Optometry and Vision Sciences - Theses
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ItemOrigins of feature selectivity in the visual system of two mammalian species( 2018)Visual information is transmitted from the retina to the primary visual cortex (V1) through the lateral geniculate nucleus (LGN). At each stage along this visual pathway, the receptive field properties of neurons are transformed, with sharp feature selectivity originating for the first time in the primary visual cortex. In this thesis, I studied the mechanism underlying the generation of the full range of, as well as the sharpening of, two such feature selectivities - the orientation and spatial frequency tuning of neurons- along the visual pathways of tree shrews and macaques. Undertaking this study in two species helps us examine the mechanisms that may be conserved during evolution. In experiment 1 (chapter 4), I used the differences in spatial scale between the geniculate inputs and the V1 spiking outputs in the optical imaging of intrinsic signals to examine the differences in the preferred orientations of the inputs and outputs in V1 of anaesthetized macaques. I determined that the majority of inputs were tuned to the radial orientation (the orientation of the line joining a point on the visual field to the centre of gaze, or fovea in the macaque). A bias for the radial orientation is already evident in the retina. I suggest that the full range of orientation preferences observed in the outputs are generated from a limited number of broadly tuned channels. In experiment 2 (chapter 5), I explored the mechanism underlying the sharpening of orientation tuning from layer 4 to layer 2/3 in tree shrew V1. I found that the orientation selectivity of layer 2/3 is generated from sharpening the broad biases observed in layer 4 of the cortex. It is likely that intracortical inhibitory connections play a bigger role in sharpening feature selectivity in the tree shrews (and by extension in macaques) compared to cats, where most such studies are undertaken. In experiment 3 (chapter 6), I found that neurons in the visual layers of the superior colliculus (SC), which form part of an alternate pathway to the visual cortices, and those in the LGN and layer 4 of V1 show similar orientation and spatial frequency tuning. Hence, it is likely that neurons in these two pathways inherit their feature selectivity from biases established in the retina. In experiment 4 (chapter 7), I found that unlike the macaques, simple cells in the tree shrew V1 act more as Fourier analysers; i.e., they deconstruct the visual scene into their spatial frequency components. I conclude that the tree shrew has several spatial frequency tuning channels in V1 in comparison. Together, my results suggest that sharp feature selectivity observed in the primary visual cortex may be generated from broad biases that are present sub-cortically. Further, in tree shrews and macaques, where sharpening of feature selectivity occurs from layer 4 to layer 2/3, intracortical mechanisms, such as cross-orientation inhibition also play an important role in elaborating feature selectivity. The full range of orientation and spatial frequency preferences in the cortex may be generated from a limited number of broadly tuned channels in both the macaques and tree shrews. These results indicate that sub-cortical biases play an important role in elaborating feature selectivity within the primary visual cortex.
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ItemAn investigation of spatial receptive fields of complex cells in the primary visual cortex( 2017)One of the main concerns of visual neuroscience is to understand how information is processed by the neural circuits in the visual system. Since the historic experiments of Hubel and Wiesel, many more aspects of visual information processing in the brain have been discovered using experimental approaches. However, a lot of computations underlying such processing remain unclear or even unknown. In the retina and the lateral geniculate nucleus, the basic computations have been identified by measuring the responses of neurons to simple visual stimuli such as gratings and oriented bars. However, in higher areas of the visual pathway, e.g. the cortical visual areas, many neurons (including complex cells) cannot be characterised entirely based on their responses to simple stimuli. The complex cells in the visual cortex do not exhibit linear receptive field properties. Hence, the failure of linear receptive field models to describe the behaviour of such neurons leads neuroscientists to seek more plausible quantitative models. Efficient coding is a computational hypothesis about sensory systems. Recently developed models based on the efficient coding hypothesis were able to capture certain properties of complex cells in the primary visual cortex. The Independent feature Subspace Analysis (ISA) model and the covariance model are such examples of these models. The ISA model employs the notion of the energy model in describing the responses of complex cells, whereas the covariance model is based on a recent speculation that complex cells tend to encode the second-order statistical dependencies of the visual input. In this thesis, the parametric technique of the generalised quadratic model (GQM) in conjunction with white Gaussian noise stimulation is used to identify the spatial receptive fields of complex cells in cat primary visual cortex. The validity of the identified receptive field filters are verified by measuring their performance in predicting the responses to test stimuli using correlation coefficients. The findings suggest that a majority of the complex cells in cat primary visual cortex are best described using a linear and one or more quadratic receptive field filters, which are classified as mixed complex cells. We observed that some complex cells exhibit linear as well as quadratic dependencies on an identified filter of their receptive fields. This often introduces a significant shift in the feature-contrast responses of these cells, which results in violations of the polarity invariance property of complex cells. Lastly, a quantitative comparison is performed between the experiment and theory using statistical analysis of the population of the cells' receptive fields identified by experiment and those predicted by the efficient coding models. For this, motivated by the experimental findings for complex cells, a modification of the ISA model that incorporates a linear term is introduced. The simulated model receptive fields of the modified ISA and the covariance model are then used to draw comparison to the experimental data. While the modified ISA and the covariance models are comparable in predicting the complex cell receptive fields characteristics in the primary visual cortex, the latter shows more capable in explaining the observed intra-receptive field inhomogeneity of complex cells, including differences in orientation preference and ratio spatial frequency for the receptive field filters of the same cell. However, the major discrepancies between theory and experiment lie in the orientation bandwidth and spatial frequency bandwidth of the receptive field filters, where the population of the predicted model receptive field filters demonstrate much narrower bandwidths. These findings, thereby, suggest the sub-optimality of the experimental receptive field filters in terms of the efficiency of the code.
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ItemReceptive field properties and dynamics in mammalian primary visual cortex( 2015)The functional properties and structure of receptive fields in primary visual cortical (V1) neurons represent how visual information is processed in the mammalian neocortex. Cortical receptive fields are diverse and highly dynamic to accommodate the constantly changing visual environment. The mechanisms behind the organisation of different types of receptive fields are still highly debated after David Hubel and Torsten Wiesel first described the fundamental properties of cortical receptive fields half a century ago. These pivotal discoveries were conducted in the classic animal models of vision research: cats and monkeys. In recent years, fuelled by the opportunities for genetic and molecular manipulation, mice have rapidly become a major model for studying cortical visual processing. It is essential to recognise the similarities and differences between mouse V1 and that of the well-established animal models. A major goal in this thesis is to compare the receptive field properties of mouse V1 (area 17) and cat V1 (area 17 and 18). Cortical neurons are largely composed of excitatory pyramidal cells and GABAergic inhibitory cells. Compared to excitatory neurons, the receptive field properties of inhibitory neurons are poorly understood due to the difficulty in identifying the diverse inhibitory subpopulations. In Chapter 4, by separating inhibitory and excitatory neurons based on their spike waveform shapes, I was able to examine the inhibitory receptive fields in both mouse and cat V1 and demonstrate differences in orientation selectivity and response linearity between these cell types in two species. In addition, I was also able to show that inhibitory cells were significantly over-represented in layer 1 of cat V1 and were less sensitive to low contrasts, as a population, compared to excitatory cells. Based on receptive field structures and response properties, V1 neurons are classified into simple cells and complex cells. Simple cells are thought to have spatially segregated ON and OFF subfields and are thus highly selective for the spatial phases of oriented edges. Complex cells have intermingled ON and OFF subfields and are largely phase-insensitive. Recent evidence reveals that some complex cells in cat and monkey V1 show increased phase sensitivity in their spiking activity as stimulus contrast is reduced, which suggests a shift towards a simple-like receptive field at low contrasts. By employing drifting sine-wave gratings (Chapter 5) and contrast-reversing gratings as visual stimuli (Chapter 6), I demonstrated the same effect in mouse V1 neurons. Furthermore, through intracellular recording I also observed contrast-dependent phase-sensitivity in the subthreshold membrane potentials of the cells as well as their spiking responses. This confirmed that the contrast-driven effect was a result of altered synaptic inputs and not the non-linear transformation from membrane potential to spike output.
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ItemMechanisms of top-down processing in visual perception( 2013)Visual attention allows the brain to selectively process only what is relevant from the rich visual world that surrounds us. This selection process can be biased by both bottom-up processes that are stimulus-driven and top-down influences that are goal or expectancy driven. Top-down processes of attention, in turn, can be sub-divided into two systems: a location-based system, where stimuli are selected on the basis of their location in the visual field, and a feature-based system, where selection of stimuli is based on their featural properties (e.g. colour, direction of motion), regardless of location. Since both location- and feature-based attentional systems rely on processing within two inter-connected but distinct pathways in the brain, the mechanisms underlying each are separable, leading to the widely disputed question of whether and which system dominates attentional processing. This thesis had two primary goals – the first was to determine whether the effects of location-based and feature-based attention were different. Experiments 1 and 2 explored this possibility using psychophysical techniques that incorporated a unique attention-demanding global motion-perception task. In Experiment 1, location- and feature-based attention were deployed using three types of cues - location of motion, direction of motion and colour cues. Differential effects were elicited depending on the type of cue employed. In general, location-based effects were larger than feature-based effects of colour and direction of motion. In Experiment 2, the effect of adding a highly salient distracter to the tasks was examined. It was found that the presence of the distracter affected performances significantly only when features were cued and not when locations were cued. Furthermore, the effect of the distracter when features were cued depended on the similarity between the target and the distracter. The second goal of this thesis was to highlight the importance of the primary visual cortex (V1) in the attention neuro-circuitry. This was accomplished in Experiment 3, using a combination of functional imaging and psychophysical techniques. It was hypothesized that the size of V1 could determine the individual attention capacity in a visual search task. Consistent with this expectation, it was found that people with larger V1s tended to perform faster searches and hence had larger attention magnitudes. It was further hypothesized that the size of V1 could predict individual reading speed. Although this relationship was not elicited, a strong positive correlation was found between attention and reading speed, consistent with what was previously reported in the literature. The results from this study provide support for a location-based model of attention. They also provide insights into the effect of attentional capture by a distracter during focused attention conditions. This helps us appreciate the various constraints of attentional processing within the brain. Finally, the results from Experiment 3 are perhaps the first demonstration of a morphological link between the brain and a cognitive ability like visual attention. Together, the findings from this study set the stage for further research into the mechanisms and structural morphology underlying attention.
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ItemFunctional correlates between the rat electroretinogram and visual evoked potential( 2012)The ERG and VEP are sequentially-activated responses, widely used for diagnosis of eye and brain diseases. Measuring both simultaneously provides additional information to help localise where in the visual pathway injury has occurred. This thesis shows how retinal information streams are encoded in the VEP. In addition, it shows that changes to ERG components can predict the amount of loss downstream in the retina. However, retinal loss may not predict VEP changes.