A cellular simultaneous recurrent network (CSRN) [1-11] is a neural network architecture that uses conventional simultaneous recurrent networks (SRNs), or cells in a cellular structure. The cellular structure adds complexity, so the training of CSRNs is far more challenging than that of conventional SRNs. Computer Go serves as an excellent test bed for CSRNs because of its clear-cut objective. For the training data, we developed an accurate theoretical foundation and game tree for the 2x2 game board. The conventional CSRN architecture suffers from the multi-valued function problem; our modified CSRN architecture overcomes the problem by employing ternary coding of the Go board's representation and a normalized input dimension reduction. We demonstrate a 2x2 game tree trained with the proposed CSRN architecture and the proposed cellular particle swarm optimization.