A cortical sparse distributed coding model linking mini- and macrocolumn-scale functionality
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 highe...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2010-06-01
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| Series: | Frontiers in Neuroanatomy |
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| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fnana.2010.00017/full |
| _version_ | 1818419239646658560 |
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| author | Gerard J Rinkus |
| author_facet | Gerard J Rinkus |
| author_sort | Gerard J Rinkus |
| collection | DOAJ |
| description | 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 ~20 L2/3 pyramidals, does this by acting as a winner-take-all (WTA) competitive module, implying that macrocolumnar codes consist of ~70 active L2/3 cells, assuming ~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 macrocolumnar code. |
| first_indexed | 2024-12-14T12:35:25Z |
| format | Article |
| id | doaj.art-2f424bd2609449e88c59a002acf51093 |
| institution | Directory Open Access Journal |
| issn | 1662-5129 |
| language | English |
| last_indexed | 2024-12-14T12:35:25Z |
| publishDate | 2010-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroanatomy |
| spelling | doaj.art-2f424bd2609449e88c59a002acf510932022-12-21T23:01:03ZengFrontiers Media S.A.Frontiers in Neuroanatomy1662-51292010-06-01410.3389/fnana.2010.000171235A cortical sparse distributed coding model linking mini- and macrocolumn-scale functionalityGerard J Rinkus0Brandeis UniversityNo 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 ~20 L2/3 pyramidals, does this by acting as a winner-take-all (WTA) competitive module, implying that macrocolumnar codes consist of ~70 active L2/3 cells, assuming ~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 macrocolumnar code.http://journal.frontiersin.org/Journal/10.3389/fnana.2010.00017/fullLearningMemorynovelty detectionNeuromodulatorpopulation codingmacrocolumn |
| spellingShingle | Gerard J Rinkus A cortical sparse distributed coding model linking mini- and macrocolumn-scale functionality Frontiers in Neuroanatomy Learning Memory novelty detection Neuromodulator population coding macrocolumn |
| title | A cortical sparse distributed coding model linking mini- and macrocolumn-scale functionality |
| title_full | A cortical sparse distributed coding model linking mini- and macrocolumn-scale functionality |
| title_fullStr | A cortical sparse distributed coding model linking mini- and macrocolumn-scale functionality |
| title_full_unstemmed | A cortical sparse distributed coding model linking mini- and macrocolumn-scale functionality |
| title_short | A cortical sparse distributed coding model linking mini- and macrocolumn-scale functionality |
| title_sort | cortical sparse distributed coding model linking mini and macrocolumn scale functionality |
| topic | Learning Memory novelty detection Neuromodulator population coding macrocolumn |
| url | http://journal.frontiersin.org/Journal/10.3389/fnana.2010.00017/full |
| work_keys_str_mv | AT gerardjrinkus acorticalsparsedistributedcodingmodellinkingminiandmacrocolumnscalefunctionality AT gerardjrinkus corticalsparsedistributedcodingmodellinkingminiandmacrocolumnscalefunctionality |