Abstract
In this paper, we construct a spiking network model based on the firing-rate coding hippocampal model proposed by Becker. Basal training patterns are presented to the model network and spiking self organizing map learning is applied to the network in order to store the training patterns. We then apply a morphogenesis model in the dentate gyrus region to generate new neurons and investigate the influence of such neurogenesis on the storage and recall of novel memory. As a result, the storage capacity is essentially unchanged by the morphogenetic algorithm even when the number of training patterns is changed.
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Tabata, Y., Adachi, M. (2009). A Spiking Network of Hippocampal Model Including Neurogenesis. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5506. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02490-0_2
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DOI: https://doi.org/10.1007/978-3-642-02490-0_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02489-4
Online ISBN: 978-3-642-02490-0
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