RI: Medium: Collaborative Research: BCSP: Automated Parameter Tuning of Large-Scale Spiking Neural Networks

一个框架将被开发出来，以帮助科学家和工程师创建大脑启发的、大脑大小的网络，这些网络可以进行实际应用。大规模的脉冲神经网络，跟随大脑的结构和活动，已经被用来成功地模拟现象，如学习和记忆，视觉，听觉处理，神经振荡，和许多其他重要方面的神经功能。此外，脉冲神经网络特别适合在神经形态硬件上运行，这是模拟大脑的最先进的计算机。S结构和动力学。这些神经形态系统依靠尖峰的二进制特性来降低通信带宽和能量消耗。尽管在各种硬件平台上对大规模脉冲神经网络的规范和模拟取得了重大进展，但在这些神经生物学启发的算法可用于实际应用之前，仍存在许多挑战。虽然生物学确实提供了越来越丰富的经验数据，可以约束这些系统，但许多参数值必须由设计师手动选择，以获得适当的神经动力学，这是一项极其繁琐且容易出错的任务。为了应对这一挑战，将开发一种能够快速有效地调整大规模尖峰神经网络的自动参数调整框架。该框架将利用现有图形处理单元（gpu）的进化算法和优化技术的最新进展。参数搜索将受到神经科学思想的指导，即生物网络调整其反应以增加传输信息的数量，减少冗余，并跨越刺激空间。这种高效编码的概念将指导人工尖峰神经网络的调谐过程。计算机科学家和工程师将能够使用由此产生的自动参数调整框架，在神经形态硬件上创建受大脑启发的应用程序，如视觉和记忆系统。此外，由此产生的框架将允许神经科学家更容易地创建模型，更好地描述他们的经验数据，并产生新的定量假设，可以在实验室中进行测试。

A framework will be developed to help scientists and engineers create brain-inspired, brain-sized networks that can carry out practical applications. Large-scale spiking neural networks, which follow the brain's architecture and activity, have been used to successfully model phenomena such as learning and memory, vision, auditory processing, neural oscillations, and many other important aspects of neural function. Additionally, spiking neural networks are particularly well suited to run on neuromorphic hardware, state of the art computers that emulate the brain?s structure and dynamics. These neuromorphic systems depend on the binary nature of spikes to lower communication bandwidth and energy consumption. Although significant progress has been made towards the specification and simulation of large-scale spiking neural networks on a variety of hardware platforms, many challenges remain before these neurobiologically inspired algorithms can be used in practical applications. While biology does provide increasingly abundant empirical data that can constrain these systems, many parameter values must be chosen manually by the designer to achieve appropriate neuronal dynamics, a task that is extremely tedious and often error-prone. To meet this challenge, an automated parametertuning framework will be developed that is capable of quickly and efficiently tuning large-scale spiking neural networks. The framework will leverage recent progress in evolutionary algorithms and optimization techniques for off-the-shelf graphics processing units (GPUs). The parameter search will be guided by the idea in neuroscience that biological networks adapt their responses to increase the amount of transmitted information, reduce redundancies, and span the stimulus space. This notion of efficient coding will guide the tuning process of the artificial spiking neural networks. Computer scientists and engineers will be able to use the resulting automated parameter-tuning framework to create brain inspired applications, such as vision and memory systems, on neuromorphic hardware. Moreover, the resulting framework will allow neuroscientists to more readily create models that better describe their empirical data and generate new quantitative hypotheses that can be tested in the laboratory.