Sensory ecology provides a conceptual framework for considering how animals ought to design sensory systems to capture meaningful information from their environments. The framework has been particularly successful at describing how one should allocate sensory receptors to maximize performance on a given task. Neural networks, in contrast, have made unique contributions to understanding how ‘hidden preferences’ can emerge as a by-product of sensory design. The two frameworks comprise complementary techniques for understanding the design and the evolution of sensation. This article reviews empirical literature from multiple modalities and levels of sensory processing, considering vision, audition and touch from the viewpoints of sensory ecology and neuroethology. In the process, it presents modifications of extant neural network algorithms that would allow a more effective integration of these diverse approaches. Together, the reviewed literature suggests important advances that can be made by explicitly formulating neural network models in terms of sensory ecology, by incorporating neural costs into models of perceptual evolution and by exploring how such demands interact with historical forces
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