Anitha Pasupathy

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.

Transformation of shape information in the ventral pathway

Object perception seems effortless to us, but it depends on intensive neural processing across multiple stages in ventral pathway visual cortex. Shape information at the retinal level is hopelessly complex, variable and implicit. The ventral pathway must somehow transform retinal signals into much more compact, stable and explicit representations of object shape. Recent findings highlight key aspects of this transformation: higher-order contour derivatives, structural representation in object-based coordinates, composite shape tuning dimensions, and long-term storage of object knowledge. These coding principles could help to explain our remarkable ability to perceive, distinguish, remember and understand a virtual infinity of objects.

An expert system for EEG monitoring in the pediatric intensive care unit.

OBJECTIVES was to design a warning system for the pediatric intensive care unit (PICU). The system should be able to make statements at regular intervals about the level of abnormality of the EEG. The warnings are aimed at alerting an expert that the EEG may be abnormal and needs to be examined. METHODS A total of 188 EEG sections lasting 6 h each were obtained from 74 patients in the PICU. Features were extracted from these EEGs, and with the use of fuzzy logic and neural networks, we designed an expert system capable of imitating a trained EEGer in providing an overall judgment of abnormality about the EEG. The 188 sections were used in training and testing the system using the rotation method, thus separating training and testing data. RESULTS The EEGer and the expert system classified the EEGs in 7 levels of abnormality. There was concordance between the two in 45% of cases. The expert system was within one abnormality level of the EEGer in 91% of cases and within two levels in 97%. CONCLUSIONS We were therefore able to design a system capable of providing reliably an assessment of the level of abnormality of a 6 h section of EEG. This system was validated with a large data set, and could prove useful as a warning device during long-term ICU monitoring to alert a neurophysiologist that an EEG requires attention.

Curvature Processing Dynamics in Macaque Area V4

We have previously analyzed shape processing dynamics in macaque monkey posterior inferotemporal cortex (PIT). We described how early PIT responses to individual contour fragments evolve into tuning for multifragment shape configurations. Here, we analyzed curvature processing dynamics in area V4, which provides feedforward inputs to PIT. We contrasted 2 hypotheses: 1) that V4 curvature tuning evolves from tuning for simpler elements, analogous to PIT shape synthesis and 2) that V4 curvature tuning emerges immediately, based on purely feedforward mechanisms. Our results clearly supported the first hypothesis. Early V4 responses carried information about individual contour orientations. Tuning for multiorientation (curved) contours developed gradually over ∼50 ms. Together, the current and previous results suggest a partial sequence for shape synthesis in ventral pathway cortex. We propose that early orientation signals are synthesized into curved contour fragment representations in V4 and that these signals are transmitted to PIT, where they are then synthesized into multifragment shape representations. The observed dynamics might additionally or alternatively reflect influences from earlier (V1, V2) and later (central and anterior IT) processing stages in the ventral pathway. In either case, the dynamics of contour information in V4 and PIT appear to reflect a sequential hierarchical process of shape synthesis.

Analogous intermediate shape coding in vision and touch.

We recognize, understand, and interact with objects through both vision and touch. Conceivably, these two sensory systems encode object shape in similar ways, which could facilitate cross-modal communication. To test this idea, we studied single neurons in macaque monkey intermediate visual (area V4) and somatosensory (area SII) cortex, using matched shape stimuli. We found similar patterns of shape sensitivity characterized by tuning for curvature direction. These parallel tuning patterns imply analogous shape coding mechanisms in intermediate visual and somatosensory cortex.

Equiluminance Cells in Visual Cortical Area V4

We report a novel class of V4 neuron in the macaque monkey that responds selectively to equiluminant colored form. These “equiluminance” cells stand apart because they violate the well established trend throughout the visual system that responses are minimal at low luminance contrast and grow and saturate as contrast increases. Equiluminance cells, which compose ∼22% of V4, exhibit the opposite behavior: responses are greatest near zero contrast and decrease as contrast increases. While equiluminance cells respond preferentially to equiluminant colored stimuli, strong hue tuning is not their distinguishing feature—some equiluminance cells do exhibit strong unimodal hue tuning, but many show little or no tuning for hue. We find that equiluminance cells are color and shape selective to a degree comparable with other classes of V4 cells with more conventional contrast response functions. Those more conventional cells respond equally well to achromatic luminance and equiluminant color stimuli, analogous to color luminance cells described in V1. The existence of equiluminance cells, which have not been reported in V1 or V2, suggests that chromatically defined boundaries and shapes are given special status in V4 and raises the possibility that form at equiluminance and form at higher contrasts are processed in separate channels in V4.

PARTIAL OCCLUSION MODULATES CONTOUR-BASED SHAPE ENCODING IN PRIMATE AREA V4

Past studies of shape coding in visual cortical area V4 have demonstrated that neurons can accurately represent isolated shapes in terms of their component contour features. However, rich natural scenes contain many partially occluded objects, which have “accidental” contours at the junction between the occluded and occluding objects. These contours do not represent the true shape of the occluded object and are known to be perceptually discounted. To discover whether V4 neurons differentially encode accidental contours, we studied the responses of single neurons in fixating monkeys to complex shapes and contextual stimuli presented either in isolation or adjoining each other to provide a percept of partial occlusion. Responses to preferred contours were suppressed when the adjoining context rendered those contours accidental. The observed suppression was reversed when the partial occlusion percept was compromised by introducing a small gap between the component stimuli. Control experiments demonstrated that these results likely depend on contour geometry at T-junctions and cannot be attributed to mechanisms based solely on local color/luminance contrast, spatial proximity of stimuli, or the spatial frequency content of images. Our findings provide novel insights into how occluded objects, which are fundamental to complex visual scenes, are encoded in area V4. They also raise the possibility that the weakened encoding of accidental contours at the junction between objects could mark the first step of image segmentation along the ventral visual pathway.

The Role of Visual Area V4 in the Discrimination of Partially Occluded Shapes

The primate brain successfully recognizes objects, even when they are partially occluded. To begin to elucidate the neural substrates of this perceptual capacity, we measured the responses of shape-selective neurons in visual area V4 while monkeys discriminated pairs of shapes under varying degrees of occlusion. We found that neuronal shape selectivity always decreased with increasing occlusion level, with some neurons being notably more robust to occlusion than others. The responses of neurons that maintained their selectivity across a wider range of occlusion levels were often sufficiently sensitive to support behavioral performance. Many of these same neurons were distinctively selective for the curvature of local boundary features and their shape tuning was well fit by a model of boundary curvature (curvature-tuned neurons). A significant subset of V4 neurons also signaled the animals upcoming behavioral choices; these decision signals had short onset latencies that emerged progressively later for higher occlusion levels. The time course of the decision signals in V4 paralleled that of shape selectivity in curvature-tuned neurons: shape selectivity in curvature-tuned neurons, but not others, emerged earlier than the decision signals. These findings provide evidence for the involvement of contour-based mechanisms in the segmentation and recognition of partially occluded objects, consistent with psychophysical theory. Furthermore, they suggest that area V4 participates in the representation of the relevant sensory signals and the generation of decision signals underlying discrimination.

Neurodynamics of the prefrontal cortex during conditional visuomotor associations

The prefrontal cortex is believed to be important for cognitive control, working memory, and learning. It is known to play an important role in the learning and execution of conditional visuomotor associations, a cognitive task in which stimuli have to be associated with actions by trial-and-error learning. In our modeling study, we sought to integrate several hypotheses on the function of the prefrontal cortex using a computational model, and compare the results to experimental data. We constructed a module of prefrontal cortex neurons exposed to two different inputs, which we envision to originate from the inferotemporal cortex and the basal ganglia. We found that working memory properties do not describe the dominant dynamics in the prefrontal cortex, but the activation seems to be transient, probably progressing along a pathway from sensory to motor areas. During the presentation of the cue, the dynamics of the prefrontal cortex is bistable, yielding a distinct activation for correct and error trails. We find that a linear change in network parameters relates to the changes in neural activity in consecutive correct trials during learning, which is important evidence for the underlying learning mechanisms.

Shape encoding consistency across colors in primate V4.

Neurons in primate cortical area V4 are sensitive to the form and color of visual stimuli. To determine whether form selectivity remains consistent across colors, we studied the responses of single V4 neurons in awake monkeys to a set of two-dimensional shapes presented in two different colors. For each neuron, we chose two colors that were visually distinct and that evoked reliable and different responses. Across neurons, the correlation coefficient between responses in the two colors ranged from -0.03 to 0.93 (median 0.54). Neurons with highly consistent shape responses, i.e., high correlation coefficients, showed greater dispersion in their responses to the different shapes, i.e., greater shape selectivity, and also tended to have less eccentric receptive field locations; among shape-selective neurons, shape consistency ranged from 0.16 to 0.93 (median 0.63). Consistency of shape responses was independent of the physical difference between the stimulus colors used and the strength of neuronal color tuning. Finally, we found that our measurement of shape response consistency was strongly influenced by the number of stimulus repeats: consistency estimates based on fewer than 10 repeats were substantially underestimated. In conclusion, our results suggest that neurons that are likely to contribute to shape perception and discrimination exhibit shape responses that are largely consistent across colors, facilitating the use of simpler algorithms for decoding shape information from V4 neuronal populations.