A Dynamical Cartography of the Epistemic Diffusion of Artificial Intelligence in Neuroscience

TL;DR

This paper maps the evolution of AI in neuroscience, highlighting its growing focus on neurodegenerative diseases since the 1990s and analyzing how AI technologies remain confined within subfields without broad transfer.

Contribution

It provides a temporal and conceptual cartography of AI's integration into neuroscience, revealing limited cross-subfield transfer of AI technologies.

Findings

AI and neuroscience continue to grow together, especially in neurodegenerative research.

AI technologies tend to remain within specific subfields, showing limited transfer across disciplines.

The study offers a detailed dynamical mapping of the discipline since the 1970s.

Abstract

Neuroscience and AI have an intertwined history, largely relayed in the literature of both fields. In recent years, due to the engineering orientations of AI research and the monopoly of industry for its large-scale applications, the mutual expansion of neuroscience and AI in fundamental research seems challenged. In this paper, we bring some empirical evidences that, on the contrary, AI and neuroscience are continuing to grow together, but with a pronounced interest in the fields of study related to neurodegenerative diseases since the 1990s. With a temporal knowledge cartography of neuroscience drawn with advanced document embedding techniques, we draw the dynamical shaping of the discipline since the 1970s and identified the conceptual articulation of AI with this particular subfield mentioned before. However, a further analysis of the underlying citation network of the studied corpus shows that the produced AI technologies remain confined in the different subfields and are not transferred from one subfield to another. This invites us to discuss the genericity capability of AI in the context of an intradisciplinary development, especially in the diffusion of its associated metrology.