Recurrent Connectivity Can Account for the Dynamics of Disparity Processing in V1

Disparity tuning measured in the primary visual cortex (V1) is described well by the disparity energy model, but not all aspects of disparity tuning are fully explained by the model. Such deviations from the disparity energy model provide us with insight into how network interactions may play a role in disparity processing and help to solve the stereo correspondence problem. Here, we propose a neuronal circuit model with recurrent connections that provides a simple account of the observed deviations. The model is based on recurrent connections inferred from neurophysiological observations on spike timing correlations, and is in good accord with existing data on disparitytuning dynamics.Wefurther performedtwo additional experimentstotest predictions ofthe model. First, we increased the size of stimuli to drive more neurons and provide a stronger recurrent input. Our model predicted sharper disparity tuning for larger stimuli. Second, we displayed anticorrelated stereograms, where dots of opposite luminance polarity are matched between the left- and right-eye images and result in inverted disparity tuning in the disparity energy model. In this case, our model predicted reduced sharpening and strength of inverted disparity tuning. For both experiments, the dynamics of disparity tuning observed from the neurophysiological recordings in macaque V1 matched model simulation predictions. Overall, the results of this study support the notion that, whilethe disparity energy model provides a primary account of disparitytuning in V1 neurons, neural disparity processing in V1 neurons is refined by recurrent interactions among elements in the neural circuit

Processing of local features in the zebrafish optic tectum

The optic tectum is the main visual processing area in zebrafish and is involved in a variety of visually-driven behaviours. A key question is how information about the visual environment is processed and integrated in order to generate guided behaviour. The aim of this study was to explore the response properties of tectal neurons, i.e., their preference for certain features of the visual input. To do this, I developed a custom set-up for calcium imaging and simultaneous visual stimulation in older zebrafish larvae, up to the age of 21 dpf. First, this set-up was employed to measure the spatial receptive fields of tectal neurons with small moving spots. Notably, the results suggested that receptive field development is not completed by 9 dpf as previously believed; instead, receptive field refinement continues beyond this age. The results also confirmed that receptive fields in the optic tectum were relatively large in older larvae. Based on this, I formulated the hypothesis that tectal neurons might process multiple local features simultaneously. To test how the optic tectum encodes local features, I used small, moving oriented bars and combinations of bars, i.e., angles. Tectal responses to these stimuli suggested that, not only does the optic tectum encode local features, but is also tuned to horizontal-oriented local stimuli. Finally, I used a set of moving stimuli, consisting of simple features (i.e., lines and angles) and a composite feature (i.e. square) to test how information about multiple local features was integrated by tectal neurons. The results indicated that local features are spatially integrated in a sublinear fashion. The outcomes of the work presented in this thesis add to our understanding of how visual information provided by the retina is processed within the optic tectum

Visualization and analysis of diffusion tensor fields

technical reportThe power of medical imaging modalities to measure and characterize biological tissue is amplified by visualization and analysis methods that help researchers to see and understand the structures within their data. Diffusion tensor magnetic resonance imaging can measure microstructural properties of biological tissue, such as the coherent linear organization of white matter of the central nervous system, or the fibrous texture of muscle tissue. This dissertation describes new methods for visualizing and analyzing the salient structure of diffusion tensor datasets. Glyphs from superquadric surfaces and textures from reactiondiffusion systems facilitate inspection of data properties and trends. Fiber tractography based on vector-tensor multiplication allows major white matter pathways to be visualized. The generalization of direct volume rendering to tensor data allows large-scale structures to be shaded and rendered. Finally, a mathematical framework for analyzing the derivatives of tensor values, in terms of shape and orientation change, enables analytical shading in volume renderings, and a method of feature detection important for feature-preserving filtering of tensor fields. Together, the combination of methods enhances the ability of diffusion tensor imaging to provide insight into the local and global structure of biological tissue

Architecture of the Mouse Brain Synaptome

Synapses are found in vast numbers in the brain and contain complex proteomes. We developed genetic labeling and imaging methods to examine synaptic proteins in individual excitatory synapses across all regions of the mouse brain. Synapse catalogs were generated from the molecular and morphological features of a billion synapses. Each synapse subtype showed a unique anatomical distribution, and each brain region showed a distinct signature of synapse subtypes. Whole-brain synaptome cartography revealed spatial architecture from dendritic to global systems levels and previously unknown anatomical features. Synaptome mapping of circuits showed correspondence between synapse diversity and structural and functional connectomes. Behaviorally relevant patterns of neuronal activity trigger spatiotemporal postsynaptic responses sensitive to the structure of synaptome maps. Areas controlling higher cognitive function contain the greatest synapse diversity, and mutations causing cognitive disorders reorganized synaptome maps. Synaptome technology and resources have wide-ranging application in studies of the normal and diseased brain

A Computational Model of Auditory Feature Extraction and Sound Classification

This thesis introduces a computer model that incorporates responses similar to those found in the cochlea, in sub-corticai auditory processing, and in auditory cortex. The principle aim of this work is to show that this can form the basis for a biologically plausible mechanism of auditory stimulus classification. We will show that this classification is robust to stimulus variation and time compression. In addition, the response of the system is shown to support multiple, concurrent, behaviourally relevant classifications of natural stimuli (speech). The model incorporates transient enhancement, an ensemble of spectro - temporal filters, and a simple measure analogous to the idea of visual salience to produce a quasi-static description of the stimulus suitable either for classification with an analogue artificial neural network or, using appropriate rate coding, a classifier based on artificial spiking neurons. We also show that the spectotemporal ensemble can be derived from a limited class of 'formative' stimuli, consistent with a developmental interpretation of ensemble formation. In addition, ensembles chosen on information theoretic grounds consist of filters with relatively simple geometries, which is consistent with reports of responses in mammalian thalamus and auditory cortex. A powerful feature of this approach is that the ensemble response, from which salient auditory events are identified, amounts to stimulus-ensemble driven method of segmentation which respects the envelope of the stimulus, and leads to a quasi-static representation of auditory events which is suitable for spike rate coding. We also present evidence that the encoded auditory events may form the basis of a representation-of-similarity, or second order isomorphism, which implies a representational space that respects similarity relationships between stimuli including novel stimuli

Visual Quality Assessment and Blur Detection Based on the Transform of Gradient Magnitudes

abstract: Digital imaging and image processing technologies have revolutionized the way in which we capture, store, receive, view, utilize, and share images. In image-based applications, through different processing stages (e.g., acquisition, compression, and transmission), images are subjected to different types of distortions which degrade their visual quality. Image Quality Assessment (IQA) attempts to use computational models to automatically evaluate and estimate the image quality in accordance with subjective evaluations. Moreover, with the fast development of computer vision techniques, it is important in practice to extract and understand the information contained in blurred images or regions. The work in this dissertation focuses on reduced-reference visual quality assessment of images and textures, as well as perceptual-based spatially-varying blur detection. A training-free low-cost Reduced-Reference IQA (RRIQA) method is proposed. The proposed method requires a very small number of reduced-reference (RR) features. Extensive experiments performed on different benchmark databases demonstrate that the proposed RRIQA method, delivers highly competitive performance as compared with the state-of-the-art RRIQA models for both natural and texture images. In the context of texture, the effect of texture granularity on the quality of synthesized textures is studied. Moreover, two RR objective visual quality assessment methods that quantify the perceived quality of synthesized textures are proposed. Performance evaluations on two synthesized texture databases demonstrate that the proposed RR metrics outperforms full-reference (FR), no-reference (NR), and RR state-of-the-art quality metrics in predicting the perceived visual quality of the synthesized textures. Last but not least, an effective approach to address the spatially-varying blur detection problem from a single image without requiring any knowledge about the blur type, level, or camera settings is proposed. The evaluations of the proposed approach on a diverse sets of blurry images with different blur types, levels, and content demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods qualitatively and quantitatively.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Neural mechanisms of hippocampal place cell sequences

The hippocampus is important for learning and memory. Specifically, a type of pyramidal neuron in the rodent hippocampus, namely the “place cell”, encodes the current location of the animal in space. Sequential activations of hippocampal place cells occurs when the animal is under active exploration (theta sequence), and during the animal’s quiet immobility or sleep (hippocampal replay). These hippocampal place cell sequences have been proposed as fundamental neural substrates of learning and memory, because they carry precise temporal coordination between individual neurons. However, the mechanisms contributing to their development with experience have not been well understood. To address this gap in knowledge, we applied high-density electrophysiological recording techniques to simultaneously monitor the activities of hundreds of hippocampal place cells, and compared neuronal activities before and after, or between early and late experiences, while the animal explored a novel environment. I found that both theta sequences and hippocampal replay emerged immediately after the first experience, and were absent prior to that. In contrast, the activities of individual place cells advanced to earlier phases of hippocampal theta rhythm (the phenomenon of “phase precession”) independently with experience, and only became synchronized and formed temporally coordinated sequential activities after the first running experience. Further, the hippocampal replays, once developed, were continuously modulated by individual experiences, where the propagation speed of their represented spatial trajectories slowed down with learning. Examination of hippocampal replays in a fine temporal scale revealed that their spatial representations were discretized in alternating stages of static representation and rapid movement, and phase-locked to the slow-gamma rhythm. The change of the propagation speed of replay was reflected by an increased number of discrete alternating stages in the represented trajectory, accompanied by an increase in duration of the static representations, and a decrease of the duration of the rapid movements. In conclusion, hippocampal place cell sequences undergo rapid plasticity with experience, which involves synchronization between individual neurons, and a dynamic change in the content of spatial information represented by the place cell sequences

Automated Quantification of Human Alpha Rhythm

This thesis seeks to quantify human alpha rhythm in order to obtain better measures to test theoretical models of neurodynamics, with potential clinical applications for the method of identification. An automated algorithm is developed in Chapter 2 to facilitate collection of objective data from an expanded alpha band (4–14 Hz) in a large number of subjects. This method avoids subjective bias inherent to traditional visual identification of the alpha activity, produced multiple peak information (if multiple peaks exist) that is absent in qEEG measures, and uses information from multiple electrode sites to eliminate spurious peaks. This method is tested and validated on 100 subjects. In addition to traditional measures of alpha activities such as the frequency and amplitude, further measures were devised to better quantify the alpha rhythm and its spatial characteristics. Background spectra in the alpha range are also characterized. In Chapter 3 the algorithm is applied to a large (1498 subjects) database of normal healthy subjects of approximately equal number in each sex, as well as a large span in age (6–86 years), in order to establish typical parameter ranges. Analysis is done comparing the age and the topological trends that are known variants in the alpha rhythm. Investigations are also performed to test for potential sex differences and/or lateralities. Key results are that double alpha peaks are resolved in a large proportion of the subjects ( 50%), while single alpha peak cases are likely to be double-peak cases in which the two peaks are not resolved. Age trends in measures of alpha activity show increase of alpha frequency from childhood to adolescence, a plateau in adulthood, and a slight decline in old age; a decrease in alpha amplitude in old age is also observed. These findings are consistent with previous literature and provide better statistics. Topological distribution of the alpha activity on the head is also explored, with little lateral asymmetry observed. No statistically significant differences between the sexes are found. The C++ code that was developed and utilized in this thesis is included in Appendix B. It is provided on disk and is available online. A study carried out on the same group of subjects to determine age-related trends of EEG parameters produced by model fitting is included in Appendixes C, to provide a comparison between the methods and highlights corresponding results