Contrast-dependence of surround suppression in Macaque V1: Experimental testing of a recurrent network model

Abstract

Neuronal responses in primary visual cortex (V1) to optimally oriented high-contrast stimuli in the receptive field (RF) center are suppressed by stimuli in the RF surround, but can be facilitated when the RF center is stimulated at low contrast. The neural circuits and mechanisms for surround modulation are still unknown. We previously proposed that topdown feedback connections mediate suppression from the "far" surround, while "near" surround suppression is mediated primarily by horizontal connections. We implemented this idea in a recurrent network model of V1. A model assumption needed to account for the contrast-dependent sign of surround modulation is a response asymmetry between excitation and inhibition; accordingly, inhibition, but not excitation, is silent for weak visual inputs to the RF center, and surround stimulation can evoke facilitation. A prediction stemming from this same assumption is that surround suppression is weaker for low than for high contrast stimuli in the RF center. Previous studies are inconsistent with this prediction. Using single unit recordings in macaque V1, we confirm this model's prediction. Model simulations demonstrate that our results can be reconciled with those from previous studies. We also performed a systematic comparison of the experimentally-measured surround suppression strength with predictions of the model operated in different parameter regimes. We find that the original model, with strong horizontal and no feedback excitation of local inhibitory neurons, can only partially account quantitatively for the experimentally-measured suppression. Strong direct feedback excitation of V1 inhibitory neurons is necessary to account for the experimentally-measured surround suppression strength.

Spatial integration in mouse primary visual cortex

“…Contrast normalization has been successfully used to model surround suppression (Carandini and Heeger 2012). In primate and cat V1, spatial and temporal properties of center-surround interactions depend on stimulus contrast (Kapadia et al 1999;Sadakane et al 2006;Sceniak et al 1999;Schwabe et al 2010;Webb et al 2005). In the present study, we have shown that stimulus contrast similarly influences surround suppression in mouse V1, where lower stimulus contrast leads to larger RF center sizes and weaker suppression strength (see also Ayaz et al 2013;Nienborg et al 2013).…”

Towards Model-Based Brain Imaging with Multi-Scale Modeling

“…For certain strengths the predicted suppression is within the 50% confidence region while for others it is outside. This study shows that one can respect the variability in the data in terms of heterogeneity of the underlying microcircuits and still use experimental data with NMM-like network models in order to learn something about the actual microcircuits: Here, we found that within the class of models we considered the models with stronger feedback projections to inhibitory neurons in the model V1 produce quantitatively better matches to the measured surround suppression than models with less feedback to these inhibitory neurons; see Schwabe et al (2010) for more details. Of course, stochastic models respecting heterogeneity in single cell properties and network connections, or models for large-scale simulations could be described with model parameters , which capture such heterogeneity and hence would be directly useable within a Bayesian model comparison.…”

“…In our modeling of visual cortical networks we also employed mean-field firing rate models as in NMMs (as well as more detailed models with so-called "spiking neurons"). We could show that single neuron responses in primary visual cortex (V1) are best explained when the local cortical microcircuits are assumed to operate in a balance between strong recurrent excitation and inhibition (Mariño et al, 2005a;Stimberg et al, 2009), and that inter-areal feedback into V1 may play a crucial role in "lateral inhibition" (Schwabe et al, 2006a;Ichida et al, 2007;Schwabe et al, 2010). To the best of our knowledge, such and other recent advances in cortical microcircuits models have not yet been implemented into NMMs used in brain imaging.…”

“…The lines show predicted surround suppression of the recurrent network model from for increasing strength of the inter-areal feedback connections to local inhibitory cells for moderate (dashed) and strong intra-areal (solid) lateral inhibition. (Figure 3c is taken from Schwabe et al(2010), with permission from Elsevier).…”

“…For example, it is conceivable that some neurons were excitatory while others were inhibitory, some neurons may operate in a different local network neighborhood than others, etc. In Schwabe et al (2010) we hypothesized that this variability is due to different strengths of the intra-areal vs. inter-areal connections (the model parameter ), and we simulated the network model with different parameter values. As we increased, for example, the strength of the feedback connections in the model (to local inhibitory neurons in V1) we predict different strengths of surround suppression (see lines in Figure 3c).…”