The boolean \(\Sigma \) \(\Pi \) -neuron is a biologically inspired formal model for logical information processing. The boolean \(\Sigma \) \(\Pi \) -neuron model adequately reflects information processing processes in the cerebral cortex and in the dendritic trees of neurons. The advantage of the boolean \(\Sigma \) \(\Pi \) -neuron model is the ability to accurately represent any boolean function and the possibility of constructive learning (direct construction) in a single pass of the training sample. Another possibility is the direct construction of an ensemble of boolean \(\Sigma \) \(\Pi \) -neurons that function correctly on the training sample. This article discusses a new algorithm for constructing an ensemble of boolean \(\Sigma \) \(\Pi \) -neurons in parameterized form. This form can also be easily represented as a single boolean \(\Sigma \) \(\Pi \) -network with a hidden layer of linear and threshold linear units. In some cases, this makes it easier to retrain on new inputs by setting the appropriate control parameter values.