Unsupervised clustering with spiking neurons by sparse temporal coding and multilayer RBF networks

Abstract

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.

Analog-Digital Self-Learning Fuzzy Spiking Neural Network in Image Processing Problems

“…Centers of each width type of activation function are calculated in different ways but in either case they cover date range uniformly. More details can be found in (Bohte et al, 2002).…”

“…If clusters number exceeded number of input signal dimensions, spiking neural network performance decreased. This drawback was overcome by using population coding of incoming signal based on pools of receptive neurons in the first hidden layer of the network (Bohte et al, 2002). Such spiking neural network was shown to be considerably powerful and significantly fast in solving real life problems.…”

Computing with Spiking Neuron Networks

“…Bohte et al, [20] extend on this approach to enhance the precision, capacity and clustering capability of a network of spiking neurons by developing a temporal version of population coding. To extend the encoding precision and clustering capacity, input data is encoded into temporal spike-time patterns by population coding, using multiple local receptive fields like Radial Basis Functions.…”

Cell Microscopic Segmentation with Spiking Neuron Networks

“…Bohte et al [25], presented a method for encoding the input data to enhance the precision. Each neuron of entry is modeled by a local receiving field (RF).…”