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Anitha Pasupathy

Publications and source records attributed to Anitha Pasupathy.

3 recordsLinked to original sources

Neural basis of shape representation in the primate brain.

Visual shape recognition--the ability to recognize a wide variety of shapes regardless of their size, position, view, clutter and ambient lighting--is a remarkable ability essential for complex behavior. In the primate brain, this depends on information processing in a multistage pathway running from primary visual cortex (V1), where cells encode local orientation and spatial frequency information, to the inferotemporal cortex (IT), where cells respond selectively to complex shapes. A fundamental question yet to be answered is how the local orientation signals (in V1) are transformed into selectivity for complex shapes (in IT). To gain insights into the underlying mechanisms we investigated the neural basis of shape representation in area V4, an intermediate stage in this processing hierarchy. Theoretical considerations and psychophysical evidence suggest that contour features, i.e. angles and curves along an object contour, may serve as the basis of representation at intermediate stages of shape processing. To test this hypothesis we studied the response properties of single units in area V4 of primates. We first demonstrated that V4 neurons show strong systematic tuning for the orientation and acuteness of angles and curves when presented in isolation within the cells' receptive field. Next, we found that responses to complex shapes were dictated by the curvature at a specific boundary location within the shape. Finally, using basis function decoding, we demonstrated that an ensemble of V4 neurons could successfully encode complete shapes as aggregates of boundary fragments. These findings identify curvature as a basis of shape representation in area V4 and provide insights into the neurophysiological basis for the salience of convex curves in shape perception.

Animals↗

Different time courses of learning-related activity in the prefrontal cortex and striatum.

To navigate our complex world, our brains have evolved a sophisticated ability to quickly learn arbitrary rules such as 'stop at red'. Studies in monkeys using a laboratory test of this capacity--conditional association learning--have revealed that frontal lobe structures (including the prefrontal cortex) as well as subcortical nuclei of the basal ganglia are involved in such learning. Neural correlates of associative learning have been observed in both brain regions, but whether or not these regions have unique functions is unclear, as they have typically been studied separately using different tasks. Here we show that during associative learning in monkeys, neural activity in these areas changes at different rates: the striatum (an input structure of the basal ganglia) showed rapid, almost bistable, changes compared with a slower trend in the prefrontal cortex that was more in accordance with slow improvements in behavioural performance. Also, pre-saccadic activity began progressively earlier in the striatum but not in the prefrontal cortex as learning took place. These results support the hypothesis that rewarded associations are first identified by the basal ganglia, the output of which 'trains' slower learning mechanisms in the frontal cortex.

Animals↗

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.

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