Unsupervised Clustering With Spiking Neurons By Sparse Temporal Coding and Multilayer RBF Networks  

Oleh:

Bohte, S. M. ; Poutre, H. La ; Kok, J. N.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 13 no. 2 (2002), page 426-435.
Topik: multilayer networks; clustering; spiking neurons; sparse temporal coding; multilayer; RBF networks

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.6 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

We demonstrate that spiking neural networks encoding information in the timing of single spikes are capable of computing and learning clusters from realistic data. We show how a spiking neural network based on spike - time coding and Hebbian learning can successfully perform unsupervised clustering on real - world data, and we demonstrate how temporal synchrony in a multilayer network can induce hierarchical clustering. We develop a temporal encoding of continuously valued data to obtain adjustable clustering capacity and precision with an efficient use of neurons : input variables are encoded in a population code by neurons with graded and overlapping sensitivity profiles. We also discuss methods for enhancing scale - sensitivity of the network and show how the induced synchronization of neurons within early RBF layers allows for the subsequent detection of complex clusters.

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