Neuroevolution insights into biological neural computation

• 成果类型: Review
• 署名作者: Miikkulainen, Risto
• 署名单位: University of Texas System; University of Texas Austin
• 刊物名称: SCIENCE
• ISSN/ISSBN: 0036-9541
• DOI: 10.1126/science.adp7478
• 发表日期: 2025-02-14
• 页码: 731-+
• 关键词: evolution; pattern; emergence; networks; models; SYSTEM; COMMUNICATION; coevolution; responses; language

摘要

This article reviews existing work and future opportunities in neuroevolution, an area of machine learning in which evolutionary optimization methods such as genetic algorithms are used to construct neural networks to achieve desired behavior. The article takes a neuroscience perspective, identifying where neuroevolution can lead to insights about the structure, function, and developmental and evolutionary origins of biological neural circuitry that can be studied in further neuroscience experiments. It proposes optimization under environmental constraints as a unifying theme and suggests the evolution of language as a grand challenge whose time may have come.