Gain Modulation as a Mechanism for Coding Depth from Motion Parallax in Macaque Area MT

Abstract

Observer translation produces differential image motion between objects that are located at different distances from the observer's point of fixation [motion parallax (MP)]. However, MP can be ambiguous with respect to depth sign (near vs far), and this ambiguity can be resolved by combining retinal image motion with signals regarding eye movement relative to the scene. We have previously demonstrated that both extra-retinal and visual signals related to smooth eye movements can modulate the responses of neurons in area MT of macaque monkeys, and that these modulations generate neural selectivity for depth sign. However, the neural mechanisms that govern this selectivity have remained unclear. In this study, we analyze responses of MT neurons as a function of both retinal velocity and direction of eye movement, and we show that smooth eye movements modulate MT responses in a systematic, temporally precise, and directionally specific manner to generate depth-sign selectivity. We demonstrate that depth-sign selectivity is primarily generated by multiplicative modulations of the response gain of MT neurons. Through simulations, we further demonstrate that depth can be estimated reasonably well by a linear decoding of a population of MT neurons with response gains that depend on eye velocity. Together, our findings provide the first mechanistic description of how visual cortical neurons signal depth from MP.

Speed tuning in head coordinates as an alternative explanation of depth selectivity from motion parallax in area MT

“…Our findings show that depth selectivity from motion parallax in area MT can arise from even a modest shift in velocity tuning from retinal to head coordinates. While the joint velocity tuning of many MT neurons is consistent with the previous suggestion of a gain modulation mechanism (Kim et al, 2017), other neurons with slow speed preferences show a clear shift in retinal speed preference with eye velocity that manifests as a diagonal structure. Our simulations reveal that a range of depth tuning properties can be explained by a partial shift of velocity tuning toward head coordinates.…”

“…Indeed, for many neurons, our results show that the HT model can produce joint tuning profiles similar to those predicted by gain modulation, especially given the limited range of the experimental data. These results imply that the gainmodulation effects reported previously by Kim et al (2017) might also arise from a shift in velocity tuning toward head-centered coordinates. In addition, for a subset of MT neurons with slow speed preferences, our findings demonstrate clearly that depth tuning arises from a shift toward headcentered tuning, not gain modulation.…”

“…We fit this model to MT spike trains and we show that the joint tuning for eye and retinal velocities is well described by this gain modulation model for many MT neurons. However, the joint tuning for other neurons reveals a clear diagonal structure that cannot be explained by a mechanism of gain modulation (Kim et al, 2017). We further develop a computational model to account for responses that are shifted, in varying degrees, toward head-centered speed tuning (i.e., tuning for object motion in the world).…”

Position-theta-phase model of hippocampal place cell activity applied to quantification of running speed modulation of firing rate

“…We have drawn from classic GLM (13,17), gain control models (18)(19)(20) and phase-precession models (21,22) to develop a position-theta-phase (PTP) model of place cell activity. In this model, spatial input is scaled by theta phase modulation to determine the firing rate of a place cell.…”

Position–theta-phase model of hippocampal place cell activity applied to quantification of running speed modulation of firing rate

“…We have drawn from classic generalized linear model (13, 17), gain control models (18–20), and phase-precession models (21, 22) to develop a position–theta-phase (PTP) model of place cell activity. In this model, spatial input is scaled by theta-phase modulation to determine the firing rate of a place cell.…”