Supervised Sadp Eval

---
name: supervised-sadp-eval
description: Evaluates the ability of a gradient-free, locally-updated spiking neural network to classify images using population-level spike agreement metrics. It probes whether replacing backpropagation with supervised Spike Agreement-Dependent Plasticity (SADP) and Cohen’s κ can achieve competitive vision and biomedical classification performance while maintaining biological plausibility and hardware compatibility. Use when the user wants to benchmark on MNIST, Fashion-MNIST, CIFAR-10, LC25000, Brain MRI Tumor, or asks about evaluating this task. Reports accuracy.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2601.08526
  bibtex_key: [REDACTED]
  confidence: high
---

# supervised-sadp-eval

> Supervised Spike Agreement Dependent Plasticity for Fast Local Learning in Spiking Neural Networks — Gouri Lakshmi S et al. (2026) (arXiv:2601.08526, 2026)

## What this evaluates

Evaluates the ability of a gradient-free, locally-updated spiking neural network to classify images using population-level spike agreement metrics. It probes whether replacing backpropagation with supervised Spike Agreement-Dependent Plasticity (SADP) and Cohen’s κ can achieve competitive vision and biomedical classification performance while maintaining biological plausibility and hardware compatibility.

## Datasets

- **MNIST** — total 70000; splits: train (60000), test (10000)
- **Fashion-MNIST** — total 70000; splits: train (-1), test (-1)
- **CIFAR-10** — total 60000; splits: train (-1), test (-1)
- **LC25000** — total 25000; splits: colon_binary (-1), lung_3class (-1)
- **Brain MRI Tumor** — total ?; splits: train (-1), test (-1)

## Metrics

- `accuracy` **(primary)** — range: [0, 1]
  - Fraction of correctly classified samples in the test set.
- `macro-averaged F1-score` — range: [0, 1]
  - Mean of the F1-scores for each class, computed over the test set.
- `Runtime per Epoch` — range: other
  - Wall-clock time required to complete one full training epoch.

## Input / output format

**Input**: Image pixels normalized to [0,1], converted to stochastic spike trains via Poisson encoding over T=25 or T=100 timesteps. Alternatively, 256-dimensional CNN-extracted features normalized and converted to Poisson spike trains.

**Output**: Class label prediction derived from the output layer's spike activity (1SADP or 2SADP architecture).

## Scoring recipe

```python
def compute_metrics(y_true, y_pred):
    accuracy = (y_true == y_pred).mean()
    f1_macro = f1_score(y_true, y_pred, average='macro')
    return accuracy, f1_macro
```

## Common pitfalls

- Assuming standard backpropagation or surrogate gradients are used for weight updates instead of the local Hebbian and kappa-based SADP rules.
- Overlooking the impact of temporal resolution (T=25 vs T=100) on spike train length, which directly affects both runtime and learning dynamics.
- Confusing the two encoding schemes (Poisson-only vs CNN+Poisson) when comparing results across datasets of varying complexity.

## Evidence (verbatim from paper)

> Model evaluation metrics included accuracy, macro-averaged F1-score computed over the test set and Runtime per Epoch.

## Citation

```bibtex
@misc{gourilakshmi2026supervised,
  title={Supervised Spike Agreement Dependent Plasticity for Fast Local Learning in Spiking Neural Networks},
  author={Gouri Lakshmi S et al. (2026)},
  year={2026},
  note={arXiv:2601.08526}
}
```

- arXiv: 2601.08526