“Also, the performance of our network scales well with the number of neurons used and shows similar performance for four different learning rules, indicating robustness of the full combination of mechanisms, which suggests applicability in heterogeneous biological neural networks.”

Four numbers, never blended. Credence: how far independent evidence supports it; its status reads its verified replication tests alone. It started at its prior, 0.55. Use: how much rests on it on the record, counted per operator. Dispute: how much the evidence disagrees.

Stakes 10.50 = use + log₂(1 + reach) + log₂(1 + reliance): use 0.00 from the operators whose claims rest on it; reach 1,451: its source cited 1,451 times (OpenAlex, 13 Oct 2026; published 2015; field: Engineering); reliance 0: no claim on the record has been identified as resting on it yet. Stakes rank what to do next and feed the pressure on blocked claims; they never enter credence.

A replication test applies the claim's method to its own data (same data, same method: a verification) or to new data covering its own population and period (new data, same method: a reproduction). A robustness test changes the data or the method, and asks whether the finding holds under the change. On a claim about the world, a confirming verification counts half a confirming reproduction, and established needs a reproduction: re-running the authors' analysis shows the arithmetic was right, not that the finding holds on new data.

unchecked No replication test in independent code yet: re-runs of its own bundle, reviews and robustness tests alone leave a claim here.

Test written by

Imago, from the paper's words, on 13 Oct 2026.

Method

It adapts the paper's method: “The paper's figures for its power-law rule rest on parameters it does not give, adapted per size. The test takes the one fully specified configuration, the authors' released code (the triplet rule, one of Figure 2B's four: about 83% at 100 neurons, 91% at 400), its threshold step scaled for 100 neurons as the paper says it adapted it; one test pass, not ten; thresholds its own”. A test of this registration is, measured against the paper, a reanalysis.

Covers

General, by construction: “A spiking network defined by its construction: the authors' released network, learning rule and parameters, trained on MNIST's 60,000 training digits and tested on its 10,000 test digits”.

Data of record

train-images-idx3-ubyte.gz (sha256 440fcabf73cc…), train-labels-idx1-ubyte.gz (sha256 3552534a0a55…), t10k-images-idx3-ubyte.gz (sha256 8d422c7b0a1c…), t10k-labels-idx1-ubyte.gz (sha256 f7ae60f92e00…), Diehl_Cook_spiking_MNIST.py (sha256 cd4017b820a4…), Diehl_Cook_random_conn_generator.py (sha256 2390a3b67547…), pretrained_XeAe.npy (sha256 b704c770763d…), pretrained_theta_A.npy (sha256 9f5bcf6794bb…), named by Imago; a receipt on "the claim's own data" reads every one of these files, by hash.