Contribution of Inferior Temporal and Posterior Parietal Activity to Three-Dimensional Shape Perception

SummaryOne of the fundamental goals of neuroscience is to understand how perception arises from the activity of neurons in the brain [1]. Stereopsis is a type of three-dimensional (3D) perception that relies on two slightly different projections of the world onto the retinas of the two eyes, i.e., binocular disparity. Neurons selective for curved surfaces defined by binocular disparity may contribute to the perception of an object's 3D structure. Such neurons have been observed in both the anterior lower bank of the superior temporal sulcus (TEs, part of the inferior temporal cortex [IT]) and the anterior intraparietal area (AIP; [2–4]). However, the specific contributions of IT and AIP to depth perception remain unknown. We simultaneously recorded multiunit activity in IT and AIP while monkeys discriminated between concave and convex 3D shapes. We observed a correlation between the neural activity and behavioral choice that arose early and during perceptual decision formation in IT but later and after perceptual decision formation in AIP. These results suggest a role for IT, but not AIP, in 3D shape discrimination. Furthermore, the results demonstrate that similar neuronal stimulus selectivities in two areas do not imply a similar function

The representation of perceived shape similarity and its role for category learning in monkeys: A modeling study

AbstractCategorization models often assume an intermediate stimulus representation by units implementing “distance functions”, that is, units that are activated according to the distance or similarity among stimuli. Here we show that a popular example of these models, ALCOVE, is able to account for the performance of monkeys during category learning when it takes the perceived similarity among stimuli into account. Similar results were obtained with a slightly different model (ITCOVE) that included experimentally measured tuning curves of neurons in inferior temporal (IT) cortex. These results show the intimate link between category learning and perceived similarity as represented in IT cortex

Representation of Semantic Similarity in the Left Intraparietal Sulcus: Functional Magnetic Resonance Imaging Evidence

According to a recent study, semantic similarity between concrete entities correlates with the similarity of activity patterns in left middle IPS during category naming. We examined the replicability of this effect under passive viewing conditions, the potential role of visuoperceptual similarity, where the effect is situated compared to regions that have been previously implicated in visuospatial attention, and how it compares to effects of object identity and location. Forty-six subjects participated. Subjects passively viewed pictures from two categories, musical instruments and vehicles. Semantic similarity between entities was estimated based on a concept-feature matrix obtained in more than 1,000 subjects. Visuoperceptual similarity was modeled based on the HMAX model, the AlexNet deep convolutional learning model, and thirdly, based on subjective visuoperceptual similarity ratings. Among the IPS regions examined, only left middle IPS showed a semantic similarity effect. The effect was significant in hIP1, hIP2, and hIP3. Visuoperceptual similarity did not correlate with similarity of activity patterns in left middle IPS. The semantic similarity effect in left middle IPS was significantly stronger than in the right middle IPS and also stronger than in the left or right posterior IPS. The semantic similarity effect was similar to that seen in the angular gyrus. Object identity effects were much more widespread across nearly all parietal areas examined. Location effects were relatively specific for posterior IPS and area 7 bilaterally. To conclude, the current findings replicate the semantic similarity effect in left middle IPS under passive viewing conditions, and demonstrate its anatomical specificity within a cytoarchitectonic reference frame. We propose that the semantic similarity effect in left middle IPS reflects the transient uploading of semantic representations in working memory

Stimulus-Dependent Adjustment of Reward Prediction Error in the Midbrain

Previous reports have described that neural activities in midbrain dopamine areas are sensitive to unexpected reward delivery and omission. These activities are correlated with reward prediction error in reinforcement learning models, the difference between predicted reward values and the obtained reward outcome. These findings suggest that the reward prediction error signal in the brain updates reward prediction through stimulus–reward experiences. It remains unknown, however, how sensory processing of reward-predicting stimuli contributes to the computation of reward prediction error. To elucidate this issue, we examined the relation between stimulus discriminability of the reward-predicting stimuli and the reward prediction error signal in the brain using functional magnetic resonance imaging (fMRI). Before main experiments, subjects learned an association between the orientation of a perceptually salient (high-contrast) Gabor patch and a juice reward. The subjects were then presented with lower-contrast Gabor patch stimuli to predict a reward. We calculated the correlation between fMRI signals and reward prediction error in two reinforcement learning models: a model including the modulation of reward prediction by stimulus discriminability and a model excluding this modulation. Results showed that fMRI signals in the midbrain are more highly correlated with reward prediction error in the model that includes stimulus discriminability than in the model that excludes stimulus discriminability. No regions showed higher correlation with the model that excludes stimulus discriminability. Moreover, results show that the difference in correlation between the two models was significant from the first session of the experiment, suggesting that the reward computation in the midbrain was modulated based on stimulus discriminability before learning a new contingency between perceptually ambiguous stimuli and a reward. These results suggest that the human reward system can incorporate the level of the stimulus discriminability flexibly into reward computations by modulating previously acquired reward values for a typical stimulus

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Laminar Differences in Associative Memory Signals in Monkey Perirhinal Cortex

New research published in Neuron describes assignment of cortical layer to single neurons recorded in awake monkeys. Applying the procedure to perirhinal cortex, Koyano et al. (2016) found marked and unsuspected differences among layers in the coding of associative memory signals.publisher: Elsevier articletitle: Laminar Differences in Associative Memory Signals in Monkey Perirhinal Cortex journaltitle: Neuron articlelink: http://dx.doi.org/10.1016/j.neuron.2016.10.005 associatedlink: http://dx.doi.org/10.1016/j.neuron.2016.09.024 content_type: article copyright: © 2016 Published by Elsevier Inc.status: publishe

Neural Mechanisms of Object Recognition in Nonhuman Primates

This chapter reviews experimental work in macaque monkeys concerning the processing of visual information for object recognition. Going from the primary visual cortex, area V1, to the inferior temporal cortex (IT), the end station of the ventral visual stream, neurons become selective for complex object features and display a greater tolerance for stimulus changes that preserve object identity. Single IT neurons code for object properties such as shape, texture, and color. Current data suggest that IT neurons do not represent whole visual objects or visual categories, but rather represent features less complex than a whole object (except perhaps in facial representation). The responses of IT neurons are affected by changes in the image that preserve object identity, but their object feature preference is largely invariant to such changes. The stimulus selectivity of IT neurons facilitates the read-out of visual categories and object identity in the regions to which IT projects.Print publication date: 2012 Print ISBN-13: 9780195334654 Published to Oxford Scholarship Online: May 2012 DOI: 10.1093/acprof:oso/9780195334654.001.0001status: publishe