  We consider the design of a pattern recognition that matches templates to
images, both of which are spatially sampled and encoded as temporal sequences.
The image is subject to a combination of various perturbations. These include
ones that can be modeled as parameterized uncertainties such as image blur,
luminance, translation, and rotation as well as unmodeled ones. Biological and
neural systems require that these perturbations be processed through a minimal
number of channels by simple adaptation mechanisms. We found that the most
suitable mathematical framework to meet this requirement is that of weakly
attracting sets. This framework provides us with a normative and unifying
solution to the pattern recognition problem. We analyze the consequences of its
explicit implementation in neural systems. Several properties inherent to the
systems designed in accordance with our normative mathematical argument
coincide with known empirical facts. This is illustrated in mental rotation,
visual search and blur/intensity adaptation. We demonstrate how our results can
be applied to a range of practical problems in template matching and pattern
recognition.
