Decoding the activity of neuronal populations in macaque primary visual cortex

Visual function depends on the accuracy of signals carried by visual cortical neurons. Combining information across neurons should improve this accuracy because single neuron activity is variable. We examined the reliability of information inferred from populations of simultaneously recorded neurons in macaque primary visual cortex. We considered a decoding framework that computes the likelihood of visual stimuli from a pattern of population activity by linearly combining neuronal responses and tested this framework for orientation estimation and discrimination. We derived a simple parametric decoder assuming neuronal independence and a more sophisticated empirical decoder that learned the structure of the measured neuronal response distributions, including their correlated variability. The empirical decoder used the structure of these response distributions to perform better than its parametric variant, indicating that their structure contains critical information for sensory decoding. These results show how neuronal responses can best be used to inform perceptual decision-making

Visual deficits in anisometropia

AbstractAmblyopia is usually associated with the presence of anisometropia, strabismus or both early in life. We set out to explore quantitative relationships between the degree of anisometropia and the loss of visual function, and to examine how the presence of strabismus affects visual function in observers with anisometropia. We measured optotype acuity, Pelli-Robson contrast sensitivity and stereoacuity in 84 persons with anisometropia and compared their results with those of 27 persons with high bilateral refractive error (isoametropia) and 101 persons with both strabismus and anisometropia. All subjects participated in a large-scale study of amblyopia (McKee et al., 2003). We found no consistent visual abnormalities in the strong eye, and therefore report only on vision in the weaker, defined as the eye with lower acuity. LogMAR acuity falls off markedly with increasing anisometropia in non-strabismic anisometropes, while contrast sensitivity is much less affected. Acuity degrades rapidly with increases in both hyperopic and myopic anisometropia, but the risk of amblyopia is about twice as great in hyperopic than myopic anisometropes of comparable refractive imbalance. For a given degree of refractive imbalance, strabismic anisometropes perform considerably worse than anisometropes without strabismus – visual acuity for strabismics was on average 2.5 times worse than for non-strabismics with similar anisometropia. For observers with equal refractive error in the two eyes there is very little change in acuity or sensitivity with increasing (bilateral) refractive error except for one extreme individual (bilaterally refractive error of –15D). Most pure anisometropes with interocular differences less than 4D retain some stereopsis, and the degree is correlated with the acuity of the weak eye. We conclude that even modest interocular differences in refractive error can influence visual function

Characterizing receptive field selectivity in area V2

The computations performed by neurons in area V1 are reasonably well understood, but computation in subsequent areas such as V2 have been more difficult to characterize. When stimulated with visual stimuli traditionally used to investigate V1, such as sinusoidal gratings, V2 neurons exhibit similar selectivity (but with larger receptive fields, and weaker responses) relative to V1 neurons. However, we find that V2 responses to synthetic stimuli designed to produce naturalistic patterns of joint activity in a model V1 population are more vigorous than responses to control stimuli that lacked this naturalistic structure (Freeman, et. al. 2013). Armed with this signature of V2 computation, we have been investigating how it might arise from canonical computational elements commonly used to explain V1 responses. The invariance of V1 complex cell responses to spatial phase has been previously captured by summing over multiple “subunits” (rectified responses of simple cell-like filters with the same orientation and spatial frequency selectivity, but differing in their receptive field locations). We modeled V2 responses using a similar architecture: V2 subunits were formed from the rectified responses of filters computing the derivatives of the V1 response map over frequencies, orientations, and spatial positions. A V2 complex cell” sums the output of such subunits across frequency, orientation, and position. This model can qualitatively account for much of the behavior of our sample of recorded V2 neurons, including their V1-like spectral tuning in response to sinusoidal gratings as well as the pattern of increased sensitivity to naturalistic images

A Two-Layer Model Explains Higher-Order Feature Selectivity of V2 Neurons

Neurons in cortical area V2 respond selectively to higher-order visual features, such as the quasi-periodic structure of natural texture. However, a functional account of how V2 neurons build selectivity for complex natural image features from their inputs – V1 neurons locally tuned for orientation and spatial frequency – remains elusive. We made single-unit recordings in area V2 in two fixating rhesus macaques. We presented stimuli composed of multiple superimposed grating patches that localize contrast energy in space, orientation, and scale. V2 activity is modeled via a two-layer linear-nonlinear network, optimized to use a sparse combination of V1-like outputs to account for observed activity. Analysis of model fits reveals V2 neurons to be well-matched to natural images, with units combining V1 afferent tuning dimensions to effectively capture natural scene variation. Remarkably, although the models are trained on responses to synthetic stimuli, they can predict responses to novel image classes, i.e. naturalistic texture, reproducing single-unit selectivity for higher-order image statistics. Thus, we demonstrate state-of-the art performance of modeling V2 selectivity, and provide a mechanistic account of single-unit tuning for higher-order natural features

Development of visual cortical function in infant macaques: A BOLD fMRI study.

Functional brain development is not well understood. In the visual system, neurophysiological studies in nonhuman primates show quite mature neuronal properties near birth although visual function is itself quite immature and continues to develop over many months or years after birth. Our goal was to assess the relative development of two main visual processing streams, dorsal and ventral, using BOLD fMRI in an attempt to understand the global mechanisms that support the maturation of visual behavior. Seven infant macaque monkeys (Macaca mulatta) were repeatedly scanned, while anesthetized, over an age range of 102 to 1431 days. Large rotating checkerboard stimuli induced BOLD activation in visual cortices at early ages. Additionally we used static and dynamic Glass pattern stimuli to probe BOLD responses in primary visual cortex and two extrastriate areas: V4 and MT-V5. The resulting activations were analyzed with standard GLM and multivoxel pattern analysis (MVPA) approaches. We analyzed three contrasts: Glass pattern present/absent, static/dynamic Glass pattern presentation, and structured/random Glass pattern form. For both GLM and MVPA approaches, robust coherent BOLD activation appeared relatively late in comparison to the maturation of known neuronal properties and the development of behavioral sensitivity to Glass patterns. Robust differential activity to Glass pattern present/absent and dynamic/static stimulus presentation appeared first in V1, followed by V4 and MT-V5 at older ages; there was no reliable distinction between the two extrastriate areas. A similar pattern of results was obtained with the two analysis methods, although MVPA analysis showed reliable differential responses emerging at later ages than GLM. Although BOLD responses to large visual stimuli are detectable, our results with more refined stimuli indicate that global BOLD activity changes as behavioral performance matures. This reflects an hierarchical development of the visual pathways. Since fMRI BOLD reflects neural activity on a population level, our results indicate that, although individual neurons might be adult-like, a longer maturation process takes place on a population level

Efficient Coding of Local 2D Shape

Efficient coding provides a concise account of key early visual properties, but can it explain higher-level visual function such as shape perception? If curvature is a key primitive of local shape representation, efficient shape coding predicts that sensitivity of visual neurons should be determined by naturally-occurring curvature statistics, which follow a scale-invariant power-law distribution. To assess visual sensitivity to these power-law statistics, we developed a novel family of synthetic maximum-entropy shape stimuli that progressively match the local curvature statistics of natural shapes, but lack global structure. We find that humans can reliably identify natural shapes based on 4th and higher-order moments of the curvature distribution, demonstrating fine sensitivity to these naturally-occurring statistics. What is the physiological basis for this sensitivity? Many V4 neurons are selective for curvature and analysis of population response suggests that neural population sensitivity is optimized to maximize information rate for natural shapes. Further, we find that average neural response in the foveal confluence of early visual cortex increases as object curvature converges to the naturally-occurring distribution, reflecting an increased upper bound on information rate. Reducing the variance of the curvature distribution of synthetic shapes to match the variance of the naturally-occurring distribution impairs the linear decoding of individual shapes, presumably due to the reduction in stimulus entropy. However, matching higher-order moments improves decoding performance, despite further reducing stimulus entropy. Collectively, these results suggest that efficient coding can account for many aspects of curvature perception