从大脑模型到机器人体内认知:生物可信性如何告知神经形态系统?
Martin Do Pham1, Amedeo D'Angiulli2, Maryam Mehri Dehnavi1
1Department of Computer Science, University of Toronto, Toronto, ON M5S 1A1, Canada.
Brain sciences
|September 28, 2023
概括
本研究探讨了人工智能和机器人的神经网络 (SNN) 中计算效率和生物现实主义的整合. 神经形态计算为体内认知提供了一个有前途的途径,将神经科学与人工智能相结合.
科学领域:
- 神经科学,人工智能和机器人的交叉点.
- 计算神经科学是一种神经科学.
- 嵌入式认知 嵌入式认知
背景情况:
- 尖端神经网络 (SNN) 对于生物系统中的自主智能行为至关重要.
- 在SNN中,将计算效率和生物可信度相结合是关键的挑战.
- 现有的研究在机器人控制中审查了神经形态计算,但在关闭认知循环方面不那么多.
研究的目的:
- 审查SNNs中的计算效率和生物可信性之间的相互作用.
- 为了批判性地比较SNN仿真的硬件和软件.
- 探索神经形态在机器人体内认知中的作用.
主要方法:
- 对神经科学,人工智能和机器人的历史和最近影响的跨学科审查.
- 对尖端神经网络 (SNN) 模型的分析.
- 对神经形态硬件和软件进行SNN仿真的比较.
- 对SNN概念的剖析和上下文化.
主要成果:
- 神经形态计算被确定为在物理系统中体现SNN的一个有希望的工具.
- 生物可行的尖端神经元模型适合于可解释性研究.
- 感知,定位和认知等机器人模块可以从神经形态硬件中受益.
结论:
- 神经形态学有效地解决了硬件中的计算能力和生物可信性之间的权衡.
- 神经机器人学中的神经形态系统为合成和自然的体内认知提供了测试平台.
- 未来的多学科努力应侧重于在这一领域的理论和实证工作的融合.
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