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Top-down, or priming signals to these neurons are sent by concept-models, '''M'''<sub>m</sub>('''S'''<sub>m</sub>,n) enumerated by index m=1,2..M. Each model is characterized by its parameters, '''S<sub>m</sub>'''; in the neuron structure of the brain they are encoded by strength of synaptic connections, mathematically, they are given by a set of numbers,
:< ,where A is the number of dimensions necessary to describe invividual model.
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]] 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.
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