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首页> 外文会议>International Conference on Artificial Neural Networks;ICANN 2008 >Neuro-inspired Speech Recognition with Recurrent Spiking Neurons
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Neuro-inspired Speech Recognition with Recurrent Spiking Neurons

机译：反复发作的神经元的神经启发语音识别。

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This paper investigates the potential of recurrent spiking neurons for classification problems. It presents a hybrid approach based on the paradigm of Reservoir Computing. The practical applications based on recurrent spiking neurons are limited due to their non-trivial learning algorithms. In the paradigm of Reservoir Computing, instead of training the whole recurrent network only the output layer (known as readout neurons) are trained. These recurrent neural networks are termed as microcircuits which are viewed as basic computational units in cortical computation. These microcircuits are connected as columns which are linked with other neighboring columns in cortical areas. These columns read out information from each other and can serve both as reservoir and readout. The design space for this paradigm is split into three domains; front end, reservoir, and back end. This work contributes to the identification of suitable front and back end processing techniques along with stable and compact reservoir dynamics, which provides a reliable framework for classification related problems.
机译：本文研究了潜在的尖峰神经元分类问题的潜力。它提出了一种基于储层计算范式的混合方法。基于递归尖峰神经元的实际应用由于其非平凡的学习算法而受到限制。在“水库计算”范式中，不是训练整个循环网络，而是只训练输出层（称为读出神经元）。这些循环神经网络被称为微电路，在皮层计算中被视为基本计算单元。这些微电路连接成列，并与皮层区域中的其他相邻列链接。这些列相互读取信息，既可以用作存储库又可以用作读数。该范式的设计空间分为三个领域：前端，容器和后端。这项工作有助于确定合适的前端和后端处理技术以及稳定而紧凑的油藏动力学特性，从而为与分类相关的问题提供了可靠的框架。

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