# A growing neural gas network learns toplogies

**A growing neural gas network learns topologies**
by Bernd Fritzke (1995).

## Overview

Hebbian learning variations for learning topologies in high-dimensional data.

## Key concepts

**topological learning**: Given some high-dimensional data distributionp, find a topological structure which closely reflects the topology of the data distribu**competitive Hebbian leanring (CHL)**: For each input signal x connect the two closest centers (measured by Euclidean distance) by an edge.**vector quantization (VC)**: Often used for data compression, quantization involves dividing a large set of points (vectors) into groups having approximatley the same number of points closest to them. Each group is representated by its centroid (similar to k-means).**competitive learning**: A type of ANN where nodes compete for the right to respond to a subset of input data.**neural gas**: For each input signal x adapt the k nearest centers whereby k is decreasing from a large initial to a small final value.