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Power-law input-output transfer functions explain the contrast-response and tuning properties of neurons in visual cortex.

Persi E, Hansel D, Nowak L, Barone P, van Vreeswijk C - PLoS Comput. Biol. (2011)

Bottom Line: We test these results with numerical simulations of a network of conductance-based model (CBM) neurons and we demonstrate that they are valid and more robust here than in the rate model.The results indicate that because of the acceleration in the transfer function, described here by a power-law, the orientation tuning curves of V1 neurons are more tuned, and their CRF is steeper than those of their inputs.Comparison with experimental data suggests that both sources contribute nearly equally to the diversity of CRF shapes observed in V1 neurons.

View Article: PubMed Central - PubMed

Affiliation: Laboratoire de Neurophysique et Physiologie, Université Paris Descartes, Paris, France.

ABSTRACT
We develop a unified model accounting simultaneously for the contrast invariance of the width of the orientation tuning curves (OT) and for the sigmoidal shape of the contrast response function (CRF) of neurons in the primary visual cortex (V1). We determine analytically the conditions for the structure of the afferent LGN and recurrent V1 inputs that lead to these properties for a hypercolumn composed of rate based neurons with a power-law transfer function. We investigate what are the relative contributions of single neuron and network properties in shaping the OT and the CRF. We test these results with numerical simulations of a network of conductance-based model (CBM) neurons and we demonstrate that they are valid and more robust here than in the rate model. The results indicate that because of the acceleration in the transfer function, described here by a power-law, the orientation tuning curves of V1 neurons are more tuned, and their CRF is steeper than those of their inputs. Last, we show that it is possible to account for the diversity in the measured CRFs by introducing heterogeneities either in single neuron properties or in the input to the neurons. We show how correlations among the parameters that characterize the CRF depend on these sources of heterogeneities. Comparison with experimental data suggests that both sources contribute nearly equally to the diversity of CRF shapes observed in V1 neurons.

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Distributions and correlations between CRF parameters obtained in marmoset V1.A. . B.  C. The exponent . D. Panels (D) and (E) show that there is no significant correlation between  and  and between  and . Panel F shows that correlation between  and  is on the margin of significance ( and  with Fisher's test, but  and  with the non parametric Spearman rank correlation). The line represents the linear correlation.
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pcbi-1001078-g014: Distributions and correlations between CRF parameters obtained in marmoset V1.A. . B. C. The exponent . D. Panels (D) and (E) show that there is no significant correlation between and and between and . Panel F shows that correlation between and is on the margin of significance ( and with Fisher's test, but and with the non parametric Spearman rank correlation). The line represents the linear correlation.

Mentions: In our sample of 98 cells, the median was 8.7 sp/sec (interquartile: 14.0), the median was 25.5% (interquartile: 17.2) and the median exponent was 3.44 (interquartile: 2.54). Distributions for the exponent and (Fig. 14) appear comparable to those obtained in macaque V1 [3]. Both appear to be distinct from those measured in either the magno- or parvocellular layers of the macaque LGN [3]. It is also to be noticed that the proportion of cells in our database displaying saturating or super-saturating response is much larger than in marmoset LGN [14], [66], [67].


Power-law input-output transfer functions explain the contrast-response and tuning properties of neurons in visual cortex.

Persi E, Hansel D, Nowak L, Barone P, van Vreeswijk C - PLoS Comput. Biol. (2011)

Distributions and correlations between CRF parameters obtained in marmoset V1.A. . B.  C. The exponent . D. Panels (D) and (E) show that there is no significant correlation between  and  and between  and . Panel F shows that correlation between  and  is on the margin of significance ( and  with Fisher's test, but  and  with the non parametric Spearman rank correlation). The line represents the linear correlation.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3044767&req=5

pcbi-1001078-g014: Distributions and correlations between CRF parameters obtained in marmoset V1.A. . B. C. The exponent . D. Panels (D) and (E) show that there is no significant correlation between and and between and . Panel F shows that correlation between and is on the margin of significance ( and with Fisher's test, but and with the non parametric Spearman rank correlation). The line represents the linear correlation.
Mentions: In our sample of 98 cells, the median was 8.7 sp/sec (interquartile: 14.0), the median was 25.5% (interquartile: 17.2) and the median exponent was 3.44 (interquartile: 2.54). Distributions for the exponent and (Fig. 14) appear comparable to those obtained in macaque V1 [3]. Both appear to be distinct from those measured in either the magno- or parvocellular layers of the macaque LGN [3]. It is also to be noticed that the proportion of cells in our database displaying saturating or super-saturating response is much larger than in marmoset LGN [14], [66], [67].

Bottom Line: We test these results with numerical simulations of a network of conductance-based model (CBM) neurons and we demonstrate that they are valid and more robust here than in the rate model.The results indicate that because of the acceleration in the transfer function, described here by a power-law, the orientation tuning curves of V1 neurons are more tuned, and their CRF is steeper than those of their inputs.Comparison with experimental data suggests that both sources contribute nearly equally to the diversity of CRF shapes observed in V1 neurons.

View Article: PubMed Central - PubMed

Affiliation: Laboratoire de Neurophysique et Physiologie, Université Paris Descartes, Paris, France.

ABSTRACT
We develop a unified model accounting simultaneously for the contrast invariance of the width of the orientation tuning curves (OT) and for the sigmoidal shape of the contrast response function (CRF) of neurons in the primary visual cortex (V1). We determine analytically the conditions for the structure of the afferent LGN and recurrent V1 inputs that lead to these properties for a hypercolumn composed of rate based neurons with a power-law transfer function. We investigate what are the relative contributions of single neuron and network properties in shaping the OT and the CRF. We test these results with numerical simulations of a network of conductance-based model (CBM) neurons and we demonstrate that they are valid and more robust here than in the rate model. The results indicate that because of the acceleration in the transfer function, described here by a power-law, the orientation tuning curves of V1 neurons are more tuned, and their CRF is steeper than those of their inputs. Last, we show that it is possible to account for the diversity in the measured CRFs by introducing heterogeneities either in single neuron properties or in the input to the neurons. We show how correlations among the parameters that characterize the CRF depend on these sources of heterogeneities. Comparison with experimental data suggests that both sources contribute nearly equally to the diversity of CRF shapes observed in V1 neurons.

Show MeSH