模式识别是一个成熟的、令人兴奋的、快速发展的领域,它支撑着计算机视觉、图像处理、文本和文档分析以及神经网络等相关领域的发展。它与机器学习非常相似,在生物识别、生物信息学、多媒体数据分析和最新的数据科学等新兴领域也有应用。模式识别(Pattern Recognition)杂志成立于大约50年前,当时该领域刚刚出现计算机科学的早期。在这期间,它已大大扩大。只要这些论文的背景得到了清晰的解释并以模式识别文献为基础,该杂志接受那些对模式识别理论、方法和在任何领域的应用做出原创贡献的论文。 官网地址:http://dblp.uni-trier.de/db/conf/par/

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Parallels between the signal processing tasks and biological neurons lead to an understanding of the principles of self-organized optimization of input signal recognition. In the present paper, we discuss such similarities among biological and technical systems. We propose the addition to the well-known STDP synaptic plasticity rule to directs the weight modification towards the state associated with the maximal difference between the background noise and correlated signals. The principle of physically constrained weight growth is used as a basis for such control of the modification of the weights. It is proposed, that biological synaptic straight modification is restricted by the existence and production of bio-chemical 'substances' needed for plasticity development. In this paper, the information about the noise-to-signal ratio is used to control such a substances' production and storage and to drive the neuron's synaptic pressures towards the state with the best signal-to-noise ratio. Several experiments with different input signal regimes are considered to understand the functioning of the proposed approach.

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