Search PubMed⌕ Search

Biomedical subjects

Charles E Connor

Publications and source records attributed to Charles E Connor.

11 recordsLinked to original sources
PubMed2006

Dynamic shape synthesis in posterior inferotemporal cortex.

How does the brain synthesize low-level neural signals for simple shape parts into coherent representations of complete objects? Here, we present evidence for a dynamic process of object part integration in macaque posterior inferotemporal cortex (IT). Immediately after stimulus onset, neural responses carried information about individual object parts (simple contour fragments) only. Subsequently, information about specific multipart configurations emerged, building gradually over the course of approximately 60 ms, producing a sparser and more explicit representation of object shape. We show that this gradual transformation can be explained by a recurrent network process that effectively compares parts signals across neurons to generate inferences about multipart shape configurations.

Animals↗
PubMed2005

Quantitative characterization of disparity tuning in ventral pathway area V4.

We performed a quantitative characterization of binocular disparity-tuning functions in the ventral (object-processing) pathway of the macaque visual cortex. We measured responses of 452 area V4 neurons to stimuli with disparities ranging from -1.0 to +1.0 degrees. Asymmetric Gaussian functions fit the raw data best (median R = 0.90), capturing both the modal components (local peaks in the -1.0 to +1.0 degrees range) and the monotonic components (linear or sigmoidal dependency on disparity) of the tuning patterns. Values derived from the asymmetric Gaussian fits were used to characterize neurons on a modal x monotonic tuning domain. Points along the modal tuning axis correspond to classic tuned excitatory and inhibitory patterns; points along the monotonic axis correspond to classic near and far patterns. The distribution on this domain was continuous, with the majority of neurons exhibiting a mixed modal/monotonic tuning pattern. The distribution in the modal dimension was shifted toward excitatory patterns, consistent with previous results in other areas. The distribution in the monotonic dimension was shifted toward tuning for crossed disparities (corresponding to stimuli nearer than the fixation plane). This could reflect a perceptual emphasis on objects or object parts closer to the observer. We also found that disparity-tuning strength was positively correlated with orientation-tuning strength and color-tuning strength, and negatively correlated with receptive field eccentricity.

Animals↗
PubMed2004

Visual attention: bottom-up versus top-down.

Visual attention is attracted by salient stimuli that 'pop out' from their surroundings. Attention can also be voluntarily directed to objects of current importance to the observer. What happens in the brain when these two processes interact?

Attention↗
PubMed2004

Underlying principles of visual shape selectivity in posterior inferotemporal cortex.

Object perception depends on shape processing in the ventral visual pathway, which in monkeys culminates in inferotemporal cortex (IT). Here we provide a description of fundamental quantitative principles governing neural selectivity for complex shape in IT. By measuring responses to large, parametric sets of two-dimensional (2D) silhouette shapes, we found that neurons in posterior IT (Brodmann's areas TEO and posterior TE) integrate information about multiple contour elements (straight and curved edge fragments of the type represented in lower-level areas) using both linear and nonlinear mechanisms. This results in complex, distributed response patterns that cannot be characterized solely in terms of example stimuli. We explained these response patterns with tuning functions in multidimensional shape space and accurately predicted neural responses to the widely varying shapes in our stimulus set. Integration of contour element information in earlier stages of IT represents an important step in the transformation from low-level shape signals to complex object representation.

Action Potentials↗
PubMed2003

Active vision and visual activation in area V4.

During normal vision, the focus of gaze continually jumps from one important image feature to the next. In this issue of Neuron, Mazer and Gallant analyze neural activity in higher-level visual cortex during this kind of active visual exploration, and they demonstrate a localized enhancement of visual responses that predicts the target of the upcoming eye movement.

Animals↗
PubMed2002

Three-dimensional orientation tuning in macaque area V4.

Tuning for the orientation of elongated, linear image elements (edges, bars, gratings), first discovered by Hubel and Wiesel, is considered a key feature of visual processing in the brain. It has been studied extensively in two dimensions (2D) using frontoparallel stimuli, but in real life most lines, edges and contours are slanted with respect to the viewer. Here we report that neurons in macaque area V4, an intermediate stage in the ventral (object-related) pathway of visual cortex, were tuned for 3D orientation--that is,for specific slants as well as for 2D orientation. The tuning for 3D orientation was consistent across depth position (binocular disparity) and position within the 2D classical receptive field. The existence of 3D orientation signals in the ventral pathway suggests that the brain may use such information to interpret 3D shape.

Animals↗
PubMed2002

Population coding of shape in area V4.

Shape is represented in the visual system by patterns of activity across populations of neurons. We studied the population code for shape in area V4 of macaque monkeys, which is part of the ventral (object-related) pathway in primate visual cortex. We have previously found that many macaque V4 neurons are tuned for the curvature and object-centered position of boundary fragments (such as 'concavity on the right'). Here we tested the hypothesis that populations of such cells represent complete shapes as aggregates of boundary fragments. To estimate the population representation of a given shape, we scaled each cell's tuning peak by its response to that shape, summed across cells and smoothed. The resulting population response surface contained 3-8 peaks that represented major boundary features and could be used to reconstruct (approximately) the original shape. This exemplifies how a multi-peaked neural population response can represent a complex stimulus in terms of its constituent elements.

Action Potentials↗