Lattice map spiking neural networks (LM-SNNs) for clustering and classifying image data
@article{Hazan2019LatticeMS, title={Lattice map spiking neural networks (LM-SNNs) for clustering and classifying image data}, author={Hananel Hazan and Daniel J. Saunders and Darpan T. Sanghavi and Hava T. Siegelmann and Robert Thijs Kozma}, journal={Annals of Mathematics and Artificial Intelligence}, year={2019}, volume={88}, pages={1237 - 1260} }
Spiking neural networks (SNNs) with a lattice architecture are introduced in this work, combining several desirable properties of SNNs and self-organized maps (SOMs). Networks are trained with biologically motivated, unsupervised learning rules to obtain a self-organized grid of filters via cooperative and competitive excitatory-inhibitory interactions. Several inhibition strategies are developed and tested, such as (i) incrementally increasing inhibition level over the course of network…
15 Citations
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