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Recovering stimulus locations using populations of eye-position modulated neurons in dorsal and ventral visual streams of non-human primates.

Sereno AB, Sereno ME, Lehky SR - Front Integr Neurosci (2014)

Bottom Line: Additionally, we developed a simple neural model of eye position signals and illustrate that differences in single cell characteristics can influence the ability to recover target position in a population of cells.We demonstrate for the first time that the ventral stream contains sufficient information for constructing an eye-position based spatial representation.Furthermore we demonstrate, in dorsal and ventral streams as well as modeling, that target locations can be extracted directly from eye position signals in cortical visual responses without computing coordinate transforms of visual space.

View Article: PubMed Central - PubMed

Affiliation: Department of Neurobiology and Anatomy, University of Texas Health Science Center at Houston Houston, TX, USA.

ABSTRACT
We recorded visual responses while monkeys fixated the same target at different gaze angles, both dorsally (lateral intraparietal cortex, LIP) and ventrally (anterior inferotemporal cortex, AIT). While eye-position modulations occurred in both areas, they were both more frequent and stronger in LIP neurons. We used an intrinsic population decoding technique, multidimensional scaling (MDS), to recover eye positions, equivalent to recovering fixated target locations. We report that eye-position based visual space in LIP was more accurate (i.e., metric). Nevertheless, the AIT spatial representation remained largely topologically correct, perhaps indicative of a categorical spatial representation (i.e., a qualitative description such as "left of" or "above" as opposed to a quantitative, metrically precise description). Additionally, we developed a simple neural model of eye position signals and illustrate that differences in single cell characteristics can influence the ability to recover target position in a population of cells. We demonstrate for the first time that the ventral stream contains sufficient information for constructing an eye-position based spatial representation. Furthermore we demonstrate, in dorsal and ventral streams as well as modeling, that target locations can be extracted directly from eye position signals in cortical visual responses without computing coordinate transforms of visual space.

No MeSH data available.


Related in: MedlinePlus

Recovery of eye positions from neural population activity, using a global stimulus configuration and multidimensional scaling (MDS) analysis. MDS analysis was based on using interpolated neural responses from recorded neurons that had significant spatial selectivity under ANOVA. This analysis used mean neural response across trials. (A) Set of eye positions used as input configuration for MDS analysis. It consisted of 32 points arranged in a polar grid. The center of the grid corresponded to central fixation. As illustrated, the eye positions were arranged over four eccentricities with visual angles of [2°, 4°, 6°, 8°]. At each eccentricity, eight locations were arranged in an iso-centric circle at 45° polar angle increments. Each of the 32 eye positions produced a different activation pattern (response vector) in the population of neurons in our data set. Lines connecting the positions merely help illustrate iso-eccentricity positions and iso-polar angles as well as highlight the overall symmetry of the spatial configuration. (B) Configuration of eye positions recovered from AIT data, shown in red. (C) Configuration of eye positions recovered from LIP data, shown in blue. There is less distortion apparent in the spatial layout of the LIP grid compared to AIT and the LIP stress value is lower than in AIT, indicating a more accurate global recovery of eye positions. For both panels (A) and (B), color darkens with decreasing eccentricity, to aid visualization. Also for both panels, normalized MDS eigenvalues are displayed.
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Figure 3: Recovery of eye positions from neural population activity, using a global stimulus configuration and multidimensional scaling (MDS) analysis. MDS analysis was based on using interpolated neural responses from recorded neurons that had significant spatial selectivity under ANOVA. This analysis used mean neural response across trials. (A) Set of eye positions used as input configuration for MDS analysis. It consisted of 32 points arranged in a polar grid. The center of the grid corresponded to central fixation. As illustrated, the eye positions were arranged over four eccentricities with visual angles of [2°, 4°, 6°, 8°]. At each eccentricity, eight locations were arranged in an iso-centric circle at 45° polar angle increments. Each of the 32 eye positions produced a different activation pattern (response vector) in the population of neurons in our data set. Lines connecting the positions merely help illustrate iso-eccentricity positions and iso-polar angles as well as highlight the overall symmetry of the spatial configuration. (B) Configuration of eye positions recovered from AIT data, shown in red. (C) Configuration of eye positions recovered from LIP data, shown in blue. There is less distortion apparent in the spatial layout of the LIP grid compared to AIT and the LIP stress value is lower than in AIT, indicating a more accurate global recovery of eye positions. For both panels (A) and (B), color darkens with decreasing eccentricity, to aid visualization. Also for both panels, normalized MDS eigenvalues are displayed.

Mentions: Multidimensional scaling forms the centerpiece of the data analyses here. To deal with the mathematical requirement of MDS that eye positions for all cells be identical, spatial interpolation from the available data points was performed before the MDS analysis. An example interpolated gain field is shown in Figure 1C, with neural responses for different eye positions indicated by a color scale. Responses at identical eye positions for all cells in our sample, derived from interpolated gain fields, were then used as input to MDS. Only cells having significant spatial selectivity under ANOVA and which had an eye-position eccentricity of less than 10° were included, producing population size n = 33 for AIT and n = 34 for LIP. Responses at 32 eye positions (four eccentricities and eight polar angles; positions arranged in a polar grid as illustrated in Figure 3A) were calculated for each cell using its interpolated gain field. Lines connecting these positions have no significance other than to aid visualization, helping to illustrate iso-eccentricity positions and iso-polar angles as well as highlight the overall symmetry of the spatial configuration. These 32 responses for each cell in the AIT and LIP populations were used as input to MDS.


Recovering stimulus locations using populations of eye-position modulated neurons in dorsal and ventral visual streams of non-human primates.

Sereno AB, Sereno ME, Lehky SR - Front Integr Neurosci (2014)

Recovery of eye positions from neural population activity, using a global stimulus configuration and multidimensional scaling (MDS) analysis. MDS analysis was based on using interpolated neural responses from recorded neurons that had significant spatial selectivity under ANOVA. This analysis used mean neural response across trials. (A) Set of eye positions used as input configuration for MDS analysis. It consisted of 32 points arranged in a polar grid. The center of the grid corresponded to central fixation. As illustrated, the eye positions were arranged over four eccentricities with visual angles of [2°, 4°, 6°, 8°]. At each eccentricity, eight locations were arranged in an iso-centric circle at 45° polar angle increments. Each of the 32 eye positions produced a different activation pattern (response vector) in the population of neurons in our data set. Lines connecting the positions merely help illustrate iso-eccentricity positions and iso-polar angles as well as highlight the overall symmetry of the spatial configuration. (B) Configuration of eye positions recovered from AIT data, shown in red. (C) Configuration of eye positions recovered from LIP data, shown in blue. There is less distortion apparent in the spatial layout of the LIP grid compared to AIT and the LIP stress value is lower than in AIT, indicating a more accurate global recovery of eye positions. For both panels (A) and (B), color darkens with decreasing eccentricity, to aid visualization. Also for both panels, normalized MDS eigenvalues are displayed.
© Copyright Policy - open-access
Related In: Results  -  Collection

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Figure 3: Recovery of eye positions from neural population activity, using a global stimulus configuration and multidimensional scaling (MDS) analysis. MDS analysis was based on using interpolated neural responses from recorded neurons that had significant spatial selectivity under ANOVA. This analysis used mean neural response across trials. (A) Set of eye positions used as input configuration for MDS analysis. It consisted of 32 points arranged in a polar grid. The center of the grid corresponded to central fixation. As illustrated, the eye positions were arranged over four eccentricities with visual angles of [2°, 4°, 6°, 8°]. At each eccentricity, eight locations were arranged in an iso-centric circle at 45° polar angle increments. Each of the 32 eye positions produced a different activation pattern (response vector) in the population of neurons in our data set. Lines connecting the positions merely help illustrate iso-eccentricity positions and iso-polar angles as well as highlight the overall symmetry of the spatial configuration. (B) Configuration of eye positions recovered from AIT data, shown in red. (C) Configuration of eye positions recovered from LIP data, shown in blue. There is less distortion apparent in the spatial layout of the LIP grid compared to AIT and the LIP stress value is lower than in AIT, indicating a more accurate global recovery of eye positions. For both panels (A) and (B), color darkens with decreasing eccentricity, to aid visualization. Also for both panels, normalized MDS eigenvalues are displayed.
Mentions: Multidimensional scaling forms the centerpiece of the data analyses here. To deal with the mathematical requirement of MDS that eye positions for all cells be identical, spatial interpolation from the available data points was performed before the MDS analysis. An example interpolated gain field is shown in Figure 1C, with neural responses for different eye positions indicated by a color scale. Responses at identical eye positions for all cells in our sample, derived from interpolated gain fields, were then used as input to MDS. Only cells having significant spatial selectivity under ANOVA and which had an eye-position eccentricity of less than 10° were included, producing population size n = 33 for AIT and n = 34 for LIP. Responses at 32 eye positions (four eccentricities and eight polar angles; positions arranged in a polar grid as illustrated in Figure 3A) were calculated for each cell using its interpolated gain field. Lines connecting these positions have no significance other than to aid visualization, helping to illustrate iso-eccentricity positions and iso-polar angles as well as highlight the overall symmetry of the spatial configuration. These 32 responses for each cell in the AIT and LIP populations were used as input to MDS.

Bottom Line: Additionally, we developed a simple neural model of eye position signals and illustrate that differences in single cell characteristics can influence the ability to recover target position in a population of cells.We demonstrate for the first time that the ventral stream contains sufficient information for constructing an eye-position based spatial representation.Furthermore we demonstrate, in dorsal and ventral streams as well as modeling, that target locations can be extracted directly from eye position signals in cortical visual responses without computing coordinate transforms of visual space.

View Article: PubMed Central - PubMed

Affiliation: Department of Neurobiology and Anatomy, University of Texas Health Science Center at Houston Houston, TX, USA.

ABSTRACT
We recorded visual responses while monkeys fixated the same target at different gaze angles, both dorsally (lateral intraparietal cortex, LIP) and ventrally (anterior inferotemporal cortex, AIT). While eye-position modulations occurred in both areas, they were both more frequent and stronger in LIP neurons. We used an intrinsic population decoding technique, multidimensional scaling (MDS), to recover eye positions, equivalent to recovering fixated target locations. We report that eye-position based visual space in LIP was more accurate (i.e., metric). Nevertheless, the AIT spatial representation remained largely topologically correct, perhaps indicative of a categorical spatial representation (i.e., a qualitative description such as "left of" or "above" as opposed to a quantitative, metrically precise description). Additionally, we developed a simple neural model of eye position signals and illustrate that differences in single cell characteristics can influence the ability to recover target position in a population of cells. We demonstrate for the first time that the ventral stream contains sufficient information for constructing an eye-position based spatial representation. Furthermore we demonstrate, in dorsal and ventral streams as well as modeling, that target locations can be extracted directly from eye position signals in cortical visual responses without computing coordinate transforms of visual space.

No MeSH data available.


Related in: MedlinePlus