Spatial structure of neuronal receptive field in awake monkey secondary visual cortex (V2)

Abstract

Visual processing depends critically on the receptive field (RF) properties of visual neurons. However, comprehensive characterization of RFs beyond the primary visual cortex (V1) remains a challenge. Here we report fine RF structures in secondary visual cortex (V2) of awake macaque monkeys, identified through a projection pursuit regression analysis of neuronal responses to natural images. We found that V2 RFs could be broadly classified as V1-like (typical Gaborshaped subunits), ultralong (subunits with high aspect ratios), or complex-shaped (subunits with multiple oriented components). Furthermore, single-unit recordings from functional domains identified by intrinsic optical imaging showed that neurons with ultralong RFs were primarily localized within pale stripes, whereas neurons with complex-shaped RFs were more concentrated in thin stripes. Thus, by combining single-unit recording with optical imaging and a computational approach, we identified RF subunits underlying spatial feature selectivity of V2 neurons and demonstrated the functional organization of these RF properties. Compared with the primary visual cortex (V1), neuronal RFs in the secondary visual cortex (V2) are much less understood. Previous studies have shown that in addition to orientation and direction selectivity (1, 2), similar to that found in V1, neurons in V2 also exhibit selectivity for more complex spatial features such as angle (3-5), illusory contour (6), complex shapes (7), texture (8), and segmentation of the scene (9, 10). Given the large number of potentially relevant visual features, traditional methods using stimulus sets with parametric variation of particular visual features are not efficient for comprehensive RF characterization.An alternative approach is to fit the stimulus-response relationship of each neuron by a parametric model. The relationship is ideally probed with large ensembles of visual stimuli, and the resulting model can be used to predict the neuronal responses to other arbitrary stimuli (11,12). Natural stimuli are well suited for this purpose, because the visual system has evolved to process natural scenes, which contain rich spatial features that are more effective than random stimuli in eliciting cortical responses (13). Such an approach imposes no prior assumption about which stimulus features are relevant to the cell and is thus well suited for unbiased RF characterization.In the present study, we used large ensembles of natural images to probe the neuronal responses in awake macaque monkeys and a linear-nonlinear model (14, 15) to represent the RF of each V2 neuron. The subunits of the RF models were identified by a method adapted from projection pursuit regression (PPR) (16-18), which does not require stimuli with specific statistical properties and is thus well suited for analyzing the neuronal responses to natural stimuli. Compared with other optimization methods, a distinct feature of PPR is to optimize one subunit of the RF model at a time to reduce the dimensionality of the problem. Using this ...

Biologically Inspired Hierarchical Contour Detection with Surround Modulation and Neural Connection

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Conjunction or co-activation? A multi-level MVPA approach to task set representations

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