A neural network model based on the analogy with the immune system
The similarities between the immune system and the central nervous system lead to the formulation of an unorthodox neural network model. The similarities between the two systems are strong at the system level, but do not seem to be so striking at the level of the components. A new model of a neuron...
Ausführliche Beschreibung
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| Autor*in: |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
1986 |
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| Reproduktion: |
Elsevier Journal Backfiles on ScienceDirect 1907 - 2002 |
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| Übergeordnetes Werk: |
in: Journal of Theoretical Biology - Amsterdam : Elsevier, 122(1986), 1, Seite 33-67 |
| Übergeordnetes Werk: |
volume:122 ; year:1986 ; number:1 ; pages:33-67 |
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| 520 | |a The similarities between the immune system and the central nervous system lead to the formulation of an unorthodox neural network model. The similarities between the two systems are strong at the system level, but do not seem to be so striking at the level of the components. A new model of a neuron is therefore formulated, in order that the analogy can be used. The essential feature of the hypothetical neuron is that it exhibits hysteresis at the single neuron level. A network of N such neurons is modelled by an N-dimensional system of ordinary differential equations, which exhibits almost 2^N attractors. The model has a property that resembles free will. A conjecture concerning how the network might learn stimulus-response behaviour is described. According to the conjecture, learning does not involve modifications of the strengths of synaptic connections. Instead, stimuli (''questions'') selectively applied to the network by a ''teacher'' can be used to take the system to a region of the N-dimensional phase space where the network gives the desired stimulus-response behaviour. A key role for sleep in the learning process is suggested. The model for sleep leads to prediction that the variance in the rates of firing of the neurons associated with memory should increase during waking hours, and decrease during sleep. | ||
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(DE-627)NLEJ184817978 (DE-599)GBVNLZ184817978 DE-627 ger DE-627 rakwb eng A neural network model based on the analogy with the immune system 1986 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The similarities between the immune system and the central nervous system lead to the formulation of an unorthodox neural network model. The similarities between the two systems are strong at the system level, but do not seem to be so striking at the level of the components. A new model of a neuron is therefore formulated, in order that the analogy can be used. The essential feature of the hypothetical neuron is that it exhibits hysteresis at the single neuron level. A network of N such neurons is modelled by an N-dimensional system of ordinary differential equations, which exhibits almost 2^N attractors. The model has a property that resembles free will. A conjecture concerning how the network might learn stimulus-response behaviour is described. According to the conjecture, learning does not involve modifications of the strengths of synaptic connections. Instead, stimuli (''questions'') selectively applied to the network by a ''teacher'' can be used to take the system to a region of the N-dimensional phase space where the network gives the desired stimulus-response behaviour. A key role for sleep in the learning process is suggested. The model for sleep leads to prediction that the variance in the rates of firing of the neurons associated with memory should increase during waking hours, and decrease during sleep. Elsevier Journal Backfiles on ScienceDirect 1907 - 2002 Hoffmann, G.W. oth in Journal of Theoretical Biology Amsterdam : Elsevier 122(1986), 1, Seite 33-67 (DE-627)NLEJ176864393 (DE-600)1470953-3 0022-5193 nnns volume:122 year:1986 number:1 pages:33-67 http://dx.doi.org/10.1016/S0022-5193(86)80224-7 GBV_USEFLAG_H ZDB-1-SDJ GBV_NL_ARTICLE AR 122 1986 1 33-67 |
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(DE-627)NLEJ184817978 (DE-599)GBVNLZ184817978 DE-627 ger DE-627 rakwb eng A neural network model based on the analogy with the immune system 1986 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The similarities between the immune system and the central nervous system lead to the formulation of an unorthodox neural network model. The similarities between the two systems are strong at the system level, but do not seem to be so striking at the level of the components. A new model of a neuron is therefore formulated, in order that the analogy can be used. The essential feature of the hypothetical neuron is that it exhibits hysteresis at the single neuron level. A network of N such neurons is modelled by an N-dimensional system of ordinary differential equations, which exhibits almost 2^N attractors. The model has a property that resembles free will. A conjecture concerning how the network might learn stimulus-response behaviour is described. According to the conjecture, learning does not involve modifications of the strengths of synaptic connections. Instead, stimuli (''questions'') selectively applied to the network by a ''teacher'' can be used to take the system to a region of the N-dimensional phase space where the network gives the desired stimulus-response behaviour. A key role for sleep in the learning process is suggested. The model for sleep leads to prediction that the variance in the rates of firing of the neurons associated with memory should increase during waking hours, and decrease during sleep. Elsevier Journal Backfiles on ScienceDirect 1907 - 2002 Hoffmann, G.W. oth in Journal of Theoretical Biology Amsterdam : Elsevier 122(1986), 1, Seite 33-67 (DE-627)NLEJ176864393 (DE-600)1470953-3 0022-5193 nnns volume:122 year:1986 number:1 pages:33-67 http://dx.doi.org/10.1016/S0022-5193(86)80224-7 GBV_USEFLAG_H ZDB-1-SDJ GBV_NL_ARTICLE AR 122 1986 1 33-67 |
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(DE-627)NLEJ184817978 (DE-599)GBVNLZ184817978 DE-627 ger DE-627 rakwb eng A neural network model based on the analogy with the immune system 1986 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The similarities between the immune system and the central nervous system lead to the formulation of an unorthodox neural network model. The similarities between the two systems are strong at the system level, but do not seem to be so striking at the level of the components. A new model of a neuron is therefore formulated, in order that the analogy can be used. The essential feature of the hypothetical neuron is that it exhibits hysteresis at the single neuron level. A network of N such neurons is modelled by an N-dimensional system of ordinary differential equations, which exhibits almost 2^N attractors. The model has a property that resembles free will. A conjecture concerning how the network might learn stimulus-response behaviour is described. According to the conjecture, learning does not involve modifications of the strengths of synaptic connections. Instead, stimuli (''questions'') selectively applied to the network by a ''teacher'' can be used to take the system to a region of the N-dimensional phase space where the network gives the desired stimulus-response behaviour. A key role for sleep in the learning process is suggested. The model for sleep leads to prediction that the variance in the rates of firing of the neurons associated with memory should increase during waking hours, and decrease during sleep. Elsevier Journal Backfiles on ScienceDirect 1907 - 2002 Hoffmann, G.W. oth in Journal of Theoretical Biology Amsterdam : Elsevier 122(1986), 1, Seite 33-67 (DE-627)NLEJ176864393 (DE-600)1470953-3 0022-5193 nnns volume:122 year:1986 number:1 pages:33-67 http://dx.doi.org/10.1016/S0022-5193(86)80224-7 GBV_USEFLAG_H ZDB-1-SDJ GBV_NL_ARTICLE AR 122 1986 1 33-67 |
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(DE-627)NLEJ184817978 (DE-599)GBVNLZ184817978 DE-627 ger DE-627 rakwb eng A neural network model based on the analogy with the immune system 1986 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The similarities between the immune system and the central nervous system lead to the formulation of an unorthodox neural network model. The similarities between the two systems are strong at the system level, but do not seem to be so striking at the level of the components. A new model of a neuron is therefore formulated, in order that the analogy can be used. The essential feature of the hypothetical neuron is that it exhibits hysteresis at the single neuron level. A network of N such neurons is modelled by an N-dimensional system of ordinary differential equations, which exhibits almost 2^N attractors. The model has a property that resembles free will. A conjecture concerning how the network might learn stimulus-response behaviour is described. According to the conjecture, learning does not involve modifications of the strengths of synaptic connections. Instead, stimuli (''questions'') selectively applied to the network by a ''teacher'' can be used to take the system to a region of the N-dimensional phase space where the network gives the desired stimulus-response behaviour. A key role for sleep in the learning process is suggested. The model for sleep leads to prediction that the variance in the rates of firing of the neurons associated with memory should increase during waking hours, and decrease during sleep. Elsevier Journal Backfiles on ScienceDirect 1907 - 2002 Hoffmann, G.W. oth in Journal of Theoretical Biology Amsterdam : Elsevier 122(1986), 1, Seite 33-67 (DE-627)NLEJ176864393 (DE-600)1470953-3 0022-5193 nnns volume:122 year:1986 number:1 pages:33-67 http://dx.doi.org/10.1016/S0022-5193(86)80224-7 GBV_USEFLAG_H ZDB-1-SDJ GBV_NL_ARTICLE AR 122 1986 1 33-67 |
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(DE-627)NLEJ184817978 (DE-599)GBVNLZ184817978 DE-627 ger DE-627 rakwb eng A neural network model based on the analogy with the immune system 1986 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The similarities between the immune system and the central nervous system lead to the formulation of an unorthodox neural network model. The similarities between the two systems are strong at the system level, but do not seem to be so striking at the level of the components. A new model of a neuron is therefore formulated, in order that the analogy can be used. The essential feature of the hypothetical neuron is that it exhibits hysteresis at the single neuron level. A network of N such neurons is modelled by an N-dimensional system of ordinary differential equations, which exhibits almost 2^N attractors. The model has a property that resembles free will. A conjecture concerning how the network might learn stimulus-response behaviour is described. According to the conjecture, learning does not involve modifications of the strengths of synaptic connections. Instead, stimuli (''questions'') selectively applied to the network by a ''teacher'' can be used to take the system to a region of the N-dimensional phase space where the network gives the desired stimulus-response behaviour. A key role for sleep in the learning process is suggested. The model for sleep leads to prediction that the variance in the rates of firing of the neurons associated with memory should increase during waking hours, and decrease during sleep. Elsevier Journal Backfiles on ScienceDirect 1907 - 2002 Hoffmann, G.W. oth in Journal of Theoretical Biology Amsterdam : Elsevier 122(1986), 1, Seite 33-67 (DE-627)NLEJ176864393 (DE-600)1470953-3 0022-5193 nnns volume:122 year:1986 number:1 pages:33-67 http://dx.doi.org/10.1016/S0022-5193(86)80224-7 GBV_USEFLAG_H ZDB-1-SDJ GBV_NL_ARTICLE AR 122 1986 1 33-67 |
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The similarities between the immune system and the central nervous system lead to the formulation of an unorthodox neural network model. The similarities between the two systems are strong at the system level, but do not seem to be so striking at the level of the components. A new model of a neuron is therefore formulated, in order that the analogy can be used. The essential feature of the hypothetical neuron is that it exhibits hysteresis at the single neuron level. A network of N such neurons is modelled by an N-dimensional system of ordinary differential equations, which exhibits almost 2^N attractors. The model has a property that resembles free will. A conjecture concerning how the network might learn stimulus-response behaviour is described. According to the conjecture, learning does not involve modifications of the strengths of synaptic connections. Instead, stimuli (''questions'') selectively applied to the network by a ''teacher'' can be used to take the system to a region of the N-dimensional phase space where the network gives the desired stimulus-response behaviour. A key role for sleep in the learning process is suggested. The model for sleep leads to prediction that the variance in the rates of firing of the neurons associated with memory should increase during waking hours, and decrease during sleep. |
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The similarities between the immune system and the central nervous system lead to the formulation of an unorthodox neural network model. The similarities between the two systems are strong at the system level, but do not seem to be so striking at the level of the components. A new model of a neuron is therefore formulated, in order that the analogy can be used. The essential feature of the hypothetical neuron is that it exhibits hysteresis at the single neuron level. A network of N such neurons is modelled by an N-dimensional system of ordinary differential equations, which exhibits almost 2^N attractors. The model has a property that resembles free will. A conjecture concerning how the network might learn stimulus-response behaviour is described. According to the conjecture, learning does not involve modifications of the strengths of synaptic connections. Instead, stimuli (''questions'') selectively applied to the network by a ''teacher'' can be used to take the system to a region of the N-dimensional phase space where the network gives the desired stimulus-response behaviour. A key role for sleep in the learning process is suggested. The model for sleep leads to prediction that the variance in the rates of firing of the neurons associated with memory should increase during waking hours, and decrease during sleep. |
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The similarities between the immune system and the central nervous system lead to the formulation of an unorthodox neural network model. The similarities between the two systems are strong at the system level, but do not seem to be so striking at the level of the components. A new model of a neuron is therefore formulated, in order that the analogy can be used. The essential feature of the hypothetical neuron is that it exhibits hysteresis at the single neuron level. A network of N such neurons is modelled by an N-dimensional system of ordinary differential equations, which exhibits almost 2^N attractors. The model has a property that resembles free will. A conjecture concerning how the network might learn stimulus-response behaviour is described. According to the conjecture, learning does not involve modifications of the strengths of synaptic connections. Instead, stimuli (''questions'') selectively applied to the network by a ''teacher'' can be used to take the system to a region of the N-dimensional phase space where the network gives the desired stimulus-response behaviour. A key role for sleep in the learning process is suggested. The model for sleep leads to prediction that the variance in the rates of firing of the neurons associated with memory should increase during waking hours, and decrease during sleep. |
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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">NLEJ184817978</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20210707001318.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">070506s1986 xx |||||o 00| ||eng c</controlfield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)NLEJ184817978</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVNLZ184817978</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="245" ind1="1" ind2="2"><subfield code="a">A neural network model based on the analogy with the immune system</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">1986</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The similarities between the immune system and the central nervous system lead to the formulation of an unorthodox neural network model. The similarities between the two systems are strong at the system level, but do not seem to be so striking at the level of the components. A new model of a neuron is therefore formulated, in order that the analogy can be used. The essential feature of the hypothetical neuron is that it exhibits hysteresis at the single neuron level. A network of N such neurons is modelled by an N-dimensional system of ordinary differential equations, which exhibits almost 2^N attractors. The model has a property that resembles free will. A conjecture concerning how the network might learn stimulus-response behaviour is described. According to the conjecture, learning does not involve modifications of the strengths of synaptic connections. Instead, stimuli (''questions'') selectively applied to the network by a ''teacher'' can be used to take the system to a region of the N-dimensional phase space where the network gives the desired stimulus-response behaviour. A key role for sleep in the learning process is suggested. The model for sleep leads to prediction that the variance in the rates of firing of the neurons associated with memory should increase during waking hours, and decrease during sleep.</subfield></datafield><datafield tag="533" ind1=" " ind2=" "><subfield code="f">Elsevier Journal Backfiles on ScienceDirect 1907 - 2002</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hoffmann, G.W.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">in</subfield><subfield code="t">Journal of Theoretical Biology</subfield><subfield code="d">Amsterdam : Elsevier</subfield><subfield code="g">122(1986), 1, Seite 33-67</subfield><subfield code="w">(DE-627)NLEJ176864393</subfield><subfield code="w">(DE-600)1470953-3</subfield><subfield code="x">0022-5193</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:122</subfield><subfield code="g">year:1986</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:33-67</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://dx.doi.org/10.1016/S0022-5193(86)80224-7</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_H</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-1-SDJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_NL_ARTICLE</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">122</subfield><subfield code="j">1986</subfield><subfield code="e">1</subfield><subfield code="h">33-67</subfield></datafield></record></collection>
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