Summary The paper describes the symbolic notation and syntax for specifying factorial models for analysis of variance in the control language of the genstat statistical program system at Rothamsted. The notation generalizes that of Nelder (1965). Algorithm AS 65 (Rogers, 1973) converts factorial model formulae in this notation to a list of model terms represented as binary integers. A further extension of the syntax is discussed for specifying models generally (including non‐linear forms).

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Self-organizing maps are extremely useful in the eld of pattern recognition. They become less useful, however, when neurons fail to activate during training. This phenomenon occurs when neurons are initialized in areas of non-input and are far enough away from the input data to never move toward the input. These neurons effectively misrepresent the data set. This results in, among other things, patterns becoming unrecognizable. We introduce an algorithm called No Neuron Left Behind to solve this problem. We show that our algorithm produces a more accurate topological representation of the input space. We also show that no neuron clusters form in areas of noninput and that mapping quality of the SOM increases drastically when our algorithm is implemented. Finally, the running time of NNLB is better or comparable to classic SOM without it.