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摘要翻译：
基于递归尖峰神经元的学习算法不简单，在实际应用中受到限制。尖峰神经元的时间特性更有利于硬件实现，其中信号可以以二进制形式表示，通信可以通过使用尖峰来完成。本文研究了循环尖峰神经元在可重构平台上实现的潜力及其在基于时态的应用中的适用性。研究了油藏计算的硬件/软件实现的理论框架。在该框架中，只训练读出神经元，克服了网络级训练的负担。这些递归神经网络被称为微电路，是皮层计算的基本计算单元。本文研究了递归神经库的潜力，并提出了一种在FPGAS上实现递归神经库的新的硬件/软件策略。在语音识别应用的背景下，实现了该设计并对其功能进行了测试。
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英文标题：
《Hardware/Software Co-Design for Spike Based Recognition》
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作者：
Arfan Ghani, Martin McGinnity, Liam Maguire, Jim Harkin
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最新提交年份：
2008
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Neural and Evolutionary Computing 神经与进化计算
分类描述：Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络，连接主义，遗传算法，人工生命，自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
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一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science 计算机科学
二级分类：Computational Engineering, Finance, and Science 计算工程、金融和科学
分类描述：Covers applications of computer science to the mathematical modeling of complex systems in the fields of science, engineering, and finance. Papers here are interdisciplinary and applications-oriented, focusing on techniques and tools that enable challenging computational simulations to be performed, for which the use of supercomputers or distributed computing platforms is often required. Includes material in ACM Subject Classes J.2, J.3, and J.4 (economics).
涵盖了计算机科学在科学、工程和金融领域复杂系统的数学建模中的应用。这里的论文是跨学科和面向应用的，集中在技术和工具，使挑战性的计算模拟能够执行，其中往往需要使用超级计算机或分布式计算平台。包括ACM学科课程J.2、J.3和J.4（经济学）中的材料。
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英文摘要：
The practical applications based on recurrent spiking neurons are limited due to their non-trivial learning algorithms. The temporal nature of spiking neurons is more favorable for hardware implementation where signals can be represented in binary form and communication can be done through the use of spikes. This work investigates the potential of recurrent spiking neurons implementations on reconfigurable platforms and their applicability in temporal based applications. A theoretical framework of reservoir computing is investigated for hardware/software implementation. In this framework, only readout neurons are trained which overcomes the burden of training at the network level. These recurrent neural networks are termed as microcircuits which are viewed as basic computational units in cortical computation. This paper investigates the potential of recurrent neural reservoirs and presents a novel hardware/software strategy for their implementation on FPGAs. The design is implemented and the functionality is tested in the context of speech recognition application.
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PDF链接：
https://arxiv.org/pdf/0807.2282
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