Abstract
Unsupervised learning has been widely used in many areas such as pattern recognition. However, it is usually difficult to acquire accurate representation of pattern within a limited period of time. Unsupervised learning, in general, is likely to be more common in brain than supervised learning. In this paper, we propose a new neural network based unsupervised learning method and evaluate its applications on 1-D and 2-D pattern learning. Our approach is inspired by recent researches on the physiological process of neural connection and brain activity. A bipolar weight scheme based on biological neural connection mechanism is presented. Moreover, we have also noticed the synaptic plasticity of brain plays an important role in learning. A new brain-inspired short-term and long-term scheme is applied in our method to adjust weights during the learning process. Experimental results of learning over 1-D and 2-D patterns demonstrate the proposed method is effective and high-efficiency.
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Weng, K., Liang, G., Wu, X. (2013). A New Bio-inspired Unsupervised Learning Method. In: Liu, D., Alippi, C., Zhao, D., Hussain, A. (eds) Advances in Brain Inspired Cognitive Systems. BICS 2013. Lecture Notes in Computer Science(), vol 7888. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38786-9_13
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DOI: https://doi.org/10.1007/978-3-642-38786-9_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38785-2
Online ISBN: 978-3-642-38786-9
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