A small-size model for a chaotic neural network is proposed using strange attractors for computation. This network has a chaotic ground state and is capable of responding to external stimuli by constraining the network dynamics to specific parts of the ground state attractor. For parameter optimization, bifurcation diagrams are evaluated.