The effects of noise on binocular rivalry waves: a stochastic neural field model

We analyse the effects of extrinsic noise on traveling waves of visual perception in a competitive neural field model of binocular rivalry. The model consists of two one-dimensional excitatory neural fields, whose activity variables represent the responses to left-eye and right-eye stimuli, respectively. The two networks mutually inhibit each other, and slow adaptation is incorporated into the model by taking the network connections to exhibit synaptic depression. We first show how, in the absence of any noise, the system supports a propagating composite wave consisting of an invading activity front in one network co-moving with a retreating front in the other network. Using a separation of time scales and perturbation methods previously developed for stochastic reaction-diffusion equations, we then show how multiplicative noise in the activity variables leads to a diffusive–like displacement (wandering) of the composite wave from its uniformly translating position at long time scales, and fluctuations in the wave profile around its instantaneous position at short time scales. The multiplicative noise also renormalizes the mean speed of the wave. We use our analysis to calculate the first passage time distribution for a stochastic rivalry wave to travel a fixed distance, which we find to be given by an inverse Gaussian. Finally, we investigate the effects of noise in the depression variables, which under an adiabatic approximation leads to quenched disorder in the neural fields during propagation of a wave

An information theoretic approach to the functional classification of neurons

A population of neurons typically exhibits a broad diversity of responses to sensory inputs. The intuitive notion of functional classification is that cells can be clustered so that most of the diversity is captured in the identity of the clusters rather than by individuals within clusters. We show how this intuition can be made precise using information theory, without any need to introduce a metric on the space of stimuli or responses. Applied to the retinal ganglion cells of the salamander, this approach recovers classical results, but also provides clear evidence for subclasses beyond those identified previously. Further, we find that each of the ganglion cells is functionally unique, and that even within the same subclass only a few spikes are needed to reliably distinguish between cells.Comment: 13 pages, 4 figures. To appear in Advances in Neural Information Processing Systems (NIPS) 1

Spatial summation of individual cones in human color vision.

The human retina contains three classes of cone photoreceptors each sensitive to different portions of the visual spectrum: long (L), medium (M) and short (S) wavelengths. Color information is computed by downstream neurons that compare relative activity across the three cone types. How cone signals are combined at a cellular scale has been more difficult to resolve. This is especially true near the fovea, where spectrally-opponent neurons in the parvocellular pathway draw excitatory input from a single cone and thus even the smallest stimulus projected through natural optics will engage multiple color-signaling neurons. We used an adaptive optics microstimulator to target individual and pairs of cones with light. Consistent with prior work, we found that color percepts elicited from individual cones were predicted by their spectral sensitivity, although there was considerable variability even between cones within the same spectral class. The appearance of spots targeted at two cones were predicted by an average of their individual activations. However, two cones of the same subclass elicited percepts that were systematically more saturated than predicted by an average. Together, these observations suggest both spectral opponency and prior experience influence the appearance of small spots

Peripheral visual response time to colored stimuli imaged on the horizontal meridian

Two male observers were administered a binocular visual response time task to small (45 min arc), flashed, photopic stimuli at four dominant wavelengths (632 nm red; 583 nm yellow; 526 nm green; 464 nm blue) imaged across the horizontal retinal meridian. The stimuli were imaged at 10 deg arc intervals from 80 deg left to 90 deg right of fixation. Testing followed either prior light adaptation or prior dark adaptation. Results indicated that mean response time (RT) varies with stimulus color. RT is faster to yellow than to blue and green and slowest to red. In general, mean RT was found to increase from fovea to periphery for all four colors, with the curve for red stimuli exhibiting the most rapid positive acceleration with increasing angular eccentricity from the fovea. The shape of the RT distribution across the retina was also found to depend upon the state of light or dark adaptation. The findings are related to previous RT research and are discussed in terms of optimizing the color and position of colored displays on instrument panels

A Neural Model of Motion Processing and Visual Navigation by Cortical Area MST

Cells in the dorsal medial superior temporal cortex (MSTd) process optic flow generated by self-motion during visually-guided navigation. A neural model shows how interactions between well-known neural mechanisms (log polar cortical magnification, Gaussian motion-sensitive receptive fields, spatial pooling of motion-sensitive signals, and subtractive extraretinal eye movement signals) lead to emergent properties that quantitatively simulate neurophysiological data about MSTd cell properties and psychophysical data about human navigation. Model cells match MSTd neuron responses to optic flow stimuli placed in different parts of the visual field, including position invariance, tuning curves, preferred spiral directions, direction reversals, average response curves, and preferred locations for stimulus motion centers. The model shows how the preferred motion direction of the most active MSTd cells can explain human judgments of self-motion direction (heading), without using complex heading templates. The model explains when extraretinal eye movement signals are needed for accurate heading perception, and when retinal input is sufficient, and how heading judgments depend on scene layouts and rotation rates.Defense Research Projects Agency (N00014-92-J-4015); Office of Naval Research (N00014-92-J-1309, N00014-95-1-0409, N00014-95-1-0657, N00014-91-J-4100, N0014-94-I-0597); Air Force Office of Scientific Research (F49620-92-J-0334)

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

Intravitreal Administration of Human Bone Marrow CD34+ Stem Cells in a Murine Model of Retinal Degeneration.

PurposeIntravitreal murine lineage-negative bone marrow (BM) hematopoietic cells slow down retinal degeneration. Because human BM CD34+ hematopoietic cells are not precisely comparable to murine cells, this study examined the effect of intravitreal human BM CD34+ cells on the degenerating retina using a murine model.MethodsC3H/HeJrd1/rd1 mice, immunosuppressed systemically with tacrolimus and rapamycin, were injected intravitreally with PBS (n = 16) or CD34+ cells (n = 16) isolated from human BM using a magnetic cell sorter and labeled with enhanced green fluorescent protein (EGFP). After 1 and 4 weeks, the injected eyes were imaged with scanning laser ophthalmoscopy (SLO)/optical coherence tomography (OCT) and tested with electroretinography (ERG). Eyes were harvested after euthanasia for immunohistochemical and microarray analysis of the retina.ResultsIn vivo SLO fundus imaging visualized EGFP-labeled cells within the eyes following intravitreal injection. Simultaneous OCT analysis localized the EGFP-labeled cells on the retinal surface resulting in a saw-toothed appearance. Immunohistochemical analysis of the retina identified EGFP-labeled cells on the retinal surface and adjacent to ganglion cells. Electroretinography testing showed a flat signal both at 1 and 4 weeks following injection in all eyes. Microarray analysis of the retina following cell injection showed altered expression of more than 300 mouse genes, predominantly those regulating photoreceptor function and maintenance and apoptosis.ConclusionsIntravitreal human BM CD34+ cells rapidly home to the degenerating retinal surface. Although a functional benefit of this cell therapy was not seen on ERG in this rapidly progressive retinal degeneration model, molecular changes in the retina associated with CD34+ cell therapy suggest potential trophic regenerative effects that warrant further exploration

Suboptimal eye movements for seeing fine details.

Human eyes are never stable, even during attempts of maintaining gaze on a visual target. Considering transient response characteristics of retinal ganglion cells, a certain amount of motion of the eyes is required to efficiently encode information and to prevent neural adaptation. However, excessive motion of the eyes leads to insufficient exposure to the stimuli, which creates blur and reduces visual acuity. Normal miniature eye movements fall in between these extremes, but it is unclear if they are optimally tuned for seeing fine spatial details. We used a state-of-the-art retinal imaging technique with eye tracking to address this question. We sought to determine the optimal gain (stimulus/eye motion ratio) that corresponds to maximum performance in an orientation-discrimination task performed at the fovea. We found that miniature eye movements are tuned but may not be optimal for seeing fine spatial details