Mechanisms of Surround Suppression Effect on the Contrast Sensitivity of V1 Neurons in Cats

Abstract

Surround suppression (SS) is a phenomenon that a neuron’s response to visual stimuli within the classical receptive field (cRF) is suppressed by a concurrent stimulation in the surrounding receptive field (sRF) beyond the cRF. Studies show that SS affects neuronal response contrast sensitivity in the primary visual cortex (V1). However, the underlying mechanisms remain unclear. Here, we examined SS effect on the contrast sensitivity of cats’ V1 neurons with different preferred SFs using external noise-masked visual stimuli and perceptual template model (PTM) analysis at the system level. The contrast sensitivity was evaluated by the inverted threshold contrast of neurons in response to circular gratings of different contrasts in the cRF with or without an annular grating in the sRF. Our results showed that SS significantly reduced the contrast sensitivity of cats’ V1 neurons. The SS-induced reduction of contrast sensitivity was not correlated with SS strength but was dependent on neuron’s preferred SF, with a larger reduction for neurons with low preferred SFs than those with high preferred SFs. PTM analysis of threshold versus external noise contrast (TvC) functions indicated that SS decreased contrast sensitivity by increasing both the internal additive noise and impact of external noise for neurons with low preferred SFs, but improving only internal additive noise for neurons with high preferred SFs. Furthermore, the SS effect on the contrast-response function of low- and high-SF neurons also exhibited different mechanisms in contrast gain and response gain. Collectively, these results suggest that the mechanisms of SS effect on neuronal contrast sensitivity may depend on neuronal populations with different SFs.

A large‐scale neuronal network modelling study: Stimulus size modulates gamma oscillations in the primary visual cortex by long‐range connections

“…This work addresses the surrounding suppression, that is, size modulation of firing rate and gamma oscillation properties, where stimulus size is the only variable and long‐range horizontal connection is the only influencing factor. On this basis, this topic can also be expanded in the following directions: Other manifestations of surround suppression can be considered, such as reduced contrast sensitivity in V1 neurons (Yu et al, 2022); different stimulus properties inside and outside the receptive field can be considered, for example, increased firing rates of central neurons when orthogonal stimuli are presented in the surroundings (Cavanaugh et al, 2002; Keller et al, 2020); additional visual stimulus properties can be considered, such as surround suppression of firing rate that occurs only at strong centre contrasts, or even a boost to small centre contrasts (Rubin et al, 2015; Schwabe et al, 2010); other types of long‐range connections can be considered, because it has been experimentally demonstrated that mammalian visual cortex contains long‐range inhibitory connections, but the exact spatial distribution and function are unclear (Chen et al, 2005; McDonald & Burkhalter, 1993); delving deeper into some of the mathematical properties of the model, for example, Jadi and Sejnowski (2014) found that the shape of the neuron response function in the induction‐stabilized network has an important influence on the modulation of the gamma oscillation properties, similarly, Rubin et al (2015) emphasized the importance of the supralinear output–input function of neurons for surround suppression.…”

Cascaded normalizations for spatial integration in the primary visual cortex of primates

“…Most studies (Alitto et al, 2019;Cavanaugh et al, 2002;DeAngelis et al, 1994;Fisher et al, 2017;Keller et al, 2020a;Levitt and Lund, 2002;Vangeneugden et al, 2019;Yu et al, 2022) have used 1D models (DoG or RoG) to describe the SS in patch-size tuning, but a 2D model (Roberts et al, 2005;Sceniak et al, 2006) is more appropriate to describe the properties of spatial integration because the cortical neuron at a given V1 depth and visual stimuli used in visual space are both in 2D space. Our study also provides evaluations for different CN models in 2D space (in the STAR Methods section).…”