Modeling grating contrast discrimination dippers: The role of surround suppression

Abstract

We consider the role of nonlinear inhibition in physiologically realistic multineuronal models of V1 to predict the dipper functions from contrast discrimination experiments with sinusoidal gratings of different geometries. The dip in dipper functions has been attributed to an expansive transducer function, which itself is attributed to two nonlinear inhibitory mechanisms: contrast normalization and surround suppression. We ran five contrast discrimination experiments, with targets and masks of different sizes and configurations: small Gabor target/small mask, small target/large mask, large target/large mask, small target/in-phase annular mask, and small target/out-of-phase annular mask. Our V1 modeling shows that the results for small Gabor target/small mask, small target/large mask, large target/large mask configurations are easily explained only if the model includes surround suppression. This is compatible with the finding that an in-phase annular mask generates only little threshold elevation while the out-of-phase mask was more effective. Surrounding mask gratings cannot be equated with surround suppression at the receptive-field level. We examine whether normalization and surround suppression occur simultaneously (parallel model) or sequentially (a better reflection of neurophysiology). The Akaike Criterion Difference showed that the sequential model was better than the parallel, but the difference was small. The large target/large mask dipper experiment was not well fit by our models, and we suggest that this may reflect selective attention for its uniquely larger test stimulus. The best-fit model replicates some behaviors of single V1 neurons, such as the decrease in receptive-field size with increasing contrast.

V1-based modeling of discrimination between natural scenes within the luminance and isoluminant color planes

“…The surround spread, at first sight, seems to be rather small, as if the surround suppression arises very close to the receptive field centre. To et al (2017) discuss similar fits (to grating detection data) and show that the small surround radius in these fits is still closely compatible with real neuronal data (Cavanaugh et al, 2002;Sceniak et al, 1999). The receptive-field aspect ratio of the best models (1.6-1.8) is greater than that we reported for grating fits but is, perhaps, more compatible with real neuronal data (Tolhurst & Thompson, 1981).…”

“…As mentioned in the Introduction, this model derives from the seminal work of Rohaly et al, (1997), Watson (1987), and Watson and Solomon (1997). We have also elaborated the model in studies of contrast discrimination in monochromatic naturalistic images and sinusoidal gratings To et al, 2017). The details of the modeling and the physiological and psychophysical justification of the various steps are given in our previous papers.…”

“…The quasi-linear responses of the many complex cells are then subject to two nonlinearities deduced from physiological and psychophysical studies with gratings: within-field, nonspecific contrast normalization or gain control (Carandini et al, 1997;Foley, 1994;Heeger, 1992;Watson & Solomon, 1997) and orientationspecific surround suppression (Blakemore & Tobin, 1972;Cavanaugh et al, 2002;Meese, 2004;Sceniak et al 1999;To et al, 2017). At each location (x,y) in the stimulus, we calculate a nonspecific contrast normalization signal N x,y by summing the quasi-linear contrast responses (C) of the 30 complex cell fields exactly centered at that point (across frequency f and orientation o), each raised to a power q:…”

“…Models of this kind have been good at explaining patterns of detection thresholds for monochromatic gratings and Gabor patches Watson & Ahumada, 2005;Watson & Solomon, 1997) and detection experiments with monochromatic natural images (Párraga, Troscianko, & Tolhurst, 2005;Rohaly, Ahumada, & Watson, 1997;Tolhurst et al, 2010). In particular, the two nonlinearities are necessary to model contrast discriminations even with quite simple sinewave grating stimuli (Foley, 1994;Meese, 2004;To et al, 2017) and it would seem logical to begin any model of natural image discriminations by including them (Rohaly et al, 1997). One aim of the present paper is to investigate whether model performance is affected by the order in which the two nonlinearities (contrast normalization and surround suppression) are imposed.…”

“…One aim of the present paper is to investigate whether model performance is affected by the order in which the two nonlinearities (contrast normalization and surround suppression) are imposed. In their study of sinusoidal grating dipper functions, To et al (2017), found that the best model predictions were obtained when the two inhibitory mechanisms were implemented sequentially rather than in parallel. This change to our 2010 model is more consistent with neurophysiological findings (e.g., Henry et al, 2013).…”