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Models represent signals in the following way. Suppose that signal '''X(''n'')''' is coming from sensory neurons n activated by object m, which is characterized by parameters '''S<sub>m</sub>'''. These parameters may include position, orientation, or lighting of an object m. Model '''M<sub>m</sub>'''('''S<sub>m</sub>''',n) predicts a value '''X'''(n) of a signal at neuron n. For example, during visual perception, a neuron n in the visual cortex receives a signal '''X'''(n) from retina and a [[Priming (psychology)|priming]] signal '''M<sub>m</sub>'''('''S<sub>m</sub>''',n) from an object-concept-model ''m''. Neuron ''n'' is activated if both the bottom-up signal from lower-level-input and the top-down priming signal are strong. Various models compete for evidence in the bottom-up signals, while adapting their parameters for better match as described below. This is a simplified description of perception. The most benign everyday visual perception uses many levels from retina to object perception. The NMF premise is that the same laws describe the basic interaction dynamics at each level. Perception of minute features, or everyday objects, or cognition of complex abstract concepts is due to the same mechanism described below. Perception and cognition involve concept-models and learning. In perception, concept-models correspond to objects; in cognition models correspond to relationships and situations.
Learning is an essential part of perception and cognition, and in NMF theory it is driven by the dynamics that increase a [[similarity measure]] between the sets of models and signals, L({'''X'''},{'''M'''}). The similarity measure is a function of model parameters and associations between the input bottom-up signals and top-down, concept-model signals. In constructing a mathematical description of the similarity measure, it is important to acknowledge two principles:
:''First'', the visual field content is unknown before perception occurred
:''Second'', it may contain any of a number of objects. Important information could be contained in any bottom-up signal;
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