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arXiv · 2503.12992

Intra-neuronal attention within language models Relationships between activation and semantics

Michael Pichat·William Pogrund·Paloma Pichat·Armanouche Gasparian·Samuel Demarchi·Corbet Alois Georgeon·Michael Veillet-Guillem
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Abstract

This study investigates the ability of perceptron-type neurons in language models to perform intra-neuronal attention; that is, to identify different homogeneous categorical segments within the synthetic thought category they encode, based on a segmentation of specific activation zones for the tokens to which they are particularly responsive. The objective of this work is therefore to determine to what extent formal neurons can establish a homomorphic relationship between activation-based and categorical segmentations. The results suggest the existence of such a relationship, albeit tenuous, only at the level of tokens with very high activation levels. This intra-neuronal attention subsequently enables categorical restructuring processes at the level of neurons in the following layer, thereby contributing to the progressive formation of high-level categorical abstractions.

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Michael Pichat, William Pogrund, Paloma Pichat, Armanouche Gasparian, Samuel Demarchi, Corbet Alois Georgeon, Michael Veillet-Guillem. 2025-03-17. Intra-neuronal attention within language models Relationships between activation and semantics. https://arxiv.org/abs/2503.12992

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