---
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: "HSNNM: A Hebbian Sparse Neural Network Model for Efficient Transformer Learning"
size_categories:
- n<1K
---

# HSNNM: A Hebbian Sparse Neural Network Model for Efficient Transformer Learning

**Author:** Kastiel Tjuandra

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

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

## Abstract

We introduce the Hebbian Sparse Neural Network Model (HSNNM), a biologically-inspired transformer architecture that replaces the standard dense Feed-Forward Network (FFN) with a dynamically routed system of sparse, competing expert sub-networks termed "lobes". HSNNM integrates three core mechanisms: k-Winners-Take-All (k-WTA) lateral inhibition, non-gradient Hebbian plasticity, and net2net structural neurogenesis. We demonstrate that HSNNM achieves a 49.6% reduction in FLOPs per token (7.18M vs 14.25M FLOPs/token) compared to a matched dense baseline, while incurring a 3.4 percentage point accuracy trade-off (76.06% vs 79.45%), a favorable efficiency-accuracy balance given the significant compute savings. Through ablation studies, we show that both Hebbian plasticity and k-WTA sparsity contribute to stability over medium term training (5,000 steps). Our results establish HSNNM as an efficient and robust alternative to dense transformers.

## Download

[Download PDF](HSNNM-3.pdf)

## 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}
}