Stimulation of non‐classical receptive field enhances orientation selectivity in the cat

Abstract

We have investigated how the nonclassical receptive field (nCRF) affects dynamic orientation selectivity of cells in the primary visual cortex (V1) in anaesthetized and paralysed cats using the reverse correlation method. We found that tuning to the orientation of the test stimulus depends on the size of the stimulation area. A significant sharpening of orientation tuning was induced by nCRF stimulation, with the magnitude of the effect increasing with the size of stimulation. The effect of the nCRF on the temporal dynamics of orientation tuning was also investigated by examining the tuning over a range of delays from stimulus onset. We found small but detectable changes in both the preferred orientation and the bandwidth of tuning over time when the classical receptive field (CRF) was stimulated alone. Stimulation in nCRF significantly increased the magnitude of these temporal changes. Thus, nCRF stimulation not only enhances the overall orientation selectivity, but also enriches the temporal dynamics of cortical neurones, which may increase the computational power of the visual cortex in information processing.

Orientation selectivity in cat primary visual cortex: local and global measurement

“…The mean WHH was 45.6 ± 26.9°. This was consistent with previous findings on tuning width in cat V1 by Watkins and Berkley [16] and Chen et al [33] , and was Neurosci Bull October 1, 2015, 31(5): 561-571 564 also similar to observations in the primary visual cortex of anaesthetized and alert monkeys [35,36] .…”

“…The other measure of selectivity we used was the tuning curve width at half-height (WHH) as used previously [24,33] . The orientation tuning curves were fitted with the von Mises distribution:…”

Semantic Control of Feature Extraction from Natural Scenes

“…The latter possibility in particular would be supported by recent experimental and theoretical work (Chen et al, 2005;Coen-Cagli et al, 2012;see also Bonds, 1989;Spratling 2010); under this view, semantic control as demonstrated here would tap onto generalpurpose cortical machinery and modulate it in concomitance with nonsemantic flanker effects (e.g., via control of divisive normalization networks). Further experimental characterization will be necessary to pinpoint the relevant circuitry and guide more detailed computational efforts.…”

“…4, compare spread of light-colored lines in A and C and orientation ranges in B), i.e., it is assumed that the filling-in process is driven to a non-negligible extent by the higher level representation of the scene so that it is more precise in the upright configuration. This specific feature of the model is motivated by the following three concepts/findings from existing literature: (1) top-down semantic information about the "gist" of the scene may be used by the visual system to aid in local object/feature identification (Lee and Mumford, 2003;Torralba et al, 2010), possibly via a coarse-tofine strategy associated with enhanced orientation precision (Kveraga et al, 2007); (2) single-unit recordings from primary visual cortex have demonstrated that optimally oriented gratings sharpen neuronal orientation tuning when extended outside the classical receptive field (Chen et al, 2005), indicating that contextual information is exploited by cortical circuitry to refine the orientation range spanned by congruent local edges; and (3) recent computational models successfully account for these neuronal effects via implicit encoding of statistical properties exhibited by natural scenes (Coen-Cagli et al, 2012). Those properties, in turn, are connected with semantic segmentation of the scene and object attribution of local edges (Arbelaez et al, 2012).…”

Dynamic Spatial Processing Originates in Early Visual Pathways

“…Specifically, coarse features of a stimulus are processed before those of fine detail, producing a refinement in resolution as response latency increases. This coarse-to-fine process has been documented for spatial frequency (SF) tuning (Bredfeldt and Ringach, 2002;Mazer et al, 2002;Frazor et al, 2004;Nishimoto et al, 2005), orientation selectivity (Ringach et al, 1997;Chen et al, 2005) (but see Gillespie et al, 2001;Mazer et al, 2002), direction preference (Pack and Born, 2001), and disparity tuning (Menz and Freeman, 2003).…”