Bioplausible multiscale filtering in retino-cortical processing as a mechanism in perceptual grouping

Why does our visual system fail to reconstruct reality, when we look at certain patterns? Where do Geometrical illusions start to emerge in the visual pathway? How far should we take computational models of vision with the same visual ability to detect illusions as we do? This study addresses these questions, by focusing on a specific underlying neural mechanism involved in our visual experiences that affects our final perception. Among many types of visual illusion, Geometrical and, in particular, Tilt Illusions are rather important, being characterized by misperception of geometric patterns involving lines and tiles in combination with contrasting orientation, size or position. Over the last decade, many new neurophysiological experiments have led to new insights as to how, when and where retinal processing takes place, and the encoding nature of the retinal representation that is sent to the cortex for further processing. Based on these neurobiological discoveries, we provide computer simulation evidence from modelling retinal ganglion cells responses to some complex Tilt Illusions, suggesting that the emergence of tilt in these illusions is partially related to the interaction of multiscale visual processing performed in the retina. The output of our low-level filtering model is presented for several types of Tilt Illusion, predicting that the final tilt percept arises from multiple-scale processing of the Differences of Gaussians and the perceptual interaction of foreground and background elements. Our results suggest that this model has a high potential in revealing the underlying mechanism connecting low-level filtering approaches to mid- and high-level explanations such as Anchoring theory and Perceptual grouping.Comment: 23 pages, 8 figures, Brain Informatics journal: Full text access: https://link.springer.com/article/10.1007/s40708-017-0072-

Brightness Illusions as Optimal Percepts - Technical Report: NUIM-CS-TR-2006-02

We show that Mach bands and a number of other low-level brightness illusions can be accounted for by assuming that the perceptual system performs simple Bayesian inference using a Gaussian image prior with noisy retinal gangion cells. This theory accounts for phenomena which have proven problematic for simple energy-based and lateral-interaction models while avoiding the complexities of mid-level vision theories that involve the estimation of structure and albedo

Aerospace Medicine and Biology: A continuing bibliography, supplement 216

One hundred twenty reports, articles, and other documents introduced into the NASA scientific and technical information system in January 1981 are listed. Topics include: sanitary problems; pharmacology; toxicology; safety and survival; life support systems; exobiology; and personnel factors

Efficient coding of natural scenes improves neural system identification

Neural system identification aims at learning the response function of neurons to arbitrary stimuli using experimentally recorded data, but typically does not leverage normative principles such as efficient coding of natural environments. Visual systems, however, have evolved to efficiently process input from the natural environment. Here, we present a normative network regularization for system identification models by incorporating, as a regularizer, the efficient coding hypothesis, which states that neural response properties of sensory representations are strongly shaped by the need to preserve most of the stimulus information with limited resources. Using this approach, we explored if a system identification model can be improved by sharing its convolutional filters with those of an autoencoder which aims to efficiently encode natural stimuli. To this end, we built a hybrid model to predict the responses of retinal neurons to noise stimuli. This approach did not only yield a higher performance than the “stand-alone” system identification model, it also produced more biologically-plausible filters. We found these results to be consistent for retinal responses to different stimuli and across model architectures. Moreover, our normatively regularized model performed particularly well in predicting responses of direction-of-motion sensitive retinal neurons. In summary, our results support the hypothesis that efficiently encoding environmental inputs can improve system identification models of early visual processing

Virtual Retina : a biological retina model and simulator, with contrast gain control

A detailed retina model is proposed, that transforms a video sequence into a set of spike trains, as those emitted by retinal ganglion cells. It includes a linear model of filtering in the Outer Plexiform Layer (OPL), a contrast gain control mechanism modeling the non-linear feedback of some amacrine cells on bipolar cells, and a spike generation process modeling ganglion cells. A strength of the model is that each of its features can be associated to a precise physiological signification and location. The resulting retina model can simulate physiological recordings on mammalian retinas, including such non-linearities as cat Y cells, or contrast gain control. Furthermore, the model has been implemented on a large-scale simulator that can emulate the spikes of up to 100,000 neurons

Computational model of MST neuron receptive field and interaction effect for the perception of self-motion

Biologically plausible approach is an alternative to conventional engineering approaches when developing algorithms for intelligent systems. It is apparent that biologically inspired algorithms may yield more expensive calculations when comparing its run time to the more commonly used engineering algorithms. However, biologically inspired approaches have great potential in generating better and more accurate outputs as healthy human brains. Therefore more and more new and exciting researches are being experimented everyday in hope to develop better models of our brain that can be utilized by the machines. This thesis work is an effort to design and implement a computational model of neurons from the visual cortex\u27s MST area (medial superior temporal area). MST\u27s primary responsibility is detecting self-motion from optic flow stimulus that are segmented from the visual input. The computational models are to be built with dual Gaussian functions and genetic algorithm as its principle training method, from the data collected through lab monkey\u27s MST neurons. The resulting computational models can be used in further researches as part of motion detection mechanism by machine vision applications, which may prove to be an effective alternative motion detection algorithm in contrast to the conventional computer vision algorithms such as frame differencing. This thesis work will also explore the interaction effect that has been discovered from the newly gathered data, provided by University of Rochester Medical Center, Neurology Department

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

Temporal information processing across primary visual cortical layers in normal and red light reared tree shrews.

Visual neuroscience research has benefitted from decades of efforts of comparative studies of different species, since exploring and understanding the diversity of functional properties of visual system in different species has helped us identify both general organization rules and unique traits of certain species. In this study, spatio-temporal receptive fields (STRFs), together with some other functional properties (etc. stimulus preference to different visual stimuli, orientation tuning, temporal frequency tuning and the F1/F0 ratio of responses to sine-wave grating stimuli), of primary visual cortex (V1) cells were measured in normally reared and red-light reared tree shrews (Tupaia), a species considered the closest non-primate relative to human being. All data were sampled in anesthetized animals using extracellular recording techniques. In the current study, a diversity of STRFs structures were found in tree shrew V1, and the STRFs found were classified into two categories, Type I receptive fields (RFs) that had spatially discontinuous on- and off-regions, or had spatio-temporal inseparable RFs, and Type II RFs that had spatially overlapped circular or elliptical on- and off- regions, and spatio-temporal separable RFs. Spatial and temporal profile analysis indicated this Type I and Type II classification did not correspond to simple and complex RF types previously described in primates and carnivores. It was also found in the current study that the linear prediction based on STRFs did not predict temporal frequency tuning, orientation tuning or the F1/F0 ratio very well in tree shrew V1. In tree shrew V1, both low-pass and band-pass cells for temporal frequency were found, and the proportion of cells with different types of tuning curves also differed across layers, resulting in a low-pass filter between layer II/II and layer IV. Last but not least, it was found in this study that red light rearing after birth changes the population stimulus preference in layer IV in tree shrew V1

Retinal ganglion cells : physiology and prosthesis

The retina is responsible for encoding different aspects of the visual world. Light enters the eyes and is converted by the photoreceptors into electrochemical signals. These signals are processed by the retinal network and proceed afferently to the brain via the axons of the retinal ganglion cells (RGCs). The RGCs outputs are in the form of action potentials (spikes), which encrypt the visual information in terms of spike shape, firing frequencies, and the firing patterns. When the photoreceptors are gone due to disease, vision is lost. The idea of a retinal prosthesis is to activate the surviving RGCs by electrical stimulation in order to recreate vision. In this thesis, I have studied the physiological properties of the RGCs, and reconstructed natural RGC spike trains by electrical stimulation. Chapter 1 introduces the anatomy of the retina and the retinal neurons. How the RGCs respond to light. Electrical stimulation is also discussed. A brief historical summary of the receptive field properties and cell physiology is also presented. Chapter 2 characterizes the intrinsic properties of 16 morphologically defined types of rat RGCs. The intrinsic properties include the biophysical properties due to morphology and dendritic stratification, in addition to physiological properties such as firing behaviours. These properties are also compared with the cat RGC intrinsic properties in order to investigate the variations between the morphologically similar RGCs of the two species. The results suggest that the RGCs among species, even with similar morphologies, do not have conservative intrinsic properties. Chapter 3 examines the details of the spiking properties of the different rat RGC types. Spikes are initiated at the axonal initial segment. A 'single' spike recorded at the soma consists of an axonal spike and a somatic spike. The existence of the two spikes can be recognized by two humps in the phase plot, and further revealed in the higher derivatives of the membrane potential. A principal component analysis shows that the parameters extracted from the phase plots are very useful for a model-independent rat RGC classification. Chapter 4 establishes the foundations for electrical stimulation of the retina. The question is to what extent optimum placement of the stimulating and reference electrodes might be affected by anatomical location. Here we placed the stimulating electrode above or below the retinal inner limiting membrane and found no statistical difference between the thresholds. In addition, reflective axonal spikes from the cut end are discussed. Chapter 5 combines the knowledge obtained in the previous chapters for the sole purpose of reproducing natural RGC outputs when using electrical stimulation. The light responses of the eye under saccadic movements were recorded and used to form the stimulus patterns. The reconstructions were performed on the brisk-transient (BT) and the brisk-sustained (BS) RGCs. Our results suggested that BT RGCs are more capable of following the stimulated stimulus patterns over a wide range of frequencies than the BS RGCs. Chapter 6 concludes the whole thesis

On the contrast-dependence of crowding

Visual clutter affects our ability to see: objects that would be identifiable on their own, may become unrecognizable when presented close together ("crowding") -- but the psychophysical characteristics of crowding have resisted simplification. Image properties initially thought to produce crowding have paradoxically yielded unexpected results, e.g., adding flanking objects can ameliorate crowding (Manassi, Sayim et al., 2012; Herzog, Sayim et al., 2015; Pachai, Doerig et al., 2016). The resulting theory revisions have been sufficiently complex and specialized as to make it difficult to discern what principles may underlie the observed phenomena. A generalized formulation of simple visual contrast energy is presented, arising from straightforward analyses of center and surround neurons in the early visual stream. Extant contrast measures, such as RMS contrast, are easily shown to fall out as reduced special cases. The new generalized contrast energy metric surprisingly predicts the principal findings of a broad range of crowding studies. These early crowding phenomena may thus be said to arise predominantly from contrast, or are, at least, severely confounded by contrast effects. (These findings may be distinct from accounts of other, likely downstream, "configural" or "semantic" instances of crowding, suggesting at least two separate forms of crowding that may resist unification.) The new fundamental contrast energy formulation provides a candidate explanatory framework that addresses multiple psychophysical phenomena beyond crowding.Comment: Journal of Vision, in pres