神经生物学中的时空模式：未来人工智能的概述
Spatiotemporal Patterns in Neurobiology: An Overview for Future Artificial Intelligence
摘要：
近年来，人们对开发模型和工具来解决脑组织中发现的复杂连接模式越来越感兴趣。具体来说，这是由于需要了解如何从这些网络结构中出现的紧急属性在多个时空尺度。我们认为，计算模型的关键工具，阐明可能出现的功能，从多尺度的时间和空间域上的复杂网络连接的异质神经元的相互作用。在这里，我们回顾了几类模型，包括尖峰神经元，具有短期可塑性（STP）的整合和激发神经元，基于STP的电导整合和激发模型，以及人口密度神经场（PDNF）模型，使用简单的例子强调神经科学应用，同时也为AI提供了一些潜在的未来研究方向。这些计算方法使我们能够从实验和理论上探索改变潜在机制对网络功能的影响。因此，我们希望这些研究能够为人工智能算法的未来发展提供信息，并帮助验证我们对基于动物或人类实验的大脑过程的理解。
英文摘要：
In recent years, there has been increasing interest in developing models and tools to address the complex patterns of connectivity found in brain tissue. Specifically, this is due to a need to understand how emergent properties emerge from these network structures at multiple spatiotemporal scales. We argue that computational models are key tools for elucidating the possible functionalities that can emerge from interactions of heterogeneous neurons connected by complex networks on multi-scale temporal and spatial domains. Here we review several classes of models including spiking neurons, integrate and fire neurons with short term plasticity (STP), conductance based integrate-and-fire models with STP, and population density neural field (PDNF) models using simple examples with emphasis on neuroscience applications while also providing some potential future research directions for AI. These computational approaches allow us to explore the impact of changing underlying mechanisms on resulting network function both experimentally as well as theoretically. Thus we hope these studies will inform future developments in artificial intelligence algorithms as well as help validate our understanding of brain processes based on experiments in animals or humans.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
