J. Anthony Movshon

Classifying simple and complex cells on the basis of response modulation

Hubel and Wiesel (1962; Journal of Physiology, London, 160, 106-154) introduced the classification of cortical neurons as simple and complex on the basis of four tests of their receptive field structure. These tests are partly subjective and no one of them unequivocally places neurons into distinct classes. A simple, objective classification criterion based on the form of the response to drifting sinusoidal gratings has been used by several laboratories, although it has been criticized by others. We review published and unpublished evidence which indicates that this simple and objective criterion reliability divides neurons of the striate cortex in both cats and monkeys into two groups that correspond closely to the classically-described simple and complex classes.

Stimulus onset quenches neural variability: a widespread cortical phenomenon

Neural responses are typically characterized by computing the mean firing rate, but response variability can exist across trials. Many studies have examined the effect of a stimulus on the mean response, but few have examined the effect on response variability. We measured neural variability in 13 extracellularly recorded datasets and one intracellularly recorded dataset from seven areas spanning the four cortical lobes in monkeys and cats. In every case, stimulus onset caused a decline in neural variability. This occurred even when the stimulus produced little change in mean firing rate. The variability decline was observed in membrane potential recordings, in the spiking of individual neurons and in correlated spiking variability measured with implanted 96-electrode arrays. The variability decline was observed for all stimuli tested, regardless of whether the animal was awake, behaving or anaesthetized. This widespread variability decline suggests a rather general property of cortex, that its state is stabilized by an input.

How MT cells analyze the motion of visual patterns

Neurons in area MT (V5) are selective for the direction of visual motion. In addition, many are selective for the motion of complex patterns independent of the orientation of their components, a behavior not seen in earlier visual areas. We show that the responses of MT cells can be captured by a linear-nonlinear model that operates not on the visual stimulus, but on the afferent responses of a population of nonlinear V1 cells. We fit this cascade model to responses of individual MT neurons and show that it robustly predicts the separately measured responses to gratings and plaids. The model captures the full range of pattern motion selectivity found in MT. Cells that signal pattern motion are distinguished by having convergent excitatory input from V1 cells with a wide range of preferred directions, strong motion opponent suppression and a tuned normalization that may reflect suppressive input from the surround of V1 cells.

Optimal representation of sensory information by neural populations

Sensory information is encoded by populations of neurons. The responses of individual neurons are inherently noisy, so the brain must interpret this information as reliably as possible. In most situations, the optimal strategy for decoding the population signal is to compute the likelihoods of the stimuli that are consistent with an observed neural response. But it has not been clear how the brain can directly compute likelihoods. Here we present a simple and biologically plausible model that can realize the likelihood function by computing a weighted sum of sensory neuron responses. The model provides the basis for an optimal decoding of sensory information. It explains a variety of psychophysical observations on detection, discrimination and identification, and it also directly predicts the relative contributions that different sensory neurons make to perceptual judgments.

The pattern of visual deficits in amblyopia

Amblyopia is usually defined as a deficit in optotype (Snellen) acuity with no detectable organic cause. We asked whether this visual abnormality is completely characterized by the deficit in optotype acuity, or whether it has distinct forms that are determined by the conditions associated with the acuity loss, such as strabismus or anisometropia. To decide this issue, we measured optotype acuity, Vernier acuity, grating acuity, contrast sensitivity, and binocular function in 427 adults with amblyopia or with risk factors for amblyopia and in a comparison group of 68 normal observers. Optotype acuity accounts for much of the variance in Vernier and grating acuity, and somewhat less of the variance in contrast sensitivity. Nevertheless, there are differences in the patterns of visual loss among the clinically defined categories, particularly between strabismic and anisometropic categories. We used factor analysis to create a succinct representation of our measurement space. This analysis revealed two main dimensions of variation in the visual performance of our abnormal sample, one related to the visual acuity measures (optotype, Vernier, and grating acuity) and the other related to the contrast sensitivity measures (Pelli-Robson and edge contrast sensitivity). Representing our data in this space reveals distinctive distributions of visual loss for different patient categories, and suggests that two consequences of the associated conditions--reduced resolution and loss of binocularity--determine the pattern of visual deficit. Non-binocular observers with mild-to-moderate acuity deficits have, on average, better monocular contrast sensitivity than do binocular observers with the same acuity loss. Despite their superior contrast sensitivity, non-binocular observers typically have poorer optotype acuity and Vernier acuity, at a given level of grating acuity, than those with residual binocular function.

Spatiotemporal Elements of Macaque V1 Receptive Fields

Neurons in primary visual cortex (V1) are commonly classified as simple or complex based upon their sensitivity to the sign of stimulus contrast. The responses of both cell types can be described by a general model in which the outputs of a set of linear filters are nonlinearly combined. We estimated the model for a population of V1 neurons by analyzing the mean and covariance of the spatiotemporal distribution of random bar stimuli that were associated with spikes. This analysis reveals an unsuspected richness of neuronal computation within V1. Specifically, simple and complex cell responses are best described using more linear filters than the one or two found in standard models. Many filters revealed by the model contribute suppressive signals that appear to have a predominantly divisive influence on neuronal firing. Suppressive signals are especially potent in direction-selective cells, where they reduce responses to stimuli moving in the nonpreferred direction.

Neuronal Correlates of Amblyopia in the Visual Cortex of Macaque Monkeys with Experimental Strabismus and Anisometropia

Amblyopia is a developmental disorder of pattern vision. After surgical creation of esotropic strabismus in the first weeks of life or after wearing −10 diopter contact lenses in one eye to simulate anisometropia during the first months of life, macaques often develop amblyopia. We studied the response properties of visual cortex neurons in six amblyopic macaques; three monkeys were anisometropic, and three were strabismic. In all monkeys, cortical binocularity was reduced. In anisometropes, the amblyopic eye influenced a relatively small proportion of cortical neurons; in strabismics, the influence of the two eyes was more nearly equal. The severity of amblyopia was related to the relative strength of the input of the amblyopic eye to the cortex only for the more seriously affected amblyopes. Measurements of the spatial frequency tuning and contrast sensitivity of cortical neurons showed few differences between the eyes for the three less severe amblyopes (two strabismic and one anisometropic). In the three more severely affected animals (one strabismic and two anisometropic), the optimal spatial frequency and spatial resolution of cortical neurons driven by the amblyopic eye were substantially and significantly lower than for neurons driven by the nonamblyopic eye. There were no reliable differences in neuronal contrast sensitivity between the eyes. A sample of neurons recorded from cortex representing the peripheral visual field showed no interocular differences, suggesting that the effects of amblyopia were more pronounced in portions of the cortex subserving foveal vision. Qualitatively, abnormalities in both the eye dominance and spatial properties of visual cortex neurons were related on a case-by-case basis to the depth of amblyopia. Quantitative analysis suggests, however, that these abnormalities alone do not explain the full range of visual deficits in amblyopia. Studies of extrastriate cortical areas may uncover further abnormalities that explain these deficits.

Neuronal Adaptation to Visual Motion in Area MT of the Macaque

The responsivity of primary sensory cortical neurons is reduced following prolonged adaptation, but such adaptation has been little studied in higher sensory areas. Adaptation to visual motion has strong perceptual effects, so we studied the effect of prolonged stimulation on neuronal responsivity in the macaques area MT, a cortical area whose importance to visual motion perception is well established. We adapted MT neurons with sinusoidal gratings drifting in the preferred or null direction. Preferred adaptation reduced the responsiveness of MT cells, primarily by changing their contrast gain, and this effect was spatially specific within the receptive field. Null adaptation reduced the ability of null gratings to inhibit the response to a simultaneously presented preferred stimulus. While both preferred and null adaptation alter MT responses, these effects probably do not occur in MT neurons but are likely to reflect adaptation-induced changes in contrast gain earlier in the visual pathway.

Tuning for Spatiotemporal Frequency and Speed in Directionally Selective Neurons of Macaque Striate Cortex

We recorded the responses of direction-selective simple and complex cells in the primary visual cortex (V1) of anesthetized, paralyzed macaque monkeys. When studied with sine-wave gratings, almost all simple cells in V1 had responses that were separable for spatial and temporal frequency: the preferred temporal frequency did not change and preferred speed decreased as a function of the spatial frequency of the grating. As in previous recordings from the middle temporal visual area (MT), approximately one-quarter of V1 complex cells had separable responses to spatial and temporal frequency, and one-quarter were “speed tuned” in the sense that preferred speed did not change as a function of spatial frequency. Half fell between these two extremes. Reducing the contrast of the gratings caused the population of V1 complex cells to become more separable in their tuning for spatial and temporal frequency. Contrast dependence is explained by the contrast gain of the neurons, which was relatively higher for gratings that were either both of high or both of low temporal and spatial frequency. For stimuli that comprised two spatially superimposed sine-wave gratings, the preferred speeds and tuning bandwidths of V1 neurons could be predicted from the sum of the responses to the component gratings presented alone, unlike neurons in MT that showed nonlinear interactions. We conclude that spatiotemporal modulation of contrast gain creates speed tuning from separable inputs in V1 complex cells. Speed tuning in MT could be primarily inherited from V1, but processing that occurs after V1 and possibly within MT computes selective combinations of speed-tuned signals of special relevance for downstream perceptual and motor mechanisms.

Partitioning neuronal variability

Responses of sensory neurons differ across repeated measurements. This variability is usually treated as stochasticity arising within neurons or neural circuits. However, some portion of the variability arises from fluctuations in excitability due to factors that are not purely sensory, such as arousal, attention and adaptation. To isolate these fluctuations, we developed a model in which spikes are generated by a Poisson process whose rate is the product of a drive that is sensory in origin and a gain summarizing stimulus-independent modulatory influences on excitability. This model provides an accurate account of response distributions of visual neurons in macaque lateral geniculate nucleus and cortical areas V1, V2 and MT, revealing that variability originates in large part from excitability fluctuations that are correlated over time and between neurons, and that increase in strength along the visual pathway. The model provides a parsimonious explanation for observed systematic dependencies of response variability and covariability on firing rate.