The spatial averaging of disparities in brief, static random-dot stereograms

Visual images from the two eyes are transmitted to the brain. Because the eyes are horizontally separated, there is a horizontal disparity between the two images. The amount of disparity between the images of a given point depends on the distance of that point from the viewer's point of fixation. A natural visual environment contains surfaces at many different depths. Therefore, the brain must process a spatial distribution of disparities. How are these disparities spatially put together? Brief (about 200 msec) static Cyclopean random-dot stereograms were used as stimuli for vergence and depth discrimination to answer this question. The results indicated a large averaging region for vergence, and a smaller pooling region for depth discrimination. Vergence responded to the mean disparity of two transparent planes. When a disparate target was present in a fixation plane surround, vergence improved as target size was increased, with a saturation at 3-6 degrees. Depth discrimination thresholds improved with target size, reaching a minimum at 1-3 degrees, but increased for larger targets. Depth discrimination showed a dependence on the extent of a disparity pedestal surrounding the target, consistent with vergence facilitation. Vergence might, therefore, implement a coarse-to-fine reduction in binocular matching noise. Interocular decorrelation can be considered as multiple chance matches at different disparities. The spatial pooling limits found for disparity were replicated when interocular decorrelation was discriminated. The disparity of the random dots also influenced the apparent horizontal. alignment of neighbouring monocular lines. This finding suggests that disparity averaging takes place at an early stage of visual processing. The following possible explanations were considered: 1) Disparities are detected in different spatial frequency channels (Marr and Poggio, 1979). 2) Second-order luminance patterns are matched between the two eyes using non-linear channels. 3) Secondary disparity filters process disparities extracted from linear filters

Role of homeostasis in learning sparse representations

Neurons in the input layer of primary visual cortex in primates develop edge-like receptive fields. One approach to understanding the emergence of this response is to state that neural activity has to efficiently represent sensory data with respect to the statistics of natural scenes. Furthermore, it is believed that such an efficient coding is achieved using a competition across neurons so as to generate a sparse representation, that is, where a relatively small number of neurons are simultaneously active. Indeed, different models of sparse coding, coupled with Hebbian learning and homeostasis, have been proposed that successfully match the observed emergent response. However, the specific role of homeostasis in learning such sparse representations is still largely unknown. By quantitatively assessing the efficiency of the neural representation during learning, we derive a cooperative homeostasis mechanism that optimally tunes the competition between neurons within the sparse coding algorithm. We apply this homeostasis while learning small patches taken from natural images and compare its efficiency with state-of-the-art algorithms. Results show that while different sparse coding algorithms give similar coding results, the homeostasis provides an optimal balance for the representation of natural images within the population of neurons. Competition in sparse coding is optimized when it is fair. By contributing to optimizing statistical competition across neurons, homeostasis is crucial in providing a more efficient solution to the emergence of independent components

Synchronous chaos and broad band gamma rhythm in a minimal multi-layer model of primary visual cortex

Visually induced neuronal activity in V1 displays a marked gamma-band component which is modulated by stimulus properties. It has been argued that synchronized oscillations contribute to these gamma-band activity [... however,] even when oscillations are observed, they undergo temporal decorrelation over very few cycles. This is not easily accounted for in previous network modeling of gamma oscillations. We argue here that interactions between cortical layers can be responsible for this fast decorrelation. We study a model of a V1 hypercolumn, embedding a simplified description of the multi-layered structure of the cortex. When the stimulus contrast is low, the induced activity is only weakly synchronous and the network resonates transiently without developing collective oscillations. When the contrast is high, on the other hand, the induced activity undergoes synchronous oscillations with an irregular spatiotemporal structure expressing a synchronous chaotic state. As a consequence the population activity undergoes fast temporal decorrelation, with concomitant rapid damping of the oscillations in LFPs autocorrelograms and peak broadening in LFPs power spectra. [...] Finally, we argue that the mechanism underlying the emergence of synchronous chaos in our model is in fact very general. It stems from the fact that gamma oscillations induced by local delayed inhibition tend to develop chaos when coupled by sufficiently strong excitation.Comment: 49 pages, 11 figures, 7 table

Effects of Adaptation in a Somatosensory Thalamocortical Circuit

In the mammalian brain, thalamocortical circuits perform the initial stage of processing before information is sent to higher levels of the cerebral cortex. Substantial changes in receptive field properties are produced in the thalamocortical response transformation. In the whisker-to-barrel thalamocortical pathway, the response magnitude of barrel excitatory cells is sensitive to the velocity of whisker deflections, whereas in the thalamus, velocity is only encoded by firing synchrony. The behavior of this circuit can be captured in a model which contains a window of opportunity for thalamic firing synchrony to engage intra-barrel recurrent excitation before being 'damped' by slightly delayed, but strong, local feedforward inhibition. Some remaining aspects of the model that require investigation are: (1) how does adaptation with ongoing and repetitive sensory stimulation affect processing in this circuit and (2) what are the rules governing intra-barrel interactions. By examining sensory processing in thalamic barreloids and cortical barrels, before and after adaptation with repetitive high-frequency whisker stimulation, I have determined that adaptation modifies the operations of the thalamocortical circuit without fundamentally changing it. In the non-adapted state, higher velocities produce larger responses in barrel cells than lower velocities. Similarly, in the adapted barrel, putative excitatory and inhibitory neurons can respond with temporal fidelity to high-frequency whisker deflections if they are of sufficient velocity. Additionally, before and after adaptation, relative to putative excitatory cells, inhibitory cells produce larger responses and are more broadly-tuned for stimulus parameters (e.g., the angle of whisker deflection). In barrel excitatory cells, adaptation is angularly-nonspecific; that is, response suppression is not specific to the angle of the adapting stimulus. The angular tuning of barrel excitatory cells is sharpened and the original angular preference is maintained. This is consistent with intra-barrel interactions being angularly-nonspecific. The maintenance of the original angular preference also suggests that the same thalamocortical inputs determine angular tuning before and after adaptation. In summary, the present findings suggest that adaptation narrows the window of opportunity for synchronous thalamic inputs to engage recurrent excitation so that it can withstand strong, local inhibition. These results from the whisker-to-barrel thalamocortical response transformation are likely to have parallels in other systems

The Spatial Structure of Stimuli Shapes the Timescale of Correlations in Population Spiking Activity

Throughout the central nervous system, the timescale over which pairs of neural spike trains are correlated is shaped by stimulus structure and behavioral context. Such shaping is thought to underlie important changes in the neural code, but the neural circuitry responsible is largely unknown. In this study, we investigate a stimulus-induced shaping of pairwise spike train correlations in the electrosensory system of weakly electric fish. Simultaneous single unit recordings of principal electrosensory cells show that an increase in the spatial extent of stimuli increases correlations at short (~10 ms) timescales while simultaneously reducing correlations at long (~100 ms) timescales. A spiking network model of the first two stages of electrosensory processing replicates this correlation shaping, under the assumptions that spatially broad stimuli both saturate feedforward afferent input and recruit an open-loop inhibitory feedback pathway. Our model predictions are experimentally verified using both the natural heterogeneity of the electrosensory system and pharmacological blockade of descending feedback projections. For weak stimuli, linear response analysis of the spiking network shows that the reduction of long timescale correlation for spatially broad stimuli is similar to correlation cancellation mechanisms previously suggested to be operative in mammalian cortex. The mechanism for correlation shaping supports population-level filtering of irrelevant distractor stimuli, thereby enhancing the population response to relevant prey and conspecific communication inputs. © 2012 Litwin-Kumar et al

Emergent Orientation Selectivity from Random Networks in Mouse Visual Cortex

The connectivity principles underlying the emergence of orientation selectivity in primary visual cortex (V1) of mammals lacking an orientation map (such as rodents and lagomorphs) are poorly understood. We present a computational model in which random connectivity gives rise to orientation selectivity that matches experimental observations. The model predicts that mouse V1 neurons should exhibit intricate receptive fields in the two-dimensional frequency domain, causing a shift in orientation preferences with spatial frequency. We find evidence for these features in mouse V1 using calcium imaging and intracellular whole-cell recordings. Pattadkal et al. show that orientation selectivity can emerge from random connectivity, and offer a distinct perspective for how computations occur in the neocortex. They propose that a random convergence of inputs can provide signals for orientation preference in contrast with the dominant model that requires a precise arrangement.Fil: Pattadkal, Jagruti J.. University of Texas at Austin; Estados UnidosFil: Mato, German. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Instituto Balseiro; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: van Vreeswijk, Carl. Centre National de la Recherche Scientifique; FranciaFil: Priebe, Nicholas J.. University of Texas at Austin; Estados UnidosFil: Hansel, David. Centre National de la Recherche Scientifique; Franci

Towards building a more complex view of the lateral geniculate nucleus: Recent advances in understanding its role

The lateral geniculate nucleus (LGN) has often been treated in the past as a linear filter that adds little to retinal processing of visual inputs. Here we review anatomical, neurophysiological, brain imaging, and modeling studies that have in recent years built up a much more complex view of LGN . These include effects related to nonlinear dendritic processing, cortical feedback, synchrony and oscillations across LGN populations, as well as involvement of LGN in higher level cognitive processing. Although recent studies have provided valuable insights into early visual processing including the role of LGN, a unified model of LGN responses to real-world objects has not yet been developed. In the light of recent data, we suggest that the role of LGN deserves more careful consideration in developing models of high-level visual processing

First- and second-order contributions to depth perception in anti-correlated random dot stereograms.

The binocular energy model of neural responses predicts that depth from binocular disparity might be perceived in the reversed direction when the contrast of dots presented to one eye is reversed. While reversed-depth has been found using anti-correlated random-dot stereograms (ACRDS) the findings are inconsistent across studies. The mixed findings may be accounted for by the presence of a gap between the target and surround, or as a result of overlap of dots around the vertical edges of the stimuli. To test this, we assessed whether (1) the gap size (0, 19.2 or 38.4 arc min) (2) the correlation of dots or (3) the border orientation (circular target, or horizontal or vertical edge) affected the perception of depth. Reversed-depth from ACRDS (circular no-gap condition) was seen by a minority of participants, but this effect reduced as the gap size increased. Depth was mostly perceived in the correct direction for ACRDS edge stimuli, with the effect increasing with the gap size. The inconsistency across conditions can be accounted for by the relative reliability of first- and second-order depth detection mechanisms, and the coarse spatial resolution of the latter