Progress, challenges and future of linguistic neural decoding with deep learning.

Area of Science:

• Neuroscience
• Artificial Intelligence
• Computational Linguistics

Background:

• Language is fundamental for human information exchange, social interaction, and knowledge sharing.
• Linguistic neural decoding seeks to interpret brain activity related to language processing in both spoken and textual forms.

Purpose of the Study:

• To present a comprehensive taxonomy of recent progress in neural decoding of language.
• To highlight the role of deep learning architectures, particularly large language models (LLMs), in this field.

Main Methods:

• Review and categorization of recent neural decoding techniques.
• Focus on deep learning strategies and the application of large language models (LLMs).

Main Results:

• Identification of key deep learning architectures and strategies for linguistic neural decoding.
• Demonstration of the capacity of LLMs in understanding, processing, and generating language-related neural information.

Conclusions:

• The study provides an overview of the methodological landscape in neural decoding of language.
• It identifies current challenges and future research directions at the intersection of neuroscience and deep learning.