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At a particular hierarchical level, we enumerate [[neurons]] by index n=1,2..N. These [[neurons]] receive input, [[bottom-up signals (bottom-up neural signals)|bottom-up signals]],
'''X(n)''', from lower levels in the processing hierarchy. '''X'''(n) is a field of bottom-up neuronal [[synapse | synaptic]] activations, coming from [[neurons]] at a lower level. Each [[neuron]] has a number of [[synapses]]; for generality, we describe each neuron activation as a set of numbers, '''X'''(n) = {X<sub>d</sub>(n), d = 1,... D}. Top-down, or priming signals to these [[neurons]] are sent by [[concept-models]], M<sub>m</sub>(S<sub>m</sub>,n); we enumerate [[concept-models]] 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, we describe them as a set of numbers, '''S<sub>m</sub>''' = {S<sub>m</sub><sup>a</sup>, a = 1,... A}.
Models represent signals in the following way. Say, signal '''X(n)''', is coming from sensory [[neurons]] activated by object m, characterized by parameters '''S<sub>m</sub>'''. These parameters may include position, orientation, or lighting of an object m. [[Model (concept-models)|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.
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