Visual object-recognition plays a crucial role in animals that utilize visual information. In this study, we address the prey-predator recognition problem by optimizing artificial convolutional neural networks, based on neuroethological studies on toads. After the optimization of the overall network by supervised learning, the network showed a reasonable performance, even though various types of image noise existed. Also, we modulated the network after the optimization process based on the computational theory of classical conditioning and the reinforcement learning algorithm for the adaptation to environmental changes. This adaptation was implemented by separated modules that implement the "innate" term and "acquired" term of outputs. The modulated network exhibited behaviors that were similar to those of real toads. The neural basis of the amphibian visual information processing and the behavioral modulation mechanism have been substantially studied by biologists. Recent advances in parallel distributed processing technologies may enable us to develop fully autonomous, adaptive artificial agents with high-dimensional input spaces through end-to-end training methodology.