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Statistics and geometry of orientation selectivity in primary visual cortex.

Sadeh S, Rotter S - Biol Cybern (2013)

Bottom Line: In macaques and cats, for example, preferred orientations of neurons are organized in a specific pattern, where cells with similar selectivity are clustered in iso-orientation domains.However, the map is not always continuous, and there are pinwheel-like singularities around which all orientations are arranged in an orderly fashion.Without any additional assumptions, we further show that the pattern of ocular dominance columns is inherently connected to the spatial pattern of orientation.

View Article: PubMed Central - PubMed

Affiliation: Bernstein Center Freiburg, Faculty of Biology, University of Freiburg, Hansastr. 9a, 79104 , Freiburg, Germany, sadra.sadeh@bcf.uni-freiburg.de.

ABSTRACT
Orientation maps are a prominent feature of the primary visual cortex of higher mammals. In macaques and cats, for example, preferred orientations of neurons are organized in a specific pattern, where cells with similar selectivity are clustered in iso-orientation domains. However, the map is not always continuous, and there are pinwheel-like singularities around which all orientations are arranged in an orderly fashion. Although subject of intense investigation for half a century now, it is still not entirely clear how these maps emerge and what function they might serve. Here, we suggest a new model of orientation selectivity that combines the geometry and statistics of clustered thalamocortical afferents to explain the emergence of orientation maps. We show that the model can generate spatial patterns of orientation selectivity closely resembling the maps found in cats or monkeys. Without any additional assumptions, we further show that the pattern of ocular dominance columns is inherently connected to the spatial pattern of orientation.

Show MeSH
Columnar receptive field from heterogeneous LGN receptive fields. a  receptive fields of LGN afferents projecting to one cortical column, as in Fig. 2b. Unlike Fig. 2b, the sizes of receptive fields are now not the same:  has a uniform distribution between , and  in each case. The half-height contours for the smallest and the largest receptive field are shown in white. The colors code the sum of all individual receptive fields. b Cortical receptive fields, for neurons at different positions in the column (denoted by the small circle in red). Dots show the centers of afferents.
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Fig4: Columnar receptive field from heterogeneous LGN receptive fields. a  receptive fields of LGN afferents projecting to one cortical column, as in Fig. 2b. Unlike Fig. 2b, the sizes of receptive fields are now not the same: has a uniform distribution between , and in each case. The half-height contours for the smallest and the largest receptive field are shown in white. The colors code the sum of all individual receptive fields. b Cortical receptive fields, for neurons at different positions in the column (denoted by the small circle in red). Dots show the centers of afferents.

Mentions: We use this simplification in the following to highlight the role of columnar interaction for orientation selectivity. Note, however, that some of the assumptions can be further relaxed. First, it is not necessary to assume a precise Gaussian distribution of inputs from LGN around the center of columns. In fact, allowing for a uniform distribution of LGN receptive fields in the column results in similar columnar receptive fields, only slightly shifted from the center (Fig. 3). Second, sampling from heterogeneous LGN receptive fields with different sizes does not change the result qualitatively (Fig. 4). Finally, the concept of a columnar receptive field itself is not a strictly necessary assumption of the model; we also consider a more general scenario without this assumption later.Fig. 3


Statistics and geometry of orientation selectivity in primary visual cortex.

Sadeh S, Rotter S - Biol Cybern (2013)

Columnar receptive field from heterogeneous LGN receptive fields. a  receptive fields of LGN afferents projecting to one cortical column, as in Fig. 2b. Unlike Fig. 2b, the sizes of receptive fields are now not the same:  has a uniform distribution between , and  in each case. The half-height contours for the smallest and the largest receptive field are shown in white. The colors code the sum of all individual receptive fields. b Cortical receptive fields, for neurons at different positions in the column (denoted by the small circle in red). Dots show the centers of afferents.
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4228171&req=5

Fig4: Columnar receptive field from heterogeneous LGN receptive fields. a  receptive fields of LGN afferents projecting to one cortical column, as in Fig. 2b. Unlike Fig. 2b, the sizes of receptive fields are now not the same: has a uniform distribution between , and in each case. The half-height contours for the smallest and the largest receptive field are shown in white. The colors code the sum of all individual receptive fields. b Cortical receptive fields, for neurons at different positions in the column (denoted by the small circle in red). Dots show the centers of afferents.
Mentions: We use this simplification in the following to highlight the role of columnar interaction for orientation selectivity. Note, however, that some of the assumptions can be further relaxed. First, it is not necessary to assume a precise Gaussian distribution of inputs from LGN around the center of columns. In fact, allowing for a uniform distribution of LGN receptive fields in the column results in similar columnar receptive fields, only slightly shifted from the center (Fig. 3). Second, sampling from heterogeneous LGN receptive fields with different sizes does not change the result qualitatively (Fig. 4). Finally, the concept of a columnar receptive field itself is not a strictly necessary assumption of the model; we also consider a more general scenario without this assumption later.Fig. 3

Bottom Line: In macaques and cats, for example, preferred orientations of neurons are organized in a specific pattern, where cells with similar selectivity are clustered in iso-orientation domains.However, the map is not always continuous, and there are pinwheel-like singularities around which all orientations are arranged in an orderly fashion.Without any additional assumptions, we further show that the pattern of ocular dominance columns is inherently connected to the spatial pattern of orientation.

View Article: PubMed Central - PubMed

Affiliation: Bernstein Center Freiburg, Faculty of Biology, University of Freiburg, Hansastr. 9a, 79104 , Freiburg, Germany, sadra.sadeh@bcf.uni-freiburg.de.

ABSTRACT
Orientation maps are a prominent feature of the primary visual cortex of higher mammals. In macaques and cats, for example, preferred orientations of neurons are organized in a specific pattern, where cells with similar selectivity are clustered in iso-orientation domains. However, the map is not always continuous, and there are pinwheel-like singularities around which all orientations are arranged in an orderly fashion. Although subject of intense investigation for half a century now, it is still not entirely clear how these maps emerge and what function they might serve. Here, we suggest a new model of orientation selectivity that combines the geometry and statistics of clustered thalamocortical afferents to explain the emergence of orientation maps. We show that the model can generate spatial patterns of orientation selectivity closely resembling the maps found in cats or monkeys. Without any additional assumptions, we further show that the pattern of ocular dominance columns is inherently connected to the spatial pattern of orientation.

Show MeSH