Abstract
Our work deals with the self-organization [1] of a memory structure that includes multiple hierarchical levels with massive recurrent communication within and between them. Such structure has to provide a representational basis for the relevant objects to be stored and recalled in a rapid and efficient way. Assuming that the object patterns consist of many spatially distributed local features, a problem of parts-based learning is posed. We speculate on the neural mechanisms governing the process of the structure formation and demonstrate their functionality on the task of human face recognition.
Highlights
Eighteenth Annual Computational Neuroscience Meeting: CNS*2009 Don H Johnson Meeting abstracts – A single PDF containing all abstracts in this Supplement is available here. http://www.biomedcentral.com/content/pdf/1471-2202-10-S1-info.pdf
The structure formation relies on the activity-dependent bidirectional plasticity [2] and the homeostatic regulation of unit's activity
BMC Neuroscience 2009, 10(Suppl 1):P207 character ensures that only a small subset of units may update their synapses during a decision cycle spanned by oscillatory inhibition and excitation in the gamma range
Summary
Eighteenth Annual Computational Neuroscience Meeting: CNS*2009 Don H Johnson Meeting abstracts – A single PDF containing all abstracts in this Supplement is available here. http://www.biomedcentral.com/content/pdf/1471-2202-10-S1-info.pdf . Email: Jenia Jitsev* - jitsev@fias.uni-frankfurt.de * Corresponding author from Eighteenth Annual Computational Neuroscience Meeting: CNS*2009 Berlin, Germany. Published: 13 July 2009 BMC Neuroscience 2009, 10(Suppl 1):P207 doi:10.1186/1471-2202-10-S1-P207
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