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Front Neuroanat. 2010 Jun 2;4:17. doi: 10.3389/fnana.2010.00017. eCollection 2010.

A cortical sparse distributed coding model linking mini- and macrocolumn-scale functionality.

Author information

  • 1Biology Department, Volen Center for Complex Systems, Brandeis University Waltham, MA, USA.

Abstract

No generic function for the minicolumn - i.e., one that would apply equally well to all cortical areas and species - has yet been proposed. I propose that the minicolumn does have a generic functionality, which only becomes clear when seen in the context of the function of the higher-level, subsuming unit, the macrocolumn. I propose that: (a) a macrocolumn's function is to store sparse distributed representations of its inputs and to be a recognizer of those inputs; and (b) the generic function of the minicolumn is to enforce macrocolumnar code sparseness. The minicolumn, defined here as a physically localized pool of approximately 20 L2/3 pyramidals, does this by acting as a winner-take-all (WTA) competitive module, implying that macrocolumnar codes consist of approximately 70 active L2/3 cells, assuming approximately 70 minicolumns per macrocolumn. I describe an algorithm for activating these codes during both learning and retrievals, which causes more similar inputs to map to more highly intersecting codes, a property which yields ultra-fast (immediate, first-shot) storage and retrieval. The algorithm achieves this by adding an amount of randomness (noise) into the code selection process, which is inversely proportional to an input's familiarity. I propose a possible mapping of the algorithm onto cortical circuitry, and adduce evidence for a neuromodulatory implementation of this familiarity-contingent noise mechanism. The model is distinguished from other recent columnar cortical circuit models in proposing a generic minicolumnar function in which a group of cells within the minicolumn, the L2/3 pyramidals, compete (WTA) to be part of the sparse distributed macrocolumnar code.

KEYWORDS:

learning; macrocolumn; memory; minicolumn; novelty detection; population coding; sparse distributed representations; winner-take-all

PMID:
20577587
PMCID:
PMC2889687
DOI:
10.3389/fnana.2010.00017
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