Abstract
Synaptic pruning is a crucial process during development. We study the imprinting and replay of spatiotemporal patterns in a spiking network, as a function of pruning degree. After a Spike Timing Dependent Plasticity-based learning of synaptic efficacies, the weak synapses are removed through a competitive pruning process. Surprisingly, after this pruning stage, the storage capacity for spatiotemporal patterns is relatively high also for very high diluition ratio.
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Scarpetta, S., De Candia, A. (2014). Effects of Pruning on Phase-Coding and Storage Capacity of a Spiking Network. In: Bassis, S., Esposito, A., Morabito, F. (eds) Recent Advances of Neural Network Models and Applications. Smart Innovation, Systems and Technologies, vol 26. Springer, Cham. https://doi.org/10.1007/978-3-319-04129-2_13
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DOI: https://doi.org/10.1007/978-3-319-04129-2_13
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-04128-5
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