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NMF is a multi-level, hetero-hierarchical system <ref>[http://www.oup.com/us/catalog/he/subject/Engineering/ElectricalandComputerEngineering/ComputerEngineering/NeuralNetworks/?view=usa&ci=9780195111620]: Perlovsky, L.I. 2001. Neural Networks and Intellect: using model based concepts. New York: Oxford University Press</ref>. The [[mind]] is not a strict hierarchy; there are multiple feedback connections among adjacent levels, hence the term hetero-hierarchy. At each level in NMF there are [[concept-models]] encapsulating the mind’s knowledge; they generate so-called [[top-down signals (top-down neural signals)|top-down signals]], interacting with input, [[bottom-up signals (bottom-up neural signals)|bottom-up signals]]. These interactions are governed by the [[knowledge instinct]], which drives [[concept-model]] learning, adaptation, and formation of new [[concept-models]] for better correspondence to the input, [[bottom-up signals (bottom-up neural signals)|bottom-up signals]].
Here we describe a basic mechanism of interaction between two adjacent hierarchical levels of bottom-up and top-down signals (fields of neural activation; in this aspect NMF follows<ref> Perlovsky, L.I. (2006). Toward Physics of the Mind: Concepts, Emotions, Consciousness, and Symbols. Phys. Life Rev. 3(1), pp.22-55.</ref>; sometimes, it will be more convenient to talk about these two signal-levels as an input to and output from a (single) processing-level. At each level, output signals are [[concepts]] recognized in (or formed from) input, [[bottom-up signals (bottom-up neural signals)|bottom-up signals]]. Input signals are associated with (or recognized, or grouped into) concepts according to the [[models (concept-models)|models]] and the [[knowledge instinct]] at this level. This general structure of NMF corresponds to our knowledge of neural structures in the brain; still, here we do not map mathematical mechanisms in all their details to specific [[neurons]] or synaptic connections. The [[knowledge instinct]] is described mathematically as maximization of a similarity measure. In the process of learning and understanding input, [[bottom-up signals (bottom-up neural signals)|bottom-up signals]], [[concept-models]] are adapted for better representation of the input signals so that similarity between the [[concept-models]] and signals increases. This increase in similarity satisfies the [[#the knowledge instinct | the knowledge instinct]] and is felt as [[aesthetic emotions]].
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