---
license: cc-by-4.0
task_categories:
- text-generation
language:
- en
tags:
- paper
- transformer
- hebbian-learning
- sparse-neural-networks
- efficient-ai
- net2net
- kwta
pretty_name: "A Sparse, Dynamically Routed Feed-Forward Alternative
for Transformer Layers
"
size_categories:
- n<1K
---

# HSNNM: A Sparse, Dynamically Routed Feed-Forward Alternative
for Transformer Layers

**Author:** Kastiel Tjuandra

**DOI:** [10.5281/zenodo.21271668](https://doi.org/10.5281/zenodo.21358950)

**GITHUB:** The link can be found on my Zenodo page. 

## Abstract

We describe the Hebbian Sparse Neural Network Model (HSNNM), a transformer feed-
forward layer that replaces the standard dense sublayer with a set of sparse, competitively routed
sub-networks (”lobes”). The design combines top-1 lobe routing, k-Winners-Take-All (k-WTA)
intra-lobe sparsity, a local Hebbian strength-modulation rule, and a net2net-style structural
growth mechanism. On a 7.33M-parameter model trained on a 10% slice of TinyStories, HSNNM
reduces FLOPs per token by 49.6% relative to a matched dense baseline (7.18M vs. 14.25M
FLOPs/token) at a cost of 3.4 percentage points of validation accuracy (76.06% vs. 79.45%).
Ablations over a 5,000-step stress test indicate that both the Hebbian mechanism and k-WTA
sparsity are associated with somewhat lower catastrophic forgetting than variants without them,
though part of this comparison uses a different evaluation protocol from the main results and
should be treated as suggestive rather than conclusive. We report these results, along with
their limitations, as a data point on the efficiency-accuracy trade-off available from a compute-
skipping sparse FFN design at small scale.

## Citation

```bibtex
@misc{tjuandra2026hsnnm,
  title={HSNNM: A Hebbian Sparse Neural Network Model for Efficient Transformer Learning},
  author={Tjuandra, Kastiel},
  year={2026},
  publisher={Zenodo},
  doi={10.5281/zenodo.21271668}
}