No description available.

The mammalian visual cortex is believed to be parcellated into functional radial units called modules, which are composed of neurons with similar physiological properties. The first demonstration of modularity was provided in 1957 by Mountcastle in the somatosensory cortex, and has since been demonstrated in the visual cortex, where neurons within a vertical unit of the visual cortex process information about the same portion of the visual field. / A new approach has been proposed for identifying functionally similar neurons by examining their molecular characteristics. Indeed, the arrangement of neurons into functional arrays might be reflected by the presence of specific molecules (e.g Cat-301 patches, cytochrome oxidase blobs). / In this experiment, immunohistochemistry was used to examine the tangential and radial distribution and development of a subset of pyramidal neurons in the kitten and adult cat visual cortex using the monoclonal antibody SMI-32, that recognizes the non-phosphorylated form of neurofilament H. It was found that the neurons recognized by this antibody were grouped into clusters, forming regularly spaced patches in the infragranular and supragranular layers. These anatomical findings support the notion of an intrinsic columnar organization.

Thesis (Ph. D. in Neuroscience)--Vanderbilt University, May 2008. / Title from title screen. Includes bibliographical references.

Thesis (Ph. D.)--University of California, San Diego, 1997. / Vita. Includes bibliographical references (leaves 103-112).

No description available.

No description available.

Over the past several decades, experimental studies of the organisation of spatial frequency (SF) preference in mammalian visual cortex (V1) have reported a wide variety of conflicting results. A consensus now appears to be emerging that in the superficial layers SF is mapped continuously across the cortical surface. However, other evidence suggests that SF may differ systematically with cortical depth, at least in layer 4, where the magnocellular (M) and parvocellular (P) pathway afferents terminate in different sublaminae. It is not yet clear whether the topographic organisation for SF observed in the superficial layers is maintained throughout the input layers as well, or whether there is a switch from a laminar to a topographic organisation along the vertical dimension in V1. I present results from two alternative self-organising computational models of V1 that receive natural image inputs through multiple SF channels in the LGN, differing in whether they develop laminar or topographic organisation in layer 4. Both models lead to topographic organisation for orientation (OR) and SF preference in upper layers, consistent with current experimental evidence. The results suggest that in either case separate sub-populations of neurons are required to obtain a wide range of SF preference from Hebbian learning of natural images. These models show that a laminar organisation for SF preference can coexist with a topographic, columnar organisation for orientation, and that the columnar organisation for orientation is dependent upon inter-laminar feedback. These results help clarify and explain the wide range of SF results reported in previous studies.

Humans’ perceptual experience of color is very different from what one might expect, given the light reaching the eye. Identical patterns of light are often perceived as different colors, and different patterns of light are often perceived as the same color. Even more strikingly, our perceptual experience is that hues are arranged circularly (with red similar to violet), even though single-wavelength lights giving rise to perceptions of red and violet are at opposite ends of the wavelength spectrum. The goal of this thesis is to understand how perceptual color space arises in the brain, focusing on the arrangement of hue. To do this, we use computational modeling to integrate findings about light, physiology of the visual system, and color representation in the brain. Recent experimental work shows that alongside spatially contiguous orientation preference maps, macaque primary visual cortex (V1) represents color in isolated patches, and within those patches hue appears to be spatially organized according to perceptual color space. We construct a model of the early visual system that develops based on natural input, and we demonstrate that several factors interact to prevent this first model from developing a realistic representation of hue. We show these factors as independent dimensions and relate them to problems the brain must be overcoming in building a representation of perceptual color space: physiological and environmental variabilities to which the brain is relatively insensitive (surprisingly, given the importance of input in driving development). We subsequently show that a model with a certain position on each dimension develops a hue representation matching the range and spatial organization found in macaque V1—the first time a model has done so. We also show that the realistic results are part of a spectrum of possible results, indicating other organizations of color and orientation that could be found in animals, depending on physiological and environmental factors. Finally, by analyzing how the models work, we hypothesize that well-accepted biological mechanisms such as adaptation, typically omitted from models of both luminance and color processing, can allow the models to overcome these variabilities, as the brain does. These results help understand how V1 can develop a stable, consistent representation of color despite variabilities in the underlying physiology and input statistics. This in turn suggests how the brain can build useful, stable representations in general based on visual experience, despite irrelevant variabilities in input and physiology. The resulting models form a platform to investigate various adult color visual phenomena, as well as to predict results of rearing experiments.

The response of a neuron encoding information about a sensory stimulus is influenced by the context in which that information is presented. In the primary visual cortex (area V1), neurons respond selectively to stimuli presented to a relatively constrained region of visual space known as the classical receptive field (CRF). These responses are influenced by stimuli in a much larger region of visual space known as the extra-classical receptive field (eCRF). In that they cannot directly evoke a response from the neuron, surround stimuli in the eCRF provide the context for the input to the CRF. Though the past few decades of research have revealed many details of the complex and nuanced interactions between the CRF and eCRF, the circuit mechanisms underlying these interactions are still unknown. In this thesis, we present a simple, novel cortical circuit model that can account for a surprisingly diverse array of eCRF properties. This model relies on extensive recurrent interactions between excitatory and inhibitory neurons, connectivity that is strongest between neurons with similar stimu- lus preferences, and an expansive input-output neuronal nonlinearity. There is substantial evidence for all of these features in V1. Through analytical and computational modeling techniques, we demonstrate how and why this circuit is able to account for such a comprehensive array of contextual modulations. In a linear network model, we demonstrate how surround suppression of both excitatory and inhibitory neurons is achieved through the selective amplification of spatially-periodic pat- terns of activity. This amplification relies on the network operating as an inhibition-stabilized network, a dynamic regime previously shown to account for the paradoxical decrease in in- hibition during surround suppression (Ozeki et al., 2009). With the addition of nonlinearity, effective connectivity strength scales with firing rate, and the network can transition be- tween different dynamic regimes as a function of input strength. By moving into and out of the inhibition-stabilized state, the model can reproduce a number of contrast-dependent changes in the eCRF without requiring any asymmetry in the intrinsic contrast-response properties of the cells. This same model also provides a biologically plausible mechanism for cortical normalization, an operation that has been shown to be ubiquitous in V1. Through a winner-take-all population response, we demonstrate how this network undergoes a strong reduction in trial-to-trial variability at stimulus onset. We also propose a novel mechanism for attentional modulation in visual cortex. We then go on to test several of the critical pre- dictions of the model using single unit electrophysiology. From these experiments, we find ample evidence for the spatially-periodic patterns of activity predicted by the model. Lastly, we show how this same circuit motif may underlie behavior in a higher cortical region, the lateral intraparietal area.

The physical architecture of neural circuits is thought to underlie the computations that give rise to higher order feature sensitivity in the neocortex. Recent technological breakthroughs have allowed the structural and functional investigation of the basic computational units of neural circuits; individual synaptic connections. However, it remains unclear how cortical neurons sample and integrate the thousands of synaptic inputs, supplied by different brain structures, to achieve feature selectivity. Here, I first describe how visual cortical circuits transform the elementary inputs supplied by the periphery into highly diverse, but well-organized, feature representations. By combining and optimizing newly developed techniques to map the functional synaptic connections with defined sources of inputs, I show that the intersection between columnar architecture and dendritic sampling strategies can lead to the selectivity properties of individual neurons: First, in the canonical feedforward circuit, the basal dendrites of a pyramidal neuron utilize unique strategies to sample ON (light increment) and OFF (light decrement) inputs in orientation columns to create the distinctive receptive field structure that is responsible for basic sensitivity to visual spatial location, orientation, spatial frequency, and phase. Second, for long-range horizontal connections, apical dendrites unbiasedly integrate functionally specialized and spatially targeted inputs in different orientation columns, which generates specific axial surround modulation of the receptive field. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2019. / FAU Electronic Theses and Dissertations Collection