Excitatory and suppressive receptive field subunits in awake monkey primary visual cortex (V1)

Abstract

An essential step in understanding visual processing is to characterize the neuronal receptive fields (RFs) at each stage of the visual pathway. However, RF characterization beyond simple cells in the primary visual cortex (V1) remains a major challenge. Recent application of spike-triggered covariance (STC) analysis has greatly facilitated characterization of complex cell RFs in anesthetized animals. Here we apply STC to RF characterization in awake monkey V1. We found up to nine subunits for each cell, including one or two dominant excitatory subunits as described by the standard model, along with additional excitatory and suppressive subunits with weaker contributions. Compared with the dominant subunits, the nondominant excitatory subunits prefer similar orientations and spatial frequencies but have larger spatial envelopes. They contribute to response invariance to small changes in stimulus orientation, position, and spatial frequency. In contrast, the suppressive subunits are tuned to orientations 45°-90°differ-ent from the excitatory subunits, which may underlie crossorientation suppression. Together, the excitatory and suppressive subunits form a compact description of RFs in awake monkey V1, allowing prediction of the responses to arbitrary visual stimuli.T he response properties of primary visual cortical (V1) neurons have been studied extensively over the past several decades. In the standard model, a simple cell receptive field (RF) consists of alternating ON and OFF subregions, which directly correspond to the orientation and spatial-frequency tuning of the cell (1, 2). Complex cells exhibit orientation and spatial-frequency tuning similar to simple cells, but they are insensitive to the contrast polarity and stimulus position within the RF. The energy model for complex cell RF consists of a pair of simple-cell-like subunits with the same orientation and spatial-frequency tuning but different ON/OFF phases (3, 4). This model accounts for the phase invariance as well as stimulus selectivity of complex cells.To validate such RF models and to predict the neuronal responses to arbitrary visual stimuli, it is necessary to measure the RF structure quantitatively. For simple cells, spike-triggered average (STA) has been used effectively to estimate their RFs from the responses to sparse noise (5) or white noise (6). For complex cells, however, because the outputs of different RF subunits are combined nonlinearly, these subunits cannot be estimated by STA. In previous studies, complex cell RFs have been studied by measuring the nonlinear interaction between paired stimuli (3,7,8). Another method used in recent studies is spike-triggered covariance (STC) analysis (9, 10). Instead of averaging all of the stimuli preceding spikes (as in STA), in STC analysis one computes the covariance matrix of the spike-triggered stimulus ensemble and identifies the eigenvectors with eigenvalues significantly different from those of the entire stimulus ensemble. This method can reveal stimulus features that drive the neuron ...

Hierarchical computation in the canonical auditory cortical circuit

“…Our findings resemble recent receptive field modeling in primate primary visual cortex that revealed multiple STRFs for simple cells (40,41). Additionally, in both the visual and auditory systems, the structure of the nonlinearities varies with depth.…”

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

“…2A, together with the spatial spectrum and associated nonlinear functions. Consistent with previous reports (20)(21)(22), we found that for most V1 neurons the RF contained multiple Gabor-like subunits of the same orientation ( Fig. 2A, cell 2), whereas a small number of them were unoriented with a single center/surround subunit (cell 1), similar to that found for lateral geniculate nucleus (LGN) neurons.…”

Representation of Cross-Frequency Spatial Phase Relationships in Human Visual Cortex

“…Recent studies estimating such filters using reverse correlation methods suggest that macaque complex cells are better described with several additional filters Chen et al, 2007). In addition, Felsen et al (2005) showed that complex cells in cat V1 are tuned to the phase regularities of natural images.…”