A Nonlinear Model of Spatiotemporal Retinal Processing: Simulations of X and Y Retinal Ganglion Cell Behavior

This article describes a nonlinear model of neural processing in the vertebrate retina, comprising model photoreceptors, model push-pull bipolar cells, and model ganglion cells. Previous analyses and simulations have shown that with a choice of parameters that mimics beta cells, the model exhibits X-like linear spatial summation (null response to contrast-reversed gratings) in spite of photoreceptor nonlinearities; on the other hand, a choice of parameters that mimics alpha cells leads to Y-like frequency doubling. This article extends the previous work by showing that the model can replicate qualitatively many of the original findings on X and Y cells with a fixed choice of parameters. The results generally support the hypothesis that X and Y cells can be seen as functional variants of a single neural circuit. The model also suggests that both depolarizing and hyperpolarizing bipolar cells converge onto both ON and OFF ganglion cell types. The push-pull connectivity enables ganglion cells to remain sensitive to deviations about the mean output level of nonlinear photoreceptors. These and other properties of the push-pull model are discussed in the general context of retinal processing of spatiotemporal luminance patterns.Alfred P. Sloan Research Fellowship (BR-3122); Air Force Office of Scientific Research (F49620-92-J-0499

Transformation of stimulus correlations by the retina

Redundancies and correlations in the responses of sensory neurons seem to waste neural resources but can carry cues about structured stimuli and may help the brain to correct for response errors. To assess how the retina negotiates this tradeoff, we measured simultaneous responses from populations of ganglion cells presented with natural and artificial stimuli that varied greatly in correlation structure. We found that pairwise correlations in the retinal output remained similar across stimuli with widely different spatio-temporal correlations including white noise and natural movies. Meanwhile, purely spatial correlations tended to increase correlations in the retinal response. Responding to more correlated stimuli, ganglion cells had faster temporal kernels and tended to have stronger surrounds. These properties of individual cells, along with gain changes that opposed changes in effective contrast at the ganglion cell input, largely explained the similarity of pairwise correlations across stimuli where receptive field measurements were possible.Comment: author list corrected in metadat

Gating Input to Visual Cortex by Feedback to LGN

Anatomical studies have documented massive back-projections from higher to lower visual cortices and to the lateral geniculate nucleus (LGN). The large number of synapses from these sources suggest that they should have a profound influence on the information carried by feed-forward inputs to these cells. However, the functional role of these connections is unclear. In order to explore the role of the feedback connections, we have recorded spike trains from electrodes placed in LGN in the macaque monkey under sufenta anesthesia, and have compared LGN cells' activity with and without suppression by cooling of feedback from primary visual cortex (V1). Normally, magno and parvo LGN cells show a wide range over which their responses are proportional to stimulus contrast. Inactivation of V1 feedback causes LGN cells to become more nonlinear and less sensitive to high contrast than during normal conditions. Responses during V1 inactivation have a similar shape to those of retinal ganglion cells. We have also tested the properties of the so-called extended surround as they relate to cortical activity and to influences on responses to LGN stimulation. A model of this data suggests an interpretation in terms of two fnuctional components of feedback: a contrast-dependent component which dominates at high input contrast, and a constant baseline level of inhibitory feedback. We also show that the influence of the extended surround on the classical center mechanism is more complicated than a simple integration model.National Institutes of Health (EY-05156); Office of Naval Research (N00014-95-1-409

A Neural Model of Surface Perception: Lightness, Anchoring, and Filling-in

This article develops a neural model of how the visual system processes natural images under variable illumination conditions to generate surface lightness percepts. Previous models have clarified how the brain can compute the relative contrast of images from variably illuminate scenes. How the brain determines an absolute lightness scale that "anchors" percepts of surface lightness to us the full dynamic range of neurons remains an unsolved problem. Lightness anchoring properties include articulation, insulation, configuration, and are effects. The model quantatively simulates these and other lightness data such as discounting the illuminant, the double brilliant illusion, lightness constancy and contrast, Mondrian contrast constancy, and the Craik-O'Brien-Cornsweet illusion. The model also clarifies the functional significance for lightness perception of anatomical and neurophysiological data, including gain control at retinal photoreceptors, and spatioal contrast adaptation at the negative feedback circuit between the inner segment of photoreceptors and interacting horizontal cells. The model retina can hereby adjust its sensitivity to input intensities ranging from dim moonlight to dazzling sunlight. A later model cortical processing stages, boundary representations gate the filling-in of surface lightness via long-range horizontal connections. Variants of this filling-in mechanism run 100-1000 times faster than diffusion mechanisms of previous biological filling-in models, and shows how filling-in can occur at realistic speeds. A new anchoring mechanism called the Blurred-Highest-Luminance-As-White (BHLAW) rule helps simulate how surface lightness becomes sensitive to the spatial scale of objects in a scene. The model is also able to process natural images under variable lighting conditions.Air Force Office of Scientific Research (F49620-01-1-0397); Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409); Office of Naval Research (N00014-01-1-0624

View-Invariant Object Category Learning, Recognition, and Search: How Spatial and Object Attention Are Coordinated Using Surface-Based Attentional Shrouds

Air Force Office of Scientific Research (F49620-01-1-0397); National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624

Linking Visual Cortical Development to Visual Perception

Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409); National Science Foundation (IRI-97-20333); Office of Naval Research (N00014-95-1-0657

Brain Differently Changes Its Algorithms in Parallel Processing of Visual Information

Feedback from the visual cortex (Vl) to the Lateral Geniculate Nucleus (LGN) in macaque monkey increase contrast gain of LGN neurons for black and white (B&W) and for color (C) stimuli. LGN parvocellular cells responses to B&W gratings are enhanced by feedback multiplicatively and in contrast independent manner. However, in magnocellular neurons corticofugal pathways enhance cells responses in a contrast~dependent non-linear manner. For C stimuli cortical feedback enhances parvocellular neurons responses in a very strong contrast-dependent manner. Based on these results [13] we propose a model which includes excitatory and inhibitory effects on cells activity (shunting equations) in retina and LGN while taking into account the anatomy of cortical feedback connections. The main mechanisms related to different algorithms of the data processing in the visual brain are differences in feedback properties from Vl to parvocellular (PC) and to magnocellular (MC) neurons. Descending pathways from Vl change differently receptive field (RF) structure of PC and MC cells. For B&W stimuli, in PC cells feedback changes gain similarly in the RF center and in the RF surround, leaving PC RF structure invariant. However, feedback influence MC cells in two ways: directly and through LGN interneurons, which together changes gain and sizes of their RF center differently than gain and size of the RF surround. For C stimuli PC cells operate like MC cells for B&W. The first mechanism extracts from the stimulus an important features in a independent way from other stimulus parameters, whereas the second channel changes its tuning properties as a function of other stimulus attributes like contrast and/or spatial extension. The model suggests novel idea about the possible functional role of PC and MC pathways

A Unified Model of Spatiotemporal Processing in the Retina

A computational model of visual processing in the vertebrate retina provides a unified explanation of a range of data previously treated by disparate models. Three results are reported here: the model proposes a functional explanation for the primary feed-forward retinal circuit found in vertebrate retinae, it shows how this retinal circuit combines nonlinear adaptation with the desirable properties of linear processing, and it accounts for the origin of parallel transient (nonlinear) and sustained (linear) visual processing streams as simple variants of the same retinal circuit. The retina, owing to its accessibility and to its fundamental role in the initial transduction of light into neural signals, is among the most extensively studied neural structures in the nervous system. Since the pioneering anatomical work by Ramón y Cajal at the turn of the last century[1], technological advances have abetted detailed descriptions of the physiological, pharmacological, and functional properties of many types of retinal cells. However, the relationship between structure and function in the retina is still poorly understood. This article outlines a computational model developed to address fundamental constraints of biological visual systems. Neurons that process nonnegative input signals-such as retinal illuminance-are subject to an inescapable tradeoff between accurate processing in the spatial and temporal domains. Accurate processing in both domains can be achieved with a model that combines nonlinear mechanisms for temporal and spatial adaptation within three layers of feed-forward processing. The resulting architecture is structurally similar to the feed-forward retinal circuit connecting photoreceptors to retinal ganglion cells through bipolar cells. This similarity suggests that the three-layer structure observed in all vertebrate retinae[2] is a required minimal anatomy for accurate spatiotemporal visual processing. This hypothesis is supported through computer simulations showing that the model's output layer accounts for many properties of retinal ganglion cells[3],[4],[5],[6]. Moreover, the model shows how the retina can extend its dynamic range through nonlinear adaptation while exhibiting seemingly linear behavior in response to a variety of spatiotemporal input stimuli. This property is the basis for the prediction that the same retinal circuit can account for both sustained (X) and transient (Y) cat ganglion cells[7] by simple morphological changes. The ability to generate distinct functional behaviors by simple changes in cell morphology suggests that different functional pathways originating in the retina may have evolved from a unified anatomy designed to cope with the constraints of low-level biological vision.Sloan Fellowshi

A Simple Cell Model with Multiple Spatial Frequency Selectivity and Linear/Non-Linear Response Properties

A model is described for cortical simple cells. Simple cells are selective for local contrast polarity, signaling light-dark and dark-light transitions. The proposed new architecture exhibits both linear and non-linear properties of simple cells. Linear responses are obtained by integration of the input stimulus within subfields of the cells, and by combinations of them. Non-linear behavior can be seen in the selectivity for certain features that can be characterized by the spatial arrangement of activations generated by initial on- and off-cells (center-surround). The new model also exhibits spatial frequency selectivity with the generation of multi-scale properties being based on a single-scale band-pass input that is generated by the initial (retinal) center-surround processing stage.German BMFT grant (413-5839-01 IN 101 C/1); CNPq and NUTES/UFRJ, Brazi