Abstract
Neurophysiological findings of graded persistent activity suggest that memory retrieval in the brain is described by dynamical systems with continuous attractors. It has recently been shown that robust graded persistent activity is generated in single cells. Multiple levels of stable activity at a single cell can be replicated by a model neuron with multiple hysteretic compartments. Here we propose a framework to simply calculate the dynamical behavior of a network of multi-stable neurons. We applied this framework to spreading activation for document retrieval. Our method shows higher performance of retrieval than other spreading activation methods. The present study thus presents novel and useful information-processing algorithm inferred from neuroscience.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .
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Tsuboshita, Y., Okamoto, H. (2005). Information Retrieval Based on a Neural-Network System with Multi-stable Neurons. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_137
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DOI: https://doi.org/10.1007/11550907_137
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