The problem of searching for additional sets of functions for information-driven permutation operations groups for general and special-purpose intelligent computer systems is relevant today. There is a problem with the insufficient productivity of methods for solving this problem. The object of the research is to find additional function sets for groups of information-driven permutation operations. The research subject is methods for determining the search for additional function sets for groups of permutation operations information-driven, based on neural networks with associative memory. The goal of the research is to improve the searching efficiency for additional function sets for groups of information-driven permutation operations using neural networks with associative memory. The neuro-associative search methods for additional function sets for groups of information-driven permutation operations were created. The method’s benefits include the following. The bidirectional recurrent correlation associative memory, which is based on auto-associative and hetero-associative memory and an exponential weighting function, can increase the capacity of associative memory while maintaining training accuracy. The numerical study enabled the evaluation of the proposed methods. These methods, in turn, facilitate the expansion of the application scope of artificial neural networks based on associative memory, as evidenced by their successful adaptation to the task of identifying additional function sets for groups of information-driven permutation operations.