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

• URL: http://arxiv.org/abs/2503.12992v1
• Date: Mon, 17 Mar 2025 09:47:11 GMT
• Title: Intra-neuronal attention within language models Relationships between activation and semantics
• Authors: Michael Pichat, William Pogrund, Paloma Pichat, Armanouche Gasparian, Samuel Demarchi, Corbet Alois Georgeon, Michael Veillet-Guillem,
• Abstract summary: This study investigates the ability of perceptron-type neurons in language models to perform intra-neuronal attention.<n>The objective of this work is to determine to what extent formal neurons can establish a homomorphic relationship between activation-based and categorical segmentations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

Related papers

• The Process of Categorical Clipping at the Core of the Genesis of Concepts in Synthetic Neural Cognition [0.0]
  This article investigates, within the field of neuropsychology of artificial intelligence, the process of categorical segmentation performed by language models.<n>This process involves, across different neural layers, the creation of new functional categorical dimensions to analyze the input textual data and perform the required tasks.<n>We explore several cognitive characteristics of this synthetic clipping in an exploratory manner.
  arXiv  Detail & Related papers  (2025-01-21T11:32:39Z)
• Neuropsychology and Explainability of AI: A Distributional Approach to the Relationship Between Activation Similarity of Neural Categories in Synthetic Cognition [0.11235145048383502]
  We propose an approach to explainability of artificial neural networks that involves using concepts from human cognitive tokens. We show that the categorical segment created by a neuron is actually the result of a superposition of categorical sub-dimensions within its input vector space.
  arXiv  Detail & Related papers  (2024-10-23T05:27:09Z)
• Sharing Matters: Analysing Neurons Across Languages and Tasks in LLMs [70.3132264719438]
  We aim to fill the research gap by examining how neuron activation is shared across tasks and languages. We classify neurons into four distinct categories based on their responses to a specific input across different languages. Our analysis reveals the following insights: (i) the patterns of neuron sharing are significantly affected by the characteristics of tasks and examples; (ii) neuron sharing does not fully correspond with language similarity; (iii) shared neurons play a vital role in generating responses, especially those shared across all languages.
  arXiv  Detail & Related papers  (2024-06-13T16:04:11Z)
• CREIMBO: Cross-Regional Ensemble Interactions in Multi-view Brain Observations [3.3713037259290255]
  Current analysis methods often fail to harness the richness of such data.<n> CREIMBO identifies the hidden composition of per-session neural ensembles through graph-driven dictionary learning.<n>We demonstrate CREIMBO's ability to recover true components in synthetic data.
  arXiv  Detail & Related papers  (2024-05-27T17:48:32Z)
• Exploring neural oscillations during speech perception via surrogate gradient spiking neural networks [59.38765771221084]
  We present a physiologically inspired speech recognition architecture compatible and scalable with deep learning frameworks. We show end-to-end gradient descent training leads to the emergence of neural oscillations in the central spiking neural network. Our findings highlight the crucial inhibitory role of feedback mechanisms, such as spike frequency adaptation and recurrent connections, in regulating and synchronising neural activity to improve recognition performance.
  arXiv  Detail & Related papers  (2024-04-22T09:40:07Z)
• Interpreting Context Look-ups in Transformers: Investigating Attention-MLP Interactions [19.33740818235595]
  This study investigates how attention heads and next-token neurons interact in large language models (LLMs) to predict new words. Our findings reveal that some attention heads recognize specific contexts and activate a token-predicting neuron accordingly.
  arXiv  Detail & Related papers  (2024-02-23T02:15:47Z)
• Astrocytes as a mechanism for meta-plasticity and contextually-guided network function [2.66269503676104]
  Astrocytes are a ubiquitous and enigmatic type of non-neuronal cell. Astrocytes may play a more direct and active role in brain function and neural computation.
  arXiv  Detail & Related papers  (2023-11-06T20:31:01Z)
• Multilayer Multiset Neuronal Networks -- MMNNs [55.2480439325792]
  The present work describes multilayer multiset neuronal networks incorporating two or more layers of coincidence similarity neurons. The work also explores the utilization of counter-prototype points, which are assigned to the image regions to be avoided.
  arXiv  Detail & Related papers  (2023-08-28T12:55:13Z)
• Contrastive-Signal-Dependent Plasticity: Self-Supervised Learning in Spiking Neural Circuits [61.94533459151743]
  This work addresses the challenge of designing neurobiologically-motivated schemes for adjusting the synapses of spiking networks. Our experimental simulations demonstrate a consistent advantage over other biologically-plausible approaches when training recurrent spiking networks.
  arXiv  Detail & Related papers  (2023-03-30T02:40:28Z)
• Identifying stimulus-driven neural activity patterns in multi-patient intracranial recordings [0.26651200086513094]
  Identifying stimulus-driven neural activity patterns is critical for studying the neural basis of cognition. This chapter first presents an overview of the major challenges to identifying stimulus-driven neural activity patterns in the general case. We will consider a variety of within-subject and across-subject approaches to identifying and modeling stimulus-driven neural activity patterns in multi-patient intracranial recordings.
  arXiv  Detail & Related papers  (2022-02-04T01:29:57Z)
• Overcoming the Domain Gap in Contrastive Learning of Neural Action Representations [60.47807856873544]
  A fundamental goal in neuroscience is to understand the relationship between neural activity and behavior. We generated a new multimodal dataset consisting of the spontaneous behaviors generated by fruit flies. This dataset and our new set of augmentations promise to accelerate the application of self-supervised learning methods in neuroscience.
  arXiv  Detail & Related papers  (2021-11-29T15:27:51Z)
• Compositional Explanations of Neurons [52.71742655312625]
  We describe a procedure for explaining neurons in deep representations by identifying compositional logical concepts. We use this procedure to answer several questions on interpretability in models for vision and natural language processing.
  arXiv  Detail & Related papers  (2020-06-24T20:37:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.