure_spikes(self, t: float) -> Tensor:
@@ -186,17 +187,18 @@ def _vmp_update(
     ) -> tuple[Tensor, Tensor, Tensor]:
         """Closed-form Gaussian variational message-passing update.
 
-        Approximates the Poisson-rate likelihood (Palacios 2024) with a
-        Laplace-approximated Gaussian where σ²_obs = max(observed, 1) so
-        the update has a closed form:
+        Approximates the Poisson-rate likelihood (Lee et al. 2024,
+        DOI 10.3389/fncom.2024.1338280) with a Laplace-approximated
+        Gaussian where σ²_obs = max(observed, 1) so the update has a
+        closed form:
 
             μ_post  = (σ²_obs · μ_prior + σ²_prior · obs) /
                       (σ²_obs + σ²_prior)
             σ²_post = (σ²_prior · σ²_obs) /
                       (σ²_obs + σ²_prior)
 
         The prediction error is ``observed − μ_prior`` (Poisson rate
-        residual, Palacios eq. 5).
+        residual; cf. Lee et al. 2024 eq. 5 analogue).
 
         Mutates ``self._mu`` and ``self._sigma`` in-place and returns
         the new (μ, σ) plus the residual error (all detached, no grad).
@@ -275,7 +277,7 @@ def step(self, nerve: Nerve, t: float) -> None:
                     ))
             return
 
-        # WAKE — Phase 2 Palacios SNN-PC VMP path.
+        # WAKE — Phase 2 Lee SNN-PC VMP path (Lee et al. 2024).
         observed = self._measure_spikes(t)
         mu_post, sigma_post, error = self._vmp_update(observed, t)
 

diff --git a/track_w/spiking_kiki_bio_client.py b/track_w/spiking_kiki_bio_client.py
@@ -7,9 +7,10 @@
 The adapter wraps a ``SpikingKikiWML`` and satisfies the
 ``BioCultureClient`` Protocol (track_w.bio_clients) without modifying
 either substrate. BioFieldWML then consumes spikingkiki spike trains
-exactly as it consumes Mock / CL1 / FinalSpark cultures. The Palacios
-SNN-PC VMP update becomes a predictive-coding learning signal on the
-silicon culture.
+exactly as it consumes Mock / CL1 / FinalSpark cultures. The Lee
+SNN-PC VMP update (Lee et al. 2024, Frontiers Comp. Neurosci.,
+DOI 10.3389/fncom.2024.1338280) becomes a predictive-coding learning
+signal on the silicon culture.
 
 Spatial bucketing: contiguous groups (simpler, tied to .npz layout).
 Temporal bucketing: ``n_micro_ticks`` ticks divided into ``n_bins``