[RNN-RBM] rename to RNN-RBM, add unrolled depth-2 mode, update README

- Rename context33.ipynb → RNN-RBM.ipynb
- Add UNROLL_DEPTH parameter (1 = shared weights, N = alternating entities)
- Training loop uses t % n_layers instead of hardcoded t % 2
- README: RNN-RBM section with signal-flow ASCII art, modes table, parameter table, papers

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

README.md

@@ -1,3 +1,54 @@
+# RNN-RBM
+
+A Recurrent Temporal RBM in which the visible layer at each time step is
+`[h_{t-1} | x_t]` — the previous hidden state (context) concatenated with the
+current input.  Training uses Contrastive Divergence on `[h_t | x_{t+1}]` to
+learn next-step prediction.
+
+## Signal flow (UNROLL_DEPTH = 2)
+
+```
+        x[0]              x[1]              x[2]              x[3]
+          │                 │                 │                 │
+          ▼                 ▼                 ▼                 ▼
+      ┌───────┐         ┌───────┐         ┌───────┐         ┌───────┐
+ 0 ──►│       ├─ h[0] ─►│       ├─ h[1] ─►│       ├─ h[2] ─►│       ├─ h[3] ─►
+      │  W0   │         │  W1   │         │  W0   │         │  W1   │
+      └───────┘         └───────┘         └───────┘         └───────┘
+        t=0               t=1               t=2               t=3
+
+  vis[t]  =  [ h[t-1] | x[t] ]    (context ‖ sensory  →  input to W)
+  h[t]    =  sigmoid( W · vis[t] + b_h )   (new context  →  passed right)
+
+  Training:  CD on [ h[t] | x[t+1] ]  to predict the next sensory from h[t]
+```
+
+## Modes
+
+| `UNROLL_DEPTH` | Mode | Behaviour |
+|---|---|---|
+| 1 | shared weights | one entity reused at every time step; sequences may have any length |
+| N > 1 | unrolled | N entities rotate as `layer[t % N]`; each position learns its own W |
+
+With `UNROLL_DEPTH = 2` the two entities specialise for even and odd positions
+respectively, doubling parameter count while keeping inference identical to the
+shared case.
+
+## Key parameters (`RNN-RBM.ipynb`)
+
+| Parameter | Default | Meaning |
+|---|---|---|
+| `SENSORY_SIZE` | 40 | vocabulary size (one-hot) |
+| `CONTEXT_SIZE` | 256 | hidden / context dimension |
+| `UNROLL_DEPTH` | 2 | number of alternating entities |
+| `NUM_EPOCHS` | 100 | training epochs |
+
+## Papers
+https://proceedings.mlr.press/v5/sutskever09a.html — The Recurrent Temporal RBM (Sutskever, Hinton, Taylor 2009)
+https://arxiv.org/abs/1206.6392 — Modeling temporal dependencies with RNN-RBM (Boulanger-Lewandowski et al. 2012)
+
+---
+
 # GRBM
 ## Paper
 https://medium.com/@rtdcunha/gaussian-bernoulli-restricted-boltzmann-machines-4a68b8765485