1994 Special Issue: Putative strategies of scene segmentation in monkey visual cortex
Neural Networks - Special issue: models of neurodynamics and behavior
Discrete neural computation: a theoretical foundation
Discrete neural computation: a theoretical foundation
Fast sigmoidal networks via spiking neurons
Neural Computation
Self-organizing maps
A unifying objective function for topographic mappings
Neural Computation
Networks of spiking neurons: the third generation of neural network models
Transactions of the Society for Computer Simulation International - Special issue: simulation methodology in transportation systems
Pulsed neural networks
Pulsed neural networks
Computing with spiking neurons
Pulsed neural networks
The handbook of brain theory and neural networks
Self-organizing feature maps: Kohonen maps
The handbook of brain theory and neural networks
Spikes: exploring the neural code
Spikes: exploring the neural code
Biologically-Inspired On-Chip Learning in Pulsed Neural Networks
Analog Integrated Circuits and Signal Processing - Special issue on Learning on Silicon
The Handbook of Brain Theory and Neural Networks
The Handbook of Brain Theory and Neural Networks
Methods in Neuronal Modeling: From Ions to Networks
Methods in Neuronal Modeling: From Ions to Networks
Neural Computation and Self-Organizing Maps; An Introduction
Neural Computation and Self-Organizing Maps; An Introduction
A Simple Aplysia-Like Spiking Neural Network to Generate Adaptive Behavior in Autonomous Robots
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
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One of the most prominent features of biological neural systems is that individual neurons communicate via short electrical pulses, the so-called action potentials or spikes. In this chapter we investigate possible mechanisms of unsupervised learning and self-organization in networks of spiking neurons. After giving a brief introduction to spiking neuron networks we describe a biologically plausible algorithm for these networks to find clusters in a high dimensional input space or a subspace of it. The algorithm is shown to work even in a dynamically changing environment. Futhermore, we study self-organizing maps of spiking neurons showing that networks of spiking neurons using temporal coding can achieve a topology preserving behavior quite similar to that of Kohonen's self-organizing map. For these networks a mechanism of competitive computation is proposed that is based on action potential timing. Thus, the winner in a population of competing neurons can be determined locally and in generally faster than in approaches which use rate coding. The models and algorithms presented in this chapter establish further steps toward more realistic descriptions of unsupervised learning in biological neural systems.