A Model of Binocular Motion Integration in MT Neurons

Abstract

Primate cortical area MT plays a central role in visual motion perception, but models of this area have largely overlooked the binocular integration of motion signals. Recent electrophysiological studies tested binocular integration in MT and found surprisingly that MT neurons lose their hallmark "pattern motion" selectivity when stimuli are presented dichoptically and that many neurons are selective for motion-in-depth (MID). By unifying these novel observations with insights from monocular, frontoparallel motion studies concurrently in a binocular MT motion model, we generated clear, testable predictions about the circuitry and mechanisms underlying visual motion processing. We built binocular models in which signals from left-and right-eye streams could be integrated at various stages from V1 to MT, attempting to create the simplest plausible circuits that accounted for the physiological range of pattern motion selectivity, that explained changes across this range for dichoptic stimulus presentation, and that spanned the spectrum of MID selectivity observed in MT. Our successful models predict that motion-opponent suppression is the key mechanism to account for the striking loss of pattern motion sensitivity with dichoptic plaids, that opponent suppression precedes binocular integration, and that opponent suppression will be stronger in inputs to pattern cells than to component cells. We also found an unexpected connection between circuits for pattern motion selectivity and MID selectivity, suggesting that these two separately studied phenomena could be related. These results also hold in models that include binocular disparity computations, providing a platform for future exploration of binocular response properties in MT.

Systematic misperceptions of 3-D motion explained by Bayesian inference

“…While the current model is perceptual and not mechanistic, our predictions and results are relevant to investigating the neural mechanisms that underlie motion perception. The central role of area MT in the processing of binocular 3-D motion signals is now well established, based on both neuroimaging (Rokers, Cormack, & Huk, 2009) and electrophysiology studies (Baker & Bair, 2016;Czuba, Huk, Cormack, & Kohn, 2014;Sanada & DeAngelis, 2014). Our model highlights the fact that both position and binocular speed tuning are essential for inferring the trajectory of a stimulus moving in three dimensions.…”

Asymmetries between achromatic and chromatic extraction of 3D motion signals

“…However, recent models of binocular motion perception in the MT suggest that V1 inputs should exhibit motion opponent suppression, and that these signals arise before binocular integration in V1 (54). A general, interocular suppressive mechanism may precede the extraction of MID (55), while monocular motion opponency has also been proposed to drive pattern motion cells in the MT (56, 57).…”

Binocular Mechanisms of 3D Motion Processing

“…However, as techniques for creating strongly IOVD-biased stimuli improved, so did the apparent contributions of velocity-based mechanisms (Czuba et al 2010, Fernandez & Farell 2005, Rokers et al 2008, Sheliga et al 2016). These methods have facilitated a greater understanding of the relative utility of IOVD versus CD cues and highlighted an increasing need for better models of binocular motion processing (Baker & Bair 2016). …”

“…In a series of adaptation studies using both fMRI and perceptual motion aftereffects, Joo et al (2016) found little to no adaptation transfer between cues—suggesting mostly separate subcircuits for IOVDs and CDs within human MT, but direct electrophysiological studies specifically addressing this question are needed. Finally, recent modeling work has suggested a nonintuitive relation between pattern motion selectivity and 3D direction sensitivity (Baker & Bair 2016), so empirical tests in the context of evolving computational models may provide important insights into the fundamental computations performed by MT.…”