Seiji Tanabe

Neural Correlates of Fine Depth Discrimination in Monkey Inferior Temporal Cortex

Binocular disparity is an important visual cue that gives rise to the perception of depth. Disparity signals are widely spread across the visual cortex, but their relative role is poorly understood. Here, we addressed the correlation between the responses of disparity-selective neurons in the occipitotemporal (ventral) visual pathway and the behavioral discrimination of stereoscopic depth. We recorded activity of disparity-selective neurons in the inferior temporal cortex (IT) while monkeys were engaged in a fine stereoscopic depth discrimination (stereoacuity) task. We found that trial-to-trial fluctuations in neuronal responses correlated with the monkeys perceptual choice. We suggest that disparity signals in the IT, located in the ventral visual pathway, are functionally linked to the discrimination of fine-grain depth.

Rejection of False Matches for Binocular Correspondence in Macaque Visual Cortical Area V4

A plane lying in depth is vividly perceived by viewing a random-dot stereogram (RDS) with a slight binocular disparity. Perception of a plane-in-depth is lost by reversing the contrast of dots seen by one of the eyes to generate an anticorrelated RDS. From a computational perspective, the visual system cannot find a globally consistent solution for matching the left and right eye images of an anticorrelated RDS. The neural representation of a global match should therefore be insensitive to binocular disparity in an anticorrelated RDS. Most neurons in the striate cortex (V1) respond to binocular disparity in anticorrelated RDSs, suggesting that further cortical processing in extrastriate areas is necessary to fully account for the matching computation. We examined neural responses to dynamic RDSs, both normal (correlated) and anticorrelated, in area V4 of the monkey visual cortex. More than half of the V4 cells were sensitive to the horizontal disparity embedded in a correlated RDS. Most of them greatly attenuated their selectivity for disparity when the RDS was anticorrelated. This attenuation was apparent from the response onset, and the degree of attenuation did not correlate with neuronal response latencies. Unlike the disparity tuning of V1 neurons to anticorrelated RDSs, that of V4 neurons was not an inversion of tuning to normal RDSs. Our results suggest that responses to false matches between contrast-reversed dots in the left and right eye images elicited in V1 are substantially reduced by the stage of V4.

Neural Activity in Cortical Area V4 Underlies Fine Disparity Discrimination

Primates are capable of discriminating depth with remarkable precision using binocular disparity. Neurons in area V4 are selective for relative disparity, which is the crucial visual cue for discrimination of fine disparity. Here, we investigated the contribution of V4 neurons to fine disparity discrimination. Monkeys discriminated whether the center disk of a dynamic random-dot stereogram was in front of or behind its surrounding annulus. We first behaviorally tested the reference frame of the disparity representation used for performing this task. After learning the task with a set of surround disparities, the monkey generalized its responses to untrained surround disparities, indicating that the perceptual decisions were generated from a disparity representation in a relative frame of reference. We then recorded single-unit responses from V4 while the monkeys performed the task. On average, neuronal thresholds were higher than the behavioral thresholds. The most sensitive neurons reached thresholds as low as the psychophysical thresholds. For subthreshold disparities, the monkeys made frequent errors. The variable decisions were predictable from the fluctuation in the neuronal responses. The predictions were based on a decision model in which each V4 neuron transmits the evidence for the disparity it prefers. We finally altered the disparity representation artificially by means of microstimulation to V4. The decisions were systematically biased when microstimulation boosted the V4 responses. The bias was toward the direction predicted from the decision model. We suggest that disparity signals carried by V4 neurons underlie precise discrimination of fine stereoscopic depth.

A first-passage-time analysis of the periodically forced noisy leaky integrate-and-fire model.

Abstract. We present a general method for the analysis of the discharge trains of periodically forced noisy leaky integrate-and-fire neuron models. This approach relies on the iterations of a stochastic phase transition operator that generalizes the phase transition function used for the study of periodically forced deterministic oscillators to noisy systems. The kernel of this operator is defined in terms of the the first passage time probability density function of the Ornstein Uhlenbeck process through a suitable threshold. Numerically, it is computed as the solution of a singular integral equation. It is shown that, for the noisy system, quantities such as the phase distribution (cycle histogram), the interspike interval distribution, the autocorrelation function of the intervals, the autocorrelogram and the power spectrum density of the spike train, as well as the input–output cross-correlation and cross-spectral density can all be computed using the stochastic phase transition operator. A detailed description of the numerical implementation of the method, together with examples, is provided.

Matching and correlation computations in stereoscopic depth perception

A fundamental task of the visual system is to infer depth by using binocular disparity. To encode binocular disparity, the visual cortex performs two distinct computations: one detects matched patterns in paired images (matching computation); the other constructs the cross-correlation between the images (correlation computation). How the two computations are used in stereoscopic perception is unclear. We dissociated their contributions in near/far discrimination by varying the magnitude of the disparity across separate sessions. For small disparity (0.03°), subjects performed at chance level to a binocularly opposite-contrast (anti-correlated) random-dot stereogram (RDS) but improved their performance with the proportion of contrast-matched (correlated) dots. For large disparity (0.48°), the direction of perceived depth reversed with an anti-correlated RDS relative to that for a correlated one. Neither reversed nor normal depth was perceived when anti-correlation was applied to half of the dots. We explain the decision process as a weighted average of the two computations, with the relative weight of the correlation computation increasing with the disparity magnitude. We conclude that matching computation dominates fine depth perception, while both computations contribute to coarser depth perception. Thus, stereoscopic depth perception recruits different computations depending on the disparity magnitude.

Coherence resonance and discharge time reliability in neurons and neuronal models.

Neurons are subject to internal and external noise that have been known to modify the way they process incoming signals. Recent studies have suggested that such alterations have functional roles and can also be used in biomedical applications. The present work goes over experimental and theoretical descriptions of the response of neurons to white noise stimulation. It examines various forms of noise related behavior in a standard neuronal model, namely the leaky integrate and fire. This clarifies the conditions under which specific noise induced changes occur in neurons, and consequently can help in determining whether nervous systems operate under similar circumstances.

Suppressive mechanisms in monkey V1 help to solve the stereo correspondence problem.

Neurons encode the depth in stereoscopic images by combining the signals from the receptive fields in the two eyes. Local variations in single images can activate neurons that do not signal the correct disparity (false matches), giving rise to the stereo correspondence problem. We used binocular white-noise stimuli to decompose the responses of monkey primary visual cortex V1 neurons into the elements of a linear–nonlinear model (via spike-triggered covariance analysis). In our population of disparity-selective neurons, we find both excitatory and suppressive elements in many of the neurons. Their binocular receptive fields were aligned in a specific push–pull manner for disparity. We demonstrate that this arrangement reduces the responses to false matches but preserves the responses to true matches. The responses of the cells to the noise stimuli were well explained by a linear summation of the elements, followed by a nonlinearity. This model also explained the shape of independently measured disparity-tuning curves, although it overestimated the response magnitude. This study constitutes the first direct physiological evidence for the contribution of suppressive mechanisms to disparity selectivity. This new mechanism contributes to solving the stereo correspondence problem.

Mechanisms Underlying the Transformation of Disparity Signals from V1 to V2 in the Macaque

Stereo vision relies on cortical signals that encode binocular disparity. In V1, the disparity energy model explains many features of binocular interaction, but it overestimates the responses to anticorrelated images. Combining the outputs of two, or more, energy model-like subunits [two-subunit (2SU) model] can resolve this discrepancy and provides an alternative explanation for disparity signals previously thought to indicate phase disparity between the receptive fields (RFs) of each eye. The 2SU model naturally explains how “near/far” (odd-symmetric) tuning becomes dominant in extrastriate cortex. To compare the energy and the 2SU models, we used a broadband compound grating and applied a common interocular phase difference to all spatial frequency components (a stimulus phase disparity), combined with a common spatial displacement (a stimulus position disparity). This produces binocular images that never occur in natural viewing, for which the 2SU model and the energy model make distinctively different predictions. Responses of neurons recorded from both V1 and V2 of awake rhesus macaques systematically deviated from the predictions of the energy model, in accordance with the 2SU model. These deviations correlated with the symmetry of the tuning curve, indicating that the 2SU mechanism is exploited to produce odd symmetry. Nonetheless, individual subunits also contain RF phase disparity that contributes to odd symmetry. The results suggest that neurons in V2 probably inherit phase disparity signals from V1 neurons, but systematically combine input from V1 neurons with different position disparities, in a way that elaborates odd-symmetric tuning and extends the range of disparities encoded by single neurons.

Disparity-energy signals in perceived stereoscopic depth

Stereopsis, the ability to sense the world in three dimensions (3D) from pairs of retinal images, functions when both images have corresponding elements. When observers view stereograms lacking a global match, they do not perceive 3D structure, whereas several cortical areas encode stereoscopic depth in the disparity energy. Whether these neural representations are exploited or ignored in perceptual decisions remains elusive. By combining contrast-reversal and delay between stereo images, we found that disparity-energy signals mediate the reversal of stereoscopic depth judgments. A crisp, adjacent plane of reference was crucial for the signal to be used in the judgments. Disparity discrimination relies on the disparity-energy signal when the stimulus has no global binocular match and is accompanied by a fixed surface of reference.

Noise-induced transition in excitable neuron models

Abstract. We studied the influence of noisy stimulation on the Hodgkin-Huxley neuron model. Rather than examining the noise-related variability of the discharge times of the model – as has been done previously – our study focused on the effect of noise on the stationary distributions of the membrane potential and gating variables of the model. We observed that a gradual increase in the noise intensity did not result in a gradual change of the distributions. Instead, we could identify a critical intermediate noise range in which the shapes of the distributions underwent a drastic qualitative change. Namely, they moved from narrow unimodal Gaussian-like shapes associated with low noise intensities to ones that spread widely at large noise intensities. In particular, for the membrane potential and the sodium activation variable, the distributions changed from unimodal to bimodal. Thus, our investigation revealed a noise-induced transition in the Hodgkin-Huxley model. In order to further characterize this phenomenon, we considered a reduced one-dimensional model of an excitable system, namely the active rotator. For this model, our analysis indicated that the noise-induced transition is associated with a deterministic bifurcation of approximate equations governing the dynamics of the mean and variance of the state variable. Finally, we shed light on the possible functional importance of this noise-induced transition in neuronal coding by determining its effect on the spike timing precision in models of neuronal ensembles.