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@@ -25,4 +25,6 @@ logs/*
 data/*
 .claude/*
 .claude
+# Notes for Claude Code stay off GitHub; they are kept in the maintainer's private backup.
+CLAUDE.md
 .vscode/*
diff --git a/CLAUDE.md b/CLAUDE.md
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-# BindsNET project notes
-
-## Python environment
-
-This project runs in the conda environment named `bindsNET`.
-
-- Interpreter: `/home/hananel/miniconda3/envs/bindsNET/bin/python`
-- Activate: `conda activate bindsNET`
-- The package is installed in editable mode from this directory, so edits to
-  `bindsnet/` are picked up without reinstalling.
-- Torch 2.14 with CUDA 13.0 is installed there. Do not use the `base` env.
-
-Run tests with:
-
-```shell
-conda run -n bindsNET python -m pytest -q
-```
-
-Run a single file with:
-
-```shell
-conda run -n bindsNET python -m pytest -q test/network/test_learning.py
-```
-
-## Committing
-
-Use `sc "message"` instead of `git commit` (see the user's global instructions).
-
-## Learning rules: sources and how to validate
-
-Each rule is pinned to its paper's equations by a from-scratch reference test.
-Read these before touching any rule:
-
-| Rule | Paper and equations | Test |
-|---|---|---|
-| `PostPre`, `WeightDependentPostPre`, `Hebbian` | Morrison, Diesmann & Gerstner 2008, *Biol. Cybern.* 98:459, eqs. 11-14 (traces Sect. 2.3) | `test/network/test_learning_rule_specs.py` |
-| `DiehlAndCook` (classic and MCC) | Diehl & Cook 2015, *Front. Comput. Neurosci.* 9:99, Sect. 2.3: post-spike-only, dw = eta (x_pre - x_tar)(w_max - w)^mu | `test/network/test_learning_rule_specs.py` |
-| `MSTDP`, `MSTDPET` | Florian 2007, *Neural Comput.* 19:1468, eqs. 3.9-3.12, 2.7-2.8 | `test/network/test_mstdp_florian.py` |
-| `Rmax` | Vasilaki et al. 2009, *PLoS Comput. Biol.* 5:e1000586, eqs. 7, 8, 13 | `test/network/test_learning_rule_specs.py` |
-
-Facts that were wrong in docstrings once and are now fixed (do not reintroduce):
-- `MSTDP` default `zero_lag=False` **is** Florian's discrete eq. 3.9 (reward at a
-  step multiplies the previous step's eligibility). `zero_lag=True` is the
-  un-lagged variant, not "exact Florian".
-- `Rmax` `tc_c = 0` is strict policy gradient; `inf` is naive Hebbian.
-- `clamp` spikes enter the trace (applied in `Nodes.forward` before the trace update).
-
-- Pair STDP rules carry no `dt` factor (the MCC `PostPre` used to; removed
-  2026-09-06). `MSTDPET` keeps the paper's `dt` (Florian eq. 2.7).
-- `PostPre` is not Diehl & Cook 2015's rule; the paper's rule is `DiehlAndCook`.
-  `DiehlAndCook2015` keeps `PostPre` as default (published replication); opt in
-  with `learning_rule=MCC_learning.DiehlAndCook`.
-
-User-facing summary of all this: `bindsnet/learning/README.md` (keep it in sync
-with `docs/source/models_spec.rst`).
-
-## Performance changes: the rule
-
-Any change to a per-timestep path must ship with a test that pins it to the
-formula it replaced (`test/network/test_perf_equivalence.py`). Before claiming
-"no change in results", run the same seeded networks on the old code (a git
-worktree of the previous commit) and the new code and compare with
-`torch.equal`; only batch>1 summation-order differences (about 1e-7) are
-acceptable, and must be stated. Exception: the batch-1 fused `addmm_` STDP update
-matches the un-fused formula bit for bit on some CPUs but not on GitHub's CI runners
-(max abs diff 1.5e-8 to 3.0e-8, CI run 35124573539, 2026-09-16), so those tests use
-the same tolerance and warn with the size. Benchmark: `examples/benchmark/hot_path_bench.py`.