Intrinsic gain modulation and adaptive neural coding.
In many cases, the computation of a neural system can be reduced to a receptive field, or a set of linear filters, and a thresholding function, or gain curve, which determines the firing probability; this is known as a linear/nonlinear model. In some forms of sensory adaptation, these linear filters...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2008-07-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/18636100/?tool=EBI |
| _version_ | 1818836164685070336 |
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| author | Sungho Hong Brian Nils Lundstrom Adrienne L Fairhall |
| author_facet | Sungho Hong Brian Nils Lundstrom Adrienne L Fairhall |
| author_sort | Sungho Hong |
| collection | DOAJ |
| description | In many cases, the computation of a neural system can be reduced to a receptive field, or a set of linear filters, and a thresholding function, or gain curve, which determines the firing probability; this is known as a linear/nonlinear model. In some forms of sensory adaptation, these linear filters and gain curve adjust very rapidly to changes in the variance of a randomly varying driving input. An apparently similar but previously unrelated issue is the observation of gain control by background noise in cortical neurons: the slope of the firing rate versus current (f-I) curve changes with the variance of background random input. Here, we show a direct correspondence between these two observations by relating variance-dependent changes in the gain of f-I curves to characteristics of the changing empirical linear/nonlinear model obtained by sampling. In the case that the underlying system is fixed, we derive relationships relating the change of the gain with respect to both mean and variance with the receptive fields derived from reverse correlation on a white noise stimulus. Using two conductance-based model neurons that display distinct gain modulation properties through a simple change in parameters, we show that coding properties of both these models quantitatively satisfy the predicted relationships. Our results describe how both variance-dependent gain modulation and adaptive neural computation result from intrinsic nonlinearity. |
| first_indexed | 2024-12-19T03:02:15Z |
| format | Article |
| id | doaj.art-132cd1d45fe4470fbbda6478267ed743 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-19T03:02:15Z |
| publishDate | 2008-07-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-132cd1d45fe4470fbbda6478267ed7432022-12-21T20:38:10ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582008-07-0147e100011910.1371/journal.pcbi.1000119Intrinsic gain modulation and adaptive neural coding.Sungho HongBrian Nils LundstromAdrienne L FairhallIn many cases, the computation of a neural system can be reduced to a receptive field, or a set of linear filters, and a thresholding function, or gain curve, which determines the firing probability; this is known as a linear/nonlinear model. In some forms of sensory adaptation, these linear filters and gain curve adjust very rapidly to changes in the variance of a randomly varying driving input. An apparently similar but previously unrelated issue is the observation of gain control by background noise in cortical neurons: the slope of the firing rate versus current (f-I) curve changes with the variance of background random input. Here, we show a direct correspondence between these two observations by relating variance-dependent changes in the gain of f-I curves to characteristics of the changing empirical linear/nonlinear model obtained by sampling. In the case that the underlying system is fixed, we derive relationships relating the change of the gain with respect to both mean and variance with the receptive fields derived from reverse correlation on a white noise stimulus. Using two conductance-based model neurons that display distinct gain modulation properties through a simple change in parameters, we show that coding properties of both these models quantitatively satisfy the predicted relationships. Our results describe how both variance-dependent gain modulation and adaptive neural computation result from intrinsic nonlinearity.https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/18636100/?tool=EBI |
| spellingShingle | Sungho Hong Brian Nils Lundstrom Adrienne L Fairhall Intrinsic gain modulation and adaptive neural coding. PLoS Computational Biology |
| title | Intrinsic gain modulation and adaptive neural coding. |
| title_full | Intrinsic gain modulation and adaptive neural coding. |
| title_fullStr | Intrinsic gain modulation and adaptive neural coding. |
| title_full_unstemmed | Intrinsic gain modulation and adaptive neural coding. |
| title_short | Intrinsic gain modulation and adaptive neural coding. |
| title_sort | intrinsic gain modulation and adaptive neural coding |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/18636100/?tool=EBI |
| work_keys_str_mv | AT sunghohong intrinsicgainmodulationandadaptiveneuralcoding AT briannilslundstrom intrinsicgainmodulationandadaptiveneuralcoding AT adriennelfairhall intrinsicgainmodulationandadaptiveneuralcoding |