[論文レビュー] Brain-inspired Artificial Intelligence: A Comprehensive Review

Jing Ren, Feng Xia|arXiv (Cornell University)|Aug 27, 2024

EEG and Brain-Computer Interfaces被引用数 5

ひとこと要約

A comprehensive survey categorizing Brain-Inspired AI (BIAI) into physical-structure and human-behavior inspired models, detailing knowledge from neuroscience, applications, challenges, and future directions.

ABSTRACT

Current artificial intelligence (AI) models often focus on enhancing performance through meticulous parameter tuning and optimization techniques. However, the fundamental design principles behind these models receive comparatively less attention, which can limit our understanding of their potential and constraints. This comprehensive review explores the diverse design inspirations that have shaped modern AI models, i.e., brain-inspired artificial intelligence (BIAI). We present a classification framework that categorizes BIAI approaches into physical structure-inspired and human behavior-inspired models. We also examine the real-world applications where different BIAI models excel, highlighting their practical benefits and deployment challenges. By delving into these areas, we provide new insights and propose future research directions to drive innovation and address current gaps in the field. This review offers researchers and practitioners a comprehensive overview of the BIAI landscape, helping them harness its potential and expedite advancements in AI development.

研究の動機と目的

• 神経科学と人間行動がAIシステム設計にどのように影響を与えるかを紹介する。
• BIAI の二カテゴリ分類法を提供する: physical structure-inspired と human behavior-inspired モデル。
• 現実世界の応用、利点、展開の課題、倫理と解釈可能性への影響について論じる。
• 未解決の問題を特定し、BIAI を前進させる将来の研究方向を提案する。

提案手法

• 神経科学情報AIと人間行動に着想を得た学習機構に関する文献調査。
• BIAI アプローチを整理するための二カテゴリフレームワークを提案する（physical structure-inspired vs. human behavior-inspired）。
• 代表的なモデルとそれらの脳に着想を得た機構（学習、注意、記憶など）を要約する。
• ロボティクス、医療、感情知覚、創造的コンテンツ生成などの応用を論じる。
• 解釈可能性、スケーラビリティ、脳原理との整合性などの課題を指摘し、将来の方向性を概説する。

実験結果

リサーチクエスチョン

• RQ1 What is BIAI and how does it differ from general AI?
• RQ2 What brain-inspired sources (neural architecture, learning, attention, memory, cognition, creativity) can inform AI model design?
• RQ3 What categories of BIAI models exist and what are their strengths and limitations?
• RQ4 Which real-world domains can benefit from BIAI and what deployment challenges arise?
• RQ5 What are the major open problems and promising directions for future BIAI research?

主な発見

• BIAI integrates principles from neural architecture, learning mechanisms, attention, memory, cognition, and creativity.
• BIAI models are organized into physical structure-inspired and human behavior-inspired approaches.
• BIAI has potential advantages in adaptability, generalization, and interpretability over traditional AI.
• Applications span robotics, healthcare, emotion perception, and content generation, with deployment challenges highlighted.
• The paper outlines open problems and future directions to advance BIAI research and practice.