Topographic Shear and the Relation of Ocular Dominance Columns to Orientation Columns in Prime and Cat Visual Cortex

Shear has been known to exist for many years in the topographic structure of prirnary visual cortex, but has received little attention in the modeling literature. Although the topographic map of V1 is largely conformal (i.e. zero shear), several groups have observed topographic shear in the region of the V1/V2 border. Furthennore, shear has also been revealed by anisotropy of cortical magnification factor within a single ocular dominance colunm. In the present paper, we make a functional hypothesis: the major axis of the topographic shear tensor provides cortical neurons with a preferred direction of orientation tuning. We demonstrate that isotropic neuronal summation of a sheared topographic map, in the presence of additional random shear can provide the major features of corlical functional architecture with the ocular dominance column system acting as the principal source of the shear tensor. The major principal axis of the shear tensor determines the direction and its eigenvalues the relative strength of cortical orientation preference. This hypothesis is then shown to be qualitatively consistent with a variety of experimental results on cat and monkey orientation column properties obtained from optical recording and from other anatomical and physiological techniques. In addition, we show that a recent result of (Das and Gilbert, 1997) is consistent with an infinite set of parameterized solutions for the cortical map. We exploit this freedom to choose a particular instance of the Das-Gilbert solution set which is consistent with the full range of local spatial structure in V1. These results suggest that further relationships between ocular dominance columns, orientation columns, and local topography may be revealed by experimental testing

Anatomy and Physiology of Macaque Visual Cortical Areas V1, V2, and V5/MT : Bases for Biologically Realistic Models

The cerebral cortex of primates encompasses multiple anatomically and physiologically distinct areas processing visual information. Areas V1, V2, and VS/MT are conserved across mammals and are central for visual behavior. To facilitate the generation of biologically accurate computational models of primate early visual processing, here we provide an overview of over 350 published studies of these three areas in the genus Macaca, whose visual system provides the closest model for human vision. The literature reports 14 anatomical connection types from the lateral geniculate nucleus of the thalamus to V1 having distinct layers of origin or termination, and 194 connection types between V1, V2, and VS, forming multiple parallel and interacting visual processing streams. Moreover, within V1, there are reports of 286 and 120 types of intrinsic excitatory and inhibitory connections, respectively. Physiologically, tuning of neuronal responses to 11 types of visual stimulus parameters has been consistently reported. Overall, the optimal spatial frequency (SF) of constituent neurons decreases with cortical hierarchy. Moreover, VS neurons are distinct from neurons in other areas for their higher direction selectivity, higher contrast sensitivity, higher temporal frequency tuning, and wider SF bandwidth. We also discuss currently unavailable data that could be useful for biologically accurate models.Peer reviewe

Binocular interactions in human vision

Early visual processing is subject to binocular interactions because cells in striate cortex show binocular responses and ocular dominance (Hubel & Weisel, 1968). The work presented in this thesis suggests that these physiological interactions can be revealed in psychophysical experiments using normal human observers. In the region corresponding to the blind spot, where binocular interactions differ from areas of the visual field which are represented by two eyes, monocular contrast sensitivity is increased. This finding can be partially explained by an absence of normal binocular interactions in this location (Chapter 2). A hemianopic patient was studied in an attempt to discover whether the effect in normal observers was mediated by either a mechanism in striate cortex or via a subcortical pathway. However, the results were unable to distinguish between these two explanations (Chapter 3).In a visual search task, no difference in reaction time was observed for targets presented to the region corresponding to the blind spot compared with targets presented to adjacent binocularly represented areas of the visual field. Since performance was unaffected by the monocularity of the region corresponding to the blind, pop-out for orientation may be mediated beyond striate cortex where cells are binocularly balanced (Chapter 5). Further support for this contention was provided by studies of orientation pop-out in central vision which found that dichoptic presentation of stimuli did not affect the degree of pop-out obtained and that in general, visual search for a target based solely on eye of origin is impossible (Chapter 6). However, a task that measured orientation difference sensitivity more directly than the search experiments, found that thresholds were higher for dichoptically presented stimuli. This suggests the involvement of neurons that receive a weighted input from each eye. A model of orientation difference coding can account for the results by assuming that the range of inhibition across which orientation differences are coded is narrower for dichoptic stimuli leading to a greater resolvable orientation difference (Chapter 7)

Point-spread function of the BOLD response across columns and cortical depth in human extra-striate cortex

Columns and layers are fundamental organizational units of the brain. Well known examples of cortical columns are the ocular dominance columns (ODCs) in primary visual cortex and the column-like stripe-based arrangement in the second visual area V2. The spatial scale of columns and layers is beyond the reach of conventional neuroimaging, but the advent of high field magnetic resonance imaging (MRI) scanners (UHF, 7 Tesla and above) has opened the possibility to acquire data at this spatial scale, in-vivo and non-invasively in humans. The most prominent non-invasive technique to measure brain function is blood oxygen level dependent (BOLD) fMRI, measuring brain activity indirectly, via changes in hemodynamics. A key determinant of the ability of high-resolution BOLD fMRI to accurately resolve columns and layers is the point-spread function (PSF) of the BOLD response in relation to the spatial extent of neuronal activity. In this study we take advantage of the stripe-based arrangement present in visual area V2, coupled with sub-millimetre anatomical and gradient-echo BOLD (GE BOLD) acquisition at 7 T to obtain PSF estimates and along cortical depth in human participants. Results show that the BOLD PSF is maximal in the superficial part of the cortex (1.78 mm), and it decreases with increasing cortical depth (0.83 mm close to white matter)

Recovery from visual dysfunction following mild traumatic brain injury is associated with adaptive reorganization of retinal inputs to lateral geniculate nucleus in the mouse model utilizing central fluid percussion injury.

Traumatic brain injury (TBI) is a leading cause of morbidity and mortality nationwide. Prevalence of mild TBI (mTBI) vastly outnumbers more severe forms however the associated morbidity has only recently gained public attention. Visual dysfunction is a significant component of mTBI associated morbidity with recovery of function linked with improvement in global outcomes. Examination of sensory and motor pathways in other brain injury paradigms support that recovery is largely dependent on adaptive plasticity of remaining connections. Current examinations of visual function recovery following mTBI is limited to identifying evidence for recovery and objective evidence for adaptive plasticity is limited. Therefore, to understand the mechanisms behind visual recovery in mTBI, we utilize a mouse model to examine the changes in the downstream target of retinal ganglion cells (RGC) in the formed vision pathway, the lateral geniculate nucleus (LGN). Using techniques designed to identify structural changes as well as electrophysiologic connectivity we aimed to identify if deafferentation due to experimental mTBI is met with adaptive structural and electrophysiologic reorganization of inputs to LGN relay cells, to determine if they may contribute to recovery of vision over time. Examination of ensuing deafferentation in LGN was performed using a combination of anterograde tract tracing with cholera toxin B conjugated fluorescent probes, immunohistochemistry targeting retinal ganglion cell axon terminals, and a transgenic mouse in which a subpopulation of retinal ganglion cells are labelled with green fluorescent protein. Our studies were designed to capture structural reorganization in specific subpopulations of retinal ganglion cells and determine if ensuing reorganization violated projection patterns established during normal development and refinement of the retinal geniculate pathway. Additionally, our studies examined the electrophysiologic responses of relay neurons in the lateral geniculate nucleus to stimulation of the optic tract as a function of time following injury. Using ex-vivo patch clamp recording of LGN relay neurons, we examined responses of these cells to stimulation of the optic tract following mTBI. Our findings demonstrated intact short-term depression at the retinal geniculate synapse following injury, which is a mechanism through which LGN relay neurons establish functional connectivity from retinal inputs. This innate mechanism of short-term plasticity likely uncovers latent connectivity between the remaining retinal inputs and LGN relay neurons to provide new connectivity for functional recovery. These studies support the premise that recovery of function in the visual axis following mild TBI is dependent on adaptive structural and electrophysiologic reorganization within the lateral geniculate nucleus

Brain at work : time, sparseness and superposition principles

Abstract : Many studies explored mechanisms through which the brain encodes sensory inputs allowing a coherent behavior. The brain could identify stimuli via a hierarchical stream of activity leading to a cardinal neuron responsive to one particular object. The opportunity to record from numerous neurons offered investigators the capability of examining simultaneously the functioning of many cells. These approaches suggested encoding processes that are parallel rather than serial. Binding the many features of a stimulus may be accomplished through an induced synchronization of cell’s action potentials. These interpretations are supported by experimental data and offer many advantages but also several shortcomings. We argue for a coding mechanism based on a sparse synchronization paradigm. We show that synchronization of spikes is a fast and efficient mode to encode the representation of objects based on feature bindings. We introduce the view that sparse synchronization coding presents an interesting venue in probing brain encoding mechanisms as it allows the functional establishment of multilayered and time-conditioned neuronal networks or multislice networks. We propose a model based on integrate-and-fire spiking neurons

Neural representation of complex motion in the primate cortex

This dissertation is concerned with how information about the environment is represented by neural activity in the primate brain. More specifically, it contains several studies that explore the representation of visual motion in the brains of humans and nonhuman primates through behavioral and physiological measures. The majority of this work is focused on the activity of individual neurons in the medial superior temporal area (MST) – a high-level, extrastriate area of the primate visual cortex. The first two studies provide an extensive review of the scientific literature on area MST. The area’s prominent role at the intersection of low-level, bottom-up, sensory processing and high-level, top-down mechanisms is highlighted. Furthermore, a specific article on how information about self-motion and object motion can be decoded from a population of MSTd neurons is reviewed in more detail. The third study describes a published and annotated dataset of MST neurons’ responses to a series of different motion stimuli. This dataset is analyzed using a variety of different analysis approaches in the fifth study. Classical tuning curve approaches confirm that MST neurons have large, but well-defined spatial receptive fields and are independently tuned for linear and spiral motion, as well as speed. We also confirm that the tuning for spiral motion is position invariant in a majority of MST neurons. A bias-free characterization of receptive field profiles based on a new stimulus that generates smooth, complex motion patterns turned out to be predictive of some of the tuning properties of MST neurons, but was generally less informative than similar approaches have been in earlier visual areas. The fifth study introduces a new motion stimulus that consists of hexgonal segments and presents an optimization algorithm for an adaptive online analysis of neurophysiological recordings. Preliminary physiological data and simulations show these tools to have a strong potential in characterizing the response functions of MST neurons. The final study describes a behavioral experiment with human subjects that explores how different stimulus features, such as size and contrast, affect motion perception and discusses what conclusions can be drawn from that about the representation of visual motion in the human brain. Together these studies highlight the visual motion processing pathway of the primate brain as an excellent model system for studying more complex relations of neural activity and external stimuli. Area MST in particular emerges as a gateway between perception, cognition, and action planning.2021-11-1

The analysis of complex motion patterns in primate cortex

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 1995.Includes bibliographical references.by Bard J. Geesaman.Ph.D

Visual statistics using neural networks.

This thesis describes the application of statistical techniques to natural images as a means of gaining insight into the operation of low level vision. First, the statistical technique of principal component analysis is applied to a collection of natural images: a match with psychophysical data is found; and a solution to the dynamic range problem proposed. The problem of learning and calibrating psychological and physiological representations of space is t hen investigated. The grey level correlations in natural images are measured and their physical causes investigated. The resulting correlations are related both to psychological distortions of space and to the cortical representation of space in \T in macaque monkey. The interpretation in terms of a system calibrating itself using the correlations in the input signals is shown to produce accurate psychological and physiological predictions. Lastly the problems of creating low level models of the visual input is looked at using a framework originally proposed by Hinton and Sejnowski (1983). The way in which phase coherence of (neuronal) firing in a network can label the probability of an interpretation is demonstrated. A new search technique, inspired by the different time courses of inhibition and excitation in the cortex, is proposed for searching for the most likely visual interpretation. It is concluded that statistical techniques can provide insight into the operation of low level vision