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'''Growing neural gas''' (GNG) is an artificial neural network algorithm that learns the topology of input data by incrementally adding neurons and connections. Unlike the [[self-organizing map]], which uses a fixed grid, GNG grows its structure dynamically: neurons are inserted near the neuron with the highest accumulated error, and connections are created between the best-matching unit and its neighbors.

The algorithm was developed by Bernd Fritzke in 1995. It is particularly useful for learning non-stationary distributions and for tasks that require an adaptive representation of the input space, such as [[robotics]] motion planning and [[computer vision]] feature extraction. GNG can be seen as a bridge between [[neural network]] topology learning and [[computational geometry]] mesh generation.

[[Category:Artificial Intelligence]] [[Category:Machine Learning]] [[Category:Systems]]