Explainable Artificial Intelligence for Neuroscience: Behavioral Neurostimulation

TLDR

Artificial Intelligence and machine learning are increasingly used in neuroscience to analyze large multimodal datasets, offering unbiased insights into brain function and aiding early detection of disorders, yet they often lack mechanistic explanations of input‑output relationships. The study reports on practical XAI approaches and discusses their potential value for neurostimulation research and therapy, while highlighting outstanding questions and obstacles. The authors present practical XAI approaches that aim to elucidate input‑output relationships in neurostimulation.

Abstract

The use of Artificial Intelligence and machine learning in basic research and clinical neuroscience is increasing. AI methods enable the interpretation of large multimodal datasets that can provide unbiased insights into the fundamental principles of brain function, potentially paving the way for earlier and more accurate detection of brain disorders and better informed intervention protocols. Despite AI’s ability to create accurate predictions and classifications, in most cases it lacks the ability to provide a mechanistic understanding of how inputs and outputs relate to each other. Explainable Artificial Intelligence (XAI) is a new set of techniques that attempts to provide such an understanding, here we report on some of these practical approaches. We discuss the potential value of XAI to the field of neurostimulation for both basic scientific inquiry and therapeutic purposes, as well as, outstanding questions and obstacles to the success of the XAI approach.