Unsupervised clustering with spiking neurons by sparse temporal coding and multi-layer RBF networks
We demonstrate that spiking neural networks encoding information in spike times 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 multi-layer network induces 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 induced synchronization of neurons within early RBF layers allows for the subsequent detection of complex clusters.
|Other Architecture Styles (acm C.1.3), Models of Computation (acm F.1.1), Learning (acm I.2.6), Models (acm I.5.1)|
|Neural nets (msc 82C32), Learning and adaptive systems (msc 68T05), Pattern recognition, speech recognition (msc 68T10), Knowledge representation (msc 68T30), Neural networks, artificial life and related topics (msc 92B20)|
|Software (theme 1), Logistics (theme 3), Energy (theme 4)|
|Software Engineering [SEN]|
|Organisation||Intelligent and autonomous systems|
Bohte, S.M, La Poutré, J.A, & Kok, J.N. (2000). Unsupervised clustering with spiking neurons by sparse temporal coding and multi-layer RBF networks. Software Engineering [SEN]. CWI.