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Computational Model of Neocortical Learning Process: Prototype

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Neural Information Processing (ICONIP 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8834))

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Abstract

The balloon model of neocortical growth and learning claims that learning starts with larger groups of functional units (neuron columns) responding to a signal, but with training and lateral cortical expansion and inhibition, the number of units responding to a particular signal decreases as the units become better able to differentiate similar inputs. This process is different from most Artificial Neural Networks, but has some similarities with Temporal Organizing Maps (TOM). This paper describes the architecture and testing of a prototype computational model, a variation on TOMs, which seeks to emulate the anatomical and physiological behavior. Preliminary results indicate that it is consistent with predictions.

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Teo, J.X., Seldon, H.L. (2014). Computational Model of Neocortical Learning Process: Prototype. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8834. Springer, Cham. https://doi.org/10.1007/978-3-319-12637-1_12

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  • DOI: https://doi.org/10.1007/978-3-319-12637-1_12

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12636-4

  • Online ISBN: 978-3-319-12637-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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