collaborators

7 papers

cs.CL2025

Polysemy of Synthetic Neurons Towards a New Type of Explanatory Categorical Vector Spaces

Michael Pichat, William Pogrund, Paloma Pichat +6

The polysemantic nature of synthetic neurons in artificial intelligence language models is currently understood as the result of a necessary superposition of distributed features w…

cs.AI2025

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

Michael Pichat, William Pogrund, Paloma Pichat +4

This study investigates the ability of perceptron-type neurons in language models to perform intra-neuronal attention; that is, to identify different homogeneous categorical segmen…

cs.CL2025

Synthetic Categorical Restructuring large Or How AIs Gradually Extract Efficient Regularities from Their Experience of the World

Michael Pichat, William Pogrund, Paloma Pichat +6

How do language models segment their internal experience of the world of words to progressively learn to interact with it more efficiently? This study in the neuropsychology of art…

cs.AI2025

The Process of Categorical Clipping at the Core of the Genesis of Concepts in Synthetic Neural Cognition

Michael Pichat, William Pogrund, Armanush Gasparian +5

This article investigates, within the field of neuropsychology of artificial intelligence, the process of categorical segmentation performed by language models. This process involv…

q-bio.NC2024

How Do Artificial Intelligences Think? The Three Mathematico-Cognitive Factors of Categorical Segmentation Operated by Synthetic Neurons

Michael Pichat, William Pogrund, Armanush Gasparian +3

How do the synthetic neurons in language models create "thought categories" to segment and analyze their informational environment? What are the cognitive characteristics, at the v…

q-bio.NC2024

Neuropsychology and Explainability of AI: A Distributional Approach to the Relationship Between Activation Similarity of Neural Categories in Synthetic Cognition

Michael Pichat, Enola Campoli, William Pogrund +7

We propose a neuropsychological approach to the explainability of artificial neural networks, which involves using concepts from human cognitive psychology as relevant heuristic re…