Datasets:
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
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.
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Citation
@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}
}