Laboratory of Synthetic Perceptive, Emotive, and Cognitive Systems (SPECS), Universitat Pompeu Fabra, Roc Boronat 138, 08018 Barcelona, Spain.
Neuroinformatics. 2010 Jun;8(2):113-34. doi: 10.1007/s12021-010-9069-7.
The brain is the most complex system we know of. Despite the wealth of data available in neuroscience, our understanding of this system is still very limited. Here we argue that an essential component in our arsenal of methods to advance our understanding of the brain is the construction of artificial brain-like systems. In this way we can encompass the multi-level organisation of the brain and its role in the context of the complete embodied real-world and real-time perceiving and behaving system. Hence, on the one hand, we must be able to develop and validate theories of brains as closing the loop between perception and action, and on the other hand as interacting with the real world. Evidence is growing that one of the sources of the computational power of neuronal systems lies in the massive and specific connectivity, rather than the complexity of single elements. To meet these challenges-multiple levels of organisation, sophisticated connectivity, and the interaction of neuronal models with the real-world-we have developed a multi-level neuronal simulation environment, iqr. This framework deals with these requirements by directly transforming them into the core elements of the simulation environment itself. iqr provides a means to design complex neuronal models graphically, and to visualise and analyse their properties on-line. In iqr connectivity is defined in a flexible, yet compact way, and simulations run at a high speed, which allows the control of real-world devices-robots in the broader sense-in real-time. The architecture of iqr is modular, providing the possibility to write new neuron, and synapse types, and custom interfaces to other hardware systems. The code of iqr is publicly accessible under the GNU General Public License (GPL). iqr has been in use since 1996 and has been the core tool for a large number of studies ranging from detailed models of neuronal systems like the cerebral cortex, and the cerebellum, to robot based models of perception, cognition and action to large-scale real-world systems. In addition, iqr has been widely used over many years to introduce students to neuronal simulation and neuromorphic control. In this paper we outline the conceptual and methodological background of iqr and its design philosophy. Thereafter we present iqr's main features and computational properties. Finally, we describe a number of projects using iqr, singling out how iqr is used for building a "synthetic insect".
大脑是我们所知道的最复杂的系统。尽管神经科学中有大量的数据，但我们对这个系统的理解仍然非常有限。在这里，我们认为，在我们用于推进对大脑理解的方法中，一个必不可少的组成部分是构建人工类脑系统。通过这种方式，我们可以包含大脑的多层次组织及其在完整的体现现实世界和实时感知和行为系统背景下的作用。因此，一方面，我们必须能够发展和验证大脑的理论，使感知和行为之间形成闭环，另一方面，大脑的理论必须与现实世界相互作用。有证据表明，神经元系统的计算能力的一个来源在于大规模和特定的连接，而不是单个元素的复杂性。为了应对这些挑战——多层次的组织、复杂的连接以及神经元模型与现实世界的相互作用，我们开发了一个多层次神经元模拟环境，即 iqr。该框架通过将这些要求直接转化为模拟环境本身的核心元素来处理这些要求。iqr 提供了一种以图形方式设计复杂神经元模型的方法，并在线可视化和分析它们的属性。在 iqr 中，连接以灵活而紧凑的方式定义，并且模拟以高速运行，这允许实时控制现实世界中的设备——广义上的机器人。iqr 的架构是模块化的，提供了编写新神经元和突触类型以及自定义接口到其他硬件系统的可能性。iqr 的代码在 GNU 通用公共许可证（GPL）下是公开可用的。自 1996 年以来，iqr 一直在使用，并一直是许多研究的核心工具，这些研究范围从大脑皮层和小脑等神经元系统的详细模型，到基于机器人的感知、认知和行为模型，再到大规模的现实世界系统。此外，iqr 多年来被广泛用于向学生介绍神经元模拟和神经形态控制。在本文中，我们概述了 iqr 的概念和方法背景及其设计理念。然后，我们介绍了 iqr 的主要特点和计算特性。最后，我们描述了使用 iqr 的一些项目，突出了 iqr 如何用于构建“合成昆虫”。
