No abstract

No abstract

Quantitative analysis of the responses of V1 neurons to horizontal disparity in dynamic random-dot stereograms. J Neurophysiol 87: 191-208, 2002; 10.1152/jn.00465.2000. Horizontal disparity tuning for dynamic random-dot stereograms was investigated for a large population of neurons (n ϭ 787) in V1 of the awake macaque. Disparity sensitivity was quantified using a measure of the discriminability of the maximum and minimum points on the disparity tuning curve. This measure and others revealed a continuum of selectivity rather than separate populations of disparity-and nondisparity-sensitive neurons. Although disparity sensitivity was correlated with the degree of direction tuning, it was not correlated with other significant neuronal properties, including preferred orientation and ocular dominance. In accordance with the Gabor energy model, tuning curves for horizontal disparity were adequately described by Gabor functions when the neuron's orientation preference was near vertical. For neurons with orientation preferences near to horizontal, a Gaussian function was more frequently sufficient. The spatial frequency of the Gabor function that described the disparity tuning was weakly correlated with measurements of the spatial frequency and orientation preference of the neuron for drifting sinusoidal gratings. Energy models make several predictions about the relationship between the response rates to monocular and binocular dot patterns. Few of the predictions were fulfilled exactly, although the observations can be reconciled with the energy model by simple modifications. These same modifications also provide an account of the observed continuum in strength of disparity selectivity. A weak correlation between the disparity sensitivity of simultaneously recorded single-and multiunit data were revealed as well as a weak tendency to show similar disparity preferences. This is compatible with a degree of local clustering for disparity sensitivity in V1, although this is much weaker than that reported in area MT. I N T R O D U C T I O NSelectivity for binocular disparity was initially demonstrated using elongated bar stimuli in cat area 17 (Barlow et al. 1967;Pettigrew et al. 1968) and V1 of the awake monkey (Poggio and Fischer 1977). Subsequently, Poggio and colleagues (Poggio 1995;Poggio et al. 1985Poggio et al. , 1988 examined the sensitivity to horizontal disparity in random-dot stereograms (RDS) in macaque V1. None of the studies using RDS has attempted to describe the disparity tuning quantitatively. Consequently, there has been no quantitative analysis of the relationship between disparity selectivity to RDS and other fundamental properties of V1 neurons, such as orientation tuning and ocular dominance.There are several reasons why it is important to study these issues with RDS. First, a change in the disparity of a bar stimulus also generates changes in the monocular images, which by themselves may influence neuronal firing. By contrast the monocular images of random-dot stimuli are spatially homogeneous. There...

No abstract

The responses of single cortical neurons were measured as a function of the binocular disparity of dynamic random dot stereograms for a large sample of neurons (n = 787) from V1 of the awake macaque. From this sample, we selected 180 neurons whose tuning curves were strongly tuned for disparity, well sampled and well described by one-dimensional Gabor functions. The fitted parameters of the Gabor functions were used to resolve three outstanding issues in binocular stereopsis. First, we considered whether tuning curves can be meaningfully divided into discrete tuning types. Careful examination of the distributions of the Gabor parameters that determine tuning shape revealed no evidence for clustering. We conclude that a continuum of tuning types is present. Second, we investigated the mechanism of disparity encoding for V1 neurons. The shape of the disparity tuning function can be used to distinguish between position-encoding (in which disparity is encoded by an interocular shift in receptive field position) and phase-encoding (in which disparity is encoded by a difference in the receptive field profile in the 2 eyes). Both position and phase encoding were found to be common. This was confirmed by an independent assessment of disparity encoding based on the measurement of disparity sensitivity for sinusoidal luminance gratings of different spatial frequencies. The contributions of phase and position to disparity encoding were compared by estimating a population average of the rate of change in firing rate per degree of disparity. When this was calculated separately for the phase and position contributions, they were found to be closely similar. Third, we investigated the range of disparity tuning in V1 as a function of eccentricity in the parafoveal range. We find few cells which are selective for disparities greater than +/-1 degrees even at the largest eccentricity of approximately 5 degrees. The preferred disparity was correlated with the spatial scale of the tuning curve, and for most units lay within a +/-pi radians phase limit. Such a size-disparity correlation is potentially useful for the solution of the correspondence problem.

Abstract-Face recognition algorithms perform very unreliably when the pose of the probe face is different from the gallery face: typical feature vectors vary more with pose than with identity. We propose a generative model that creates a one-to-many mapping from an idealized "identity" space to the observed data space. In identity space, the representation for each individual does not vary with pose. We model the measured feature vector as being generated by a pose-contingent linear transformation of the identity variable in the presence of Gaussian noise. We term this model "tied" factor analysis. The choice of linear transformation (factors) depends on the pose, but the loadings are constant (tied) for a given individual. We use the EM algorithm to estimate the linear transformations and the noise parameters from training data. We propose a probabilistic distance metric that allows a full posterior over possible matches to be established. We introduce a novel feature extraction process and investigate recognition performance by using the FERET, XM2VTS, and PIE databases. Recognition performance compares favorably with contemporary approaches.

We apply state space estimation techniques to the time-varying reconstruction problem in optical tomography. We develop a stochastic model for describing the evolution of quasi-sinusoidal medical signals such as the heartbeat, assuming these are represented as a known frequency with randomly varying amplitude and phase. We use the extended Kalman filter in combination with spatial regularization techniques to reconstruct images from highly under-determined time-series data. This system also naturally segments activity belonging to different biological processes. We present reconstructions of simulated data and of real data recorded from the human motor cortex (Franceschini et al 2000 Optics Express 6 49-57). It is argued that the application of these time-series techniques improves both the fidelity and temporal resolution of reconstruction in optical tomography.

The performance of single neurons in cortical area V1 of alert macaque monkeys was compared against the animals' psychophysical performance during a binocular disparity discrimination task. Performance was assessed with stimuli that consisted of a patch of dynamic random dots, whose disparity varied from trial to trial, surrounded by an annulus of similar dots at a fixed disparity. On each trial, the animals indicated whether the depth of the central patch was in front of or behind the annulus. For each disparity of the center patch, neural performance was assessed by calculating the probability that the response of the neuron was greater or less than the response when the center disparity was the same as that of the annulus. Initially the animals performed the task simultaneously with the neural recording. However, the range of disparities used, which was appropriate for the neuronal recording, may have affected performance, because the thresholds were substantially lower (2.6x) when the psychophysical measurements were repeated later. Average neuronal thresholds were approximately 4x poorer than these behavioral thresholds, although the best neurons were marginally better than the animals' behavior. Thus, the well known precision of relative depth judgments can be supported with signals from a small number of V1 neurons. Interference with the relative depth information in the stimulus profoundly affected behavioral thresholds, which were approximately 10x poorer when the surround was absent or contained binocularly uncorrelated dots. In this case, single V1 neurons consistently outperform the observer: presumably here, psychophysical thresholds are limited by other factors (such as uncertainty about vergence eye position).