Theoretical Note Parsimony in neural representations: Generalization of a model of spatial orientation ability
Abstract Brains must be adequate to process complex information, but they must also have a simple enough underlying organization to have evolved by natural selection. Beginning with a network model, originally developed to show how spatial orientational behavior might be organized with a modest numb...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Glassman, Robert B. [verfasserIn] |
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| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
1985 |
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| Schlagwörter: |
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| Anmerkung: |
© Psychonomic Society, Inc. 1985 |
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| Übergeordnetes Werk: |
Enthalten in: Physiological Psychology - Springer-Verlag, 1973, 13(1985), 1 vom: März, Seite 43-47 |
|---|---|
| Übergeordnetes Werk: |
volume:13 ; year:1985 ; number:1 ; month:03 ; pages:43-47 |
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10.3758/BF03326494 |
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| 520 | |a Abstract Brains must be adequate to process complex information, but they must also have a simple enough underlying organization to have evolved by natural selection. Beginning with a network model, originally developed to show how spatial orientational behavior might be organized with a modest number of neural connections, the following hypothesis is offered about a pattern of connections recurring within the brain: All significant activation patterns of a large number of input elements are transformed to output patterns by small numbers of associative subsystems, or modules, which do not require computerlike algorithms. They are single neurons or other functional units, which individually merely summate inputs; together, they discriminate among probable inputs without requiring a complex representation to do so. They may be thought of as registering (1) values of input dimensions, (2) combinations of activated input elements, or (3) numerical labels for distinct inputs. The informational capacity of a set of modules is a function of its number of modules and their dynamic range. The set works most efficiently if all its modules have the same range. Elementary combinatorial considerations suggest that besides receiving patterns of connections appropriate to the information being processed, sets of associative modules probably receive inputs systematically restricted in more general ways, for example by lateral inhibition or by connections from a small or large, but not intermediate, proportion of the set of input modules. | ||
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10.3758/BF03326494 doi (DE-627)SPR037014005 (SPR)BF03326494-e DE-627 ger DE-627 rakwb eng Glassman, Robert B. verfasserin aut Theoretical Note Parsimony in neural representations: Generalization of a model of spatial orientation ability 1985 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Psychonomic Society, Inc. 1985 Abstract Brains must be adequate to process complex information, but they must also have a simple enough underlying organization to have evolved by natural selection. Beginning with a network model, originally developed to show how spatial orientational behavior might be organized with a modest number of neural connections, the following hypothesis is offered about a pattern of connections recurring within the brain: All significant activation patterns of a large number of input elements are transformed to output patterns by small numbers of associative subsystems, or modules, which do not require computerlike algorithms. They are single neurons or other functional units, which individually merely summate inputs; together, they discriminate among probable inputs without requiring a complex representation to do so. They may be thought of as registering (1) values of input dimensions, (2) combinations of activated input elements, or (3) numerical labels for distinct inputs. The informational capacity of a set of modules is a function of its number of modules and their dynamic range. The set works most efficiently if all its modules have the same range. Elementary combinatorial considerations suggest that besides receiving patterns of connections appropriate to the information being processed, sets of associative modules probably receive inputs systematically restricted in more general ways, for example by lateral inhibition or by connections from a small or large, but not intermediate, proportion of the set of input modules. Associative Memory (dpeaa)DE-He213 Input Module (dpeaa)DE-He213 Input Element (dpeaa)DE-He213 Associative System (dpeaa)DE-He213 Distinct Input (dpeaa)DE-He213 Enthalten in Physiological Psychology Springer-Verlag, 1973 13(1985), 1 vom: März, Seite 43-47 (DE-627)SPR037003089 nnns volume:13 year:1985 number:1 month:03 pages:43-47 https://dx.doi.org/10.3758/BF03326494 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 13 1985 1 03 43-47 |
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10.3758/BF03326494 doi (DE-627)SPR037014005 (SPR)BF03326494-e DE-627 ger DE-627 rakwb eng Glassman, Robert B. verfasserin aut Theoretical Note Parsimony in neural representations: Generalization of a model of spatial orientation ability 1985 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Psychonomic Society, Inc. 1985 Abstract Brains must be adequate to process complex information, but they must also have a simple enough underlying organization to have evolved by natural selection. Beginning with a network model, originally developed to show how spatial orientational behavior might be organized with a modest number of neural connections, the following hypothesis is offered about a pattern of connections recurring within the brain: All significant activation patterns of a large number of input elements are transformed to output patterns by small numbers of associative subsystems, or modules, which do not require computerlike algorithms. They are single neurons or other functional units, which individually merely summate inputs; together, they discriminate among probable inputs without requiring a complex representation to do so. They may be thought of as registering (1) values of input dimensions, (2) combinations of activated input elements, or (3) numerical labels for distinct inputs. The informational capacity of a set of modules is a function of its number of modules and their dynamic range. The set works most efficiently if all its modules have the same range. Elementary combinatorial considerations suggest that besides receiving patterns of connections appropriate to the information being processed, sets of associative modules probably receive inputs systematically restricted in more general ways, for example by lateral inhibition or by connections from a small or large, but not intermediate, proportion of the set of input modules. Associative Memory (dpeaa)DE-He213 Input Module (dpeaa)DE-He213 Input Element (dpeaa)DE-He213 Associative System (dpeaa)DE-He213 Distinct Input (dpeaa)DE-He213 Enthalten in Physiological Psychology Springer-Verlag, 1973 13(1985), 1 vom: März, Seite 43-47 (DE-627)SPR037003089 nnns volume:13 year:1985 number:1 month:03 pages:43-47 https://dx.doi.org/10.3758/BF03326494 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 13 1985 1 03 43-47 |
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10.3758/BF03326494 doi (DE-627)SPR037014005 (SPR)BF03326494-e DE-627 ger DE-627 rakwb eng Glassman, Robert B. verfasserin aut Theoretical Note Parsimony in neural representations: Generalization of a model of spatial orientation ability 1985 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Psychonomic Society, Inc. 1985 Abstract Brains must be adequate to process complex information, but they must also have a simple enough underlying organization to have evolved by natural selection. Beginning with a network model, originally developed to show how spatial orientational behavior might be organized with a modest number of neural connections, the following hypothesis is offered about a pattern of connections recurring within the brain: All significant activation patterns of a large number of input elements are transformed to output patterns by small numbers of associative subsystems, or modules, which do not require computerlike algorithms. They are single neurons or other functional units, which individually merely summate inputs; together, they discriminate among probable inputs without requiring a complex representation to do so. They may be thought of as registering (1) values of input dimensions, (2) combinations of activated input elements, or (3) numerical labels for distinct inputs. The informational capacity of a set of modules is a function of its number of modules and their dynamic range. The set works most efficiently if all its modules have the same range. Elementary combinatorial considerations suggest that besides receiving patterns of connections appropriate to the information being processed, sets of associative modules probably receive inputs systematically restricted in more general ways, for example by lateral inhibition or by connections from a small or large, but not intermediate, proportion of the set of input modules. Associative Memory (dpeaa)DE-He213 Input Module (dpeaa)DE-He213 Input Element (dpeaa)DE-He213 Associative System (dpeaa)DE-He213 Distinct Input (dpeaa)DE-He213 Enthalten in Physiological Psychology Springer-Verlag, 1973 13(1985), 1 vom: März, Seite 43-47 (DE-627)SPR037003089 nnns volume:13 year:1985 number:1 month:03 pages:43-47 https://dx.doi.org/10.3758/BF03326494 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 13 1985 1 03 43-47 |
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10.3758/BF03326494 doi (DE-627)SPR037014005 (SPR)BF03326494-e DE-627 ger DE-627 rakwb eng Glassman, Robert B. verfasserin aut Theoretical Note Parsimony in neural representations: Generalization of a model of spatial orientation ability 1985 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Psychonomic Society, Inc. 1985 Abstract Brains must be adequate to process complex information, but they must also have a simple enough underlying organization to have evolved by natural selection. Beginning with a network model, originally developed to show how spatial orientational behavior might be organized with a modest number of neural connections, the following hypothesis is offered about a pattern of connections recurring within the brain: All significant activation patterns of a large number of input elements are transformed to output patterns by small numbers of associative subsystems, or modules, which do not require computerlike algorithms. They are single neurons or other functional units, which individually merely summate inputs; together, they discriminate among probable inputs without requiring a complex representation to do so. They may be thought of as registering (1) values of input dimensions, (2) combinations of activated input elements, or (3) numerical labels for distinct inputs. The informational capacity of a set of modules is a function of its number of modules and their dynamic range. The set works most efficiently if all its modules have the same range. Elementary combinatorial considerations suggest that besides receiving patterns of connections appropriate to the information being processed, sets of associative modules probably receive inputs systematically restricted in more general ways, for example by lateral inhibition or by connections from a small or large, but not intermediate, proportion of the set of input modules. Associative Memory (dpeaa)DE-He213 Input Module (dpeaa)DE-He213 Input Element (dpeaa)DE-He213 Associative System (dpeaa)DE-He213 Distinct Input (dpeaa)DE-He213 Enthalten in Physiological Psychology Springer-Verlag, 1973 13(1985), 1 vom: März, Seite 43-47 (DE-627)SPR037003089 nnns volume:13 year:1985 number:1 month:03 pages:43-47 https://dx.doi.org/10.3758/BF03326494 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 13 1985 1 03 43-47 |
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Abstract Brains must be adequate to process complex information, but they must also have a simple enough underlying organization to have evolved by natural selection. Beginning with a network model, originally developed to show how spatial orientational behavior might be organized with a modest number of neural connections, the following hypothesis is offered about a pattern of connections recurring within the brain: All significant activation patterns of a large number of input elements are transformed to output patterns by small numbers of associative subsystems, or modules, which do not require computerlike algorithms. They are single neurons or other functional units, which individually merely summate inputs; together, they discriminate among probable inputs without requiring a complex representation to do so. They may be thought of as registering (1) values of input dimensions, (2) combinations of activated input elements, or (3) numerical labels for distinct inputs. The informational capacity of a set of modules is a function of its number of modules and their dynamic range. The set works most efficiently if all its modules have the same range. Elementary combinatorial considerations suggest that besides receiving patterns of connections appropriate to the information being processed, sets of associative modules probably receive inputs systematically restricted in more general ways, for example by lateral inhibition or by connections from a small or large, but not intermediate, proportion of the set of input modules. © Psychonomic Society, Inc. 1985 |
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Abstract Brains must be adequate to process complex information, but they must also have a simple enough underlying organization to have evolved by natural selection. Beginning with a network model, originally developed to show how spatial orientational behavior might be organized with a modest number of neural connections, the following hypothesis is offered about a pattern of connections recurring within the brain: All significant activation patterns of a large number of input elements are transformed to output patterns by small numbers of associative subsystems, or modules, which do not require computerlike algorithms. They are single neurons or other functional units, which individually merely summate inputs; together, they discriminate among probable inputs without requiring a complex representation to do so. They may be thought of as registering (1) values of input dimensions, (2) combinations of activated input elements, or (3) numerical labels for distinct inputs. The informational capacity of a set of modules is a function of its number of modules and their dynamic range. The set works most efficiently if all its modules have the same range. Elementary combinatorial considerations suggest that besides receiving patterns of connections appropriate to the information being processed, sets of associative modules probably receive inputs systematically restricted in more general ways, for example by lateral inhibition or by connections from a small or large, but not intermediate, proportion of the set of input modules. © Psychonomic Society, Inc. 1985 |
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Abstract Brains must be adequate to process complex information, but they must also have a simple enough underlying organization to have evolved by natural selection. Beginning with a network model, originally developed to show how spatial orientational behavior might be organized with a modest number of neural connections, the following hypothesis is offered about a pattern of connections recurring within the brain: All significant activation patterns of a large number of input elements are transformed to output patterns by small numbers of associative subsystems, or modules, which do not require computerlike algorithms. They are single neurons or other functional units, which individually merely summate inputs; together, they discriminate among probable inputs without requiring a complex representation to do so. They may be thought of as registering (1) values of input dimensions, (2) combinations of activated input elements, or (3) numerical labels for distinct inputs. The informational capacity of a set of modules is a function of its number of modules and their dynamic range. The set works most efficiently if all its modules have the same range. Elementary combinatorial considerations suggest that besides receiving patterns of connections appropriate to the information being processed, sets of associative modules probably receive inputs systematically restricted in more general ways, for example by lateral inhibition or by connections from a small or large, but not intermediate, proportion of the set of input modules. © Psychonomic Society, Inc. 1985 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR037014005</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230328181458.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s1985 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3758/BF03326494</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR037014005</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)BF03326494-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Glassman, Robert B.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Theoretical Note Parsimony in neural representations: Generalization of a model of spatial orientation ability</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">1985</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Psychonomic Society, Inc. 1985</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Brains must be adequate to process complex information, but they must also have a simple enough underlying organization to have evolved by natural selection. Beginning with a network model, originally developed to show how spatial orientational behavior might be organized with a modest number of neural connections, the following hypothesis is offered about a pattern of connections recurring within the brain: All significant activation patterns of a large number of input elements are transformed to output patterns by small numbers of associative subsystems, or modules, which do not require computerlike algorithms. They are single neurons or other functional units, which individually merely summate inputs; together, they discriminate among probable inputs without requiring a complex representation to do so. They may be thought of as registering (1) values of input dimensions, (2) combinations of activated input elements, or (3) numerical labels for distinct inputs. The informational capacity of a set of modules is a function of its number of modules and their dynamic range. The set works most efficiently if all its modules have the same range. Elementary combinatorial considerations suggest that besides receiving patterns of connections appropriate to the information being processed, sets of associative modules probably receive inputs systematically restricted in more general ways, for example by lateral inhibition or by connections from a small or large, but not intermediate, proportion of the set of input modules.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Associative Memory</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Input Module</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Input Element</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Associative System</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Distinct Input</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Physiological Psychology</subfield><subfield code="d">Springer-Verlag, 1973</subfield><subfield code="g">13(1985), 1 vom: März, Seite 43-47</subfield><subfield code="w">(DE-627)SPR037003089</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:13</subfield><subfield code="g">year:1985</subfield><subfield code="g">number:1</subfield><subfield code="g">month:03</subfield><subfield code="g">pages:43-47</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.3758/BF03326494</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">13</subfield><subfield code="j">1985</subfield><subfield code="e">1</subfield><subfield code="c">03</subfield><subfield code="h">43-47</subfield></datafield></record></collection>
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