Biomedical subjects

Stephen V David

Publications and source records attributed to Stephen V David.

6 recordsLinked to original sources

PubMed2006

Spectral receptive field properties explain shape selectivity in area V4.

Neurons in cortical area V4 respond selectively to complex visual patterns such as curved contours and non-Cartesian gratings. Most previous experiments in V4 have measured responses to small, idiosyncratic stimulus sets and no single functional model yet accounts for all of the disparate results. We propose that one model, the spectral receptive field (SRF), can explain many observations of selectivity in V4. The SRF describes tuning in terms of the orientation and spatial frequency spectrum and can, in principle, predict the response to any visual stimulus. We estimated SRFs for neurons in V4 of awake primates by linearized reverse correlation of responses to a large set of natural images. We find that V4 neurons have large orientation and spatial frequency bandwidth and often bimodal orientation tuning. For comparison, we estimated SRFs for neurons in primary visual cortex (V1). Consistent with previous observations, we find that V1 neurons have narrower bandwidth than that of V4. To determine whether estimated SRFs can account for previous observations of selectivity, we used them to predict responses to Cartesian gratings, non-Cartesian gratings, natural images, and curved contours. Based on these predictions, we find that the majority of neurons in V1 are selective for Cartesian gratings, whereas the majority of V4 neurons are selective for non-Cartesian gratings or natural images. The SRF describes visual tuning properties with a second-order nonlinear model. These results support the hypothesis that a second-order model is sufficient to describe the general mechanisms mediating shape selectivity in area V4.

Algorithms↗

PubMed2006

Complete functional characterization of sensory neurons by system identification.

System identification is a growing approach to sensory neurophysiology that facilitates the development of quantitative functional models of sensory processing. This approach provides a clear set of guidelines for combining experimental data with other knowledge about sensory function to obtain a description that optimally predicts the way that neurons process sensory information. This prediction paradigm provides an objective method for evaluating and comparing computational models. In this chapter we review many of the system identification algorithms that have been used in sensory neurophysiology, and we show how they can be viewed as variants of a single statistical inference problem. We then review many of the practical issues that arise when applying these methods to neurophysiological experiments: stimulus selection, behavioral control, model visualization, and validation. Finally we discuss several problems to which system identification has been applied recently, including one important long-term goal of sensory neuroscience: developing models of sensory systems that accurately predict neuronal responses under completely natural conditions.

Animals↗

PubMed2004

Natural stimulus statistics alter the receptive field structure of v1 neurons.

Studies of the primary visual cortex (V1) have produced models that account for neuronal responses to synthetic stimuli such as sinusoidal gratings. Little is known about how these models generalize to activity during natural vision. We recorded neural responses in area V1 of awake macaques to a stimulus with natural spatiotemporal statistics and to a dynamic grating sequence stimulus. We fit nonlinear receptive field models using each of these data sets and compared how well they predicted time-varying responses to a novel natural visual stimulus. On average, the model fit using the natural stimulus predicted natural visual responses more than twice as accurately as the model fit to the synthetic stimulus. The natural vision model produced better predictions in >75% of the neurons studied. This large difference in predictive power suggests that natural spatiotemporal stimulus statistics activate nonlinear response properties in a different manner than the grating stimulus. To characterize this modulation, we compared the temporal and spatial response properties of the model fits. During natural stimulation, temporal responses often showed a stronger late inhibitory component, indicating an effect of nonlinear temporal summation during natural vision. In addition, spatial tuning underwent complex shifts, primarily in the inhibitory, rather than excitatory, elements of the response profile. These differences in late and spatially tuned inhibition accounted fully for the difference in predictive power between the two models. Both the spatial and temporal statistics of the natural stimulus contributed to the modulatory effects.

Animals↗

PubMed2004

Parametric reverse correlation reveals spatial linearity of retinotopic human V1 BOLD response.

Many experiments measuring blood oxygen level dependent (BOLD) signal in functional magnetic resonance imaging (fMRI) data assume that the BOLD signal is predominantly linear in space and time. Previous investigations of temporal linearity have reported that the temporal BOLD response contains both linear and nonlinear components. Here, we used a novel method to investigate spatial linearity of BOLD within area V1. The visual field was divided into regions shaped like wedges, rings, or the intersections of the wedges and rings. The appearance of a flickering checkerboard texture within each region was governed by an independent M-sequence. fMRI data were acquired as the human subjects maintained visual fixation on a central cross. The time series data from each voxel were cross-correlated with every stimulus sequence to estimate each voxel's BOLD responses to all independent regions of the visual field. Linearity by spatial summation was assessed directly by comparing responses to wedges and rings with sums of responses to component patches. The BOLD responses of voxels responding positively to stimuli, measured with independent stimuli subtending several degrees of visual angle, were well predicted by linear spatial summation.

Evoked Potentials, Visual↗

PubMedDate not supplied

Nonlinear V1 responses to natural scenes revealed by neural network analysis.

A key goal in the study of visual processing is to obtain a comprehensive description of the relationship between visual stimuli and neuronal responses. One way to guide the search for models is to use a general nonparametric regression algorithm, such as a neural network. We have developed a multilayer feed-forward network algorithm that can be used to characterize nonlinear stimulus-response mapping functions of neurons in primary visual cortex (area V1) using natural image stimuli. The network is capable of extracting several known V1 response properties such as: orientation and spatial frequency tuning, the spatial phase invariance of complex cells, and direction selectivity. We present details of a method for training networks and visualizing their properties. We also compare how well conventional explicit models and those developed using neural networks can predict novel responses to natural scenes.

Action Potentials↗

PubMedDate not supplied

Predicting neuronal responses during natural vision.

A model that fully describes the response properties of visual neurons must be able to predict their activity during natural vision. While many models have been proposed for the visual system, few have ever been tested against this criterion. To address this issue, we have developed a general framework for fitting and validating nonlinear models of visual neurons using natural visual stimuli. Our approach derives from linear spatiotemporal receptive field (STRF) analysis, which has frequently been used to study the visual system. However, prior to the linear filtering stage typical of STRFs, a linearizing transformation is applied to the stimulus to account for nonlinear response properties. We used this approach to compare two models for neurons in primary visual cortex: a nonlinear Fourier power model, which accounts for spatial phase invariant tuning, and a traditional linear model. We characterized prediction accuracy in terms of the total explainable variance, given intrinsic experimental noise. On average, Fourier power STRFs predicted 40% of explainable variance while linear STRFs were able to predict only 21% of explainable variance. The performance of the Fourier power model provides a benchmark for evaluating more sophisticated models in the future.

Animals↗