Multiplicative modulations enhance diversity of hue-selective cells

Abstract

There is still much to understand about the brain’s colour processing mechanisms and the transformation from cone-opponent representations to perceptual hues. Moreover, it is unclear which area(s) in the brain represent unique hues. We propose a hierarchical model inspired by the neuronal mechanisms in the brain for local hue representation, which reveals the contributions of each visual cortical area in hue representation. Hue encoding is achieved through incrementally increasing processing nonlinearities beginning with cone input. Besides employing nonlinear rectifications, we propose multiplicative modulations as a form of nonlinearity. Our simulation results indicate that multiplicative modulations have significant contributions in encoding of hues along intermediate directions in the MacLeod-Boynton diagram and that our model V2 neurons have the capacity to encode unique hues. Additionally, responses of our model neurons resemble those of biological colour cells, suggesting that our model provides a novel formulation of the brain’s colour processing pathway.

On the control of attentional processes in vision

“…-vision hierarchy (e.g., Biparva & Tsotsos 2017, Tsotsos et al 2005, Mehrani et al 2020, Rodríguez-Sánchez & Tsotsos 2012); -attention (Tsotsos 2011, Bruce & Tsotsos 2009; -fixation control (Wloka et al 2018; -cognitive programs (Abid 2018, Kotseruba & Tsotsos 2017, Tsotsos & Kruijne 2014; and -how ST, task and memory might interact (Berga et al 2019, Tsotsos & Kruijne 2014. As can be easily seen, the overall STAR architecture contains a full mix of stimulus, knowledge (memory) and tasks driven mechanisms.…”

Pattern forming mechanisms of color vision

“…Finally, we emphasize that the mechanism we offer departs from previous combinatorial color models that predict hue sensation at the final stage of processing (De Valois & De Valois, 1993;Mehrani et al, 2020), as well as neural field models that conflate cone-and coloropponency in their interpretations (Faugeras, Song, & Veltz, 2022;Smirnova, Chizhkova, & Chizhov, 2015;Song, Faugeras, & Veltz, 2019). The emergent hue tuning curves we have characterized are a network property reflective of the physiological neuronal responses, and should not be confounded with our perception of hue.…”

“…With this form, we point out the similarity of our combinatorial scheme to that of Mehrani et al (2020), in which the input from cone-opponent V2 cells into hue tuning V4 cells is weighted as a function of the difference in their preferred hue angles. Most evidently, we differ from this model by first combining the cone-opponent signals in V1 rather than V4, in accordance with the above-mentioned studies.…”

“…The necessary processing has often been attributed to higher-level brain function (De Valois & De Valois, 1993;Lennie, Krauskopf, & Sclar, 1990;M. Li, Liu, Juusola, & Tang, 2014;Mehrani, Mouraviev, & Tsotsos, 2020;Zaidi & Conway, 2019) or yet unidentified higher order mechanisms (Valberg, 2001;Wuerger et al, 2005). A central question of color vision research is whether these mechanisms rely on parallel or modular processing to encode stimulus chromaticity (Conway, 2009;Garg, Li, Rashid, & Callaway, 2019;Liu et al, 2020;Nauhaus, Nielsen, Disney, & Callaway, 2012;Schluppeck & Engel, 2002;Shapley & Hawken, 2011).…”

“…Several authors have addressed these questions through combinatorial models that parameterize the weights of the L, M, and S cones contributing to successive stages of processing (De Valois & De Valois, 1993;Gegenfurtner & Ennis, 2015;Judd, 1949;Mehrani et al, 2020;Stockman & Brainard, 2010). Though differing in their assumptions of modularity, the theories share a mechanistic framework for the transition of single-cell receptive field properties Figure 1.…”