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Information Retrieval and Categorisation using a Cell Assembly Network

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Abstract

Simulated networks of spiking leaky integrators are used to categorise and for Information Retrieval (IR). Neurons in the network are sparsely connected, learn using Hebbian learning rules, and are simulated in discrete time steps. Our earlier work has used these models to simulate human concept formation and usage, but we were interested in the model’s applicability to real world problems, so we have done experiments on categorisation and IR. The results of the system show that congresspeople are correctly categorised 89% of the time. The IR systems have 40% average precision on the Time collection, and 28% on the Cranfield 1,400. All scores are comparable to the state of the art results on these tasks.

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Acknowledgements

This work was supported by EPSRC grant GR/R13975/01.

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Authors and Affiliations

  1. School of Computer Science, Middlesex University, The Burroughs, London, NW44BT, UK

    Christian R. Huyck & Viviane Orengo

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  1. Christian R. Huyck
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  2. Viviane Orengo
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Correspondence to Christian R. Huyck.

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Huyck, C.R., Orengo, V. Information Retrieval and Categorisation using a Cell Assembly Network. Neural Comput & Applic 14, 282–289 (2005). https://doi.org/10.1007/s00521-004-0464-6

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  • DOI: https://doi.org/10.1007/s00521-004-0464-6

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