Neural modeling fields: Difference between revisions

Revision as of 19:57, 10 August 2008

Revision as of 20:15, 10 August 2008

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Learning is an essential part of perception and cognition, and it is driven by the knowledge instinct. It increases a similarity measure between the sets of [[models (concept-models)|models]] and signals, L({'''X'''},{'''M'''}). The similarity measure is a function of model parameters and associations between the input [[bottom-up signals (bottom-up neural signals)|bottom-up signals]] and top-down, concept-model signals. For concreteness the following testtext refers to an object perception using simplified terminology, as if perception of objects in retinal signals occurs in a single level.

In constructing a mathematical description of the similarity measure, it is important to acknowledge two principles (which are almost obvious). ''First'', the visual field content is unknown before perception occurred and ''second'', it may contain any of a number of objects. Important information could be contained in any bottom-up signal; therefore, the similarity measure is constructed so that it accounts for all [[bottom-up signals (bottom-up neural signals)|bottom-up signals]], X(n),

:<big>L({'''X'''},{'''M'''}) = &prod;_n=1..N l('''X'''(n)).</big>

In constructing a mathematical description of the similarity measure, it is important to acknowledge two principles (which are almost obvious). ''First'', the visual field content is unknown before perception occurred and ''second'', it may contain any of a number of objects.