Sparse coding is an influential unsupervised learning approach proposed as a theoretical model of the encoding process in the primary visual cortex (V1). While sparse coding has been successful in explaining classical receptive field properties of simple cells, it was unclear whether it can account for more complex response properties in a variety of cell types. In this dissertation, we demonstrate that sparse coding and its variants are consistent with key aspects of neural response in V1, including many contextual and nonlinear effects, a number of inhibitory interneuron properties, as well as the variance and correlation distributions in the population response. The results suggest that important response properties in V1 can be interpreted as emergent effects of a neural population efficiently representing the statistical structures of natural scenes under resource constraints. Based on the models, we make predictions of the circuit structure and response properties in V1 that can be verified by future experiments.

The cerebral cortex of mammals comprises a series of topographic maps, forming sensory and motor areas such as those in the visual, auditory, and somatosensory systems. Understanding the rules that govern the development of these maps and how this topographic organization relates to information processing is critical for the understanding of cortical processing and whole brain function. Previous computational models have shown that topographic maps can develop through a process of self-organization, if spatially localized patches of cortical neurons are activated by particular stimuli. This thesis presents a series of computational models, based on this principle of self-organization, that focus on the development of the map of orientation preference in primary visual cortex (V1). This map is the prototypical example of topographic map development in the brain, and is the most widely studied, however the same self-organizing principles can also apply to maps of many other visual features and maps in many other sensory areas. Experimental evidence indicates that orientation preference maps in V1 develop in a stable way, with the initial layout determined before eye opening. This constraint is at odds with previous self-organizing models, which have used biologically unfounded ad-hoc methods to obtain robust and reliable development. Such mechanisms inherently lead to instability, by causing massive reorganization over time. The first model presented in this thesis (ALISSOM) shows how ad-hoc methods can be replaced with biologically realistic homeostatic mechanisms that lead to development that is both robust and stable. This model shows for the first time how orientation maps can remain stable despite the massive circuit reconstruction and change in visual inputs occurring during development. This model also highlights the requirements for homeostasis in the developing visual circuit. A second model shows how this development can occur using circuitry that is consistent with the known wiring in V1, unlike previous models. This new model, LESI, contains Long-range Excitatory and Short-range Inhibitory connections between model neurons. Instead of direct long-range inhibition, it uses di-synaptic inhibition to ensure that when visual stimuli are at high contrast, long-range excitatory connections have an overall inhibitory influence. The results match previous models in the special case of the high-contrast inputs that drive development most strongly, but show how the behavior relates to the underlying circuitry, and also make it possible to explore effects at a wide range of contrasts. The final part of this thesis explores the differences between rodents and higher mammals that lead to the lack of topographic organization in rodent species. A lack of organization for orientation also implies local disorder in retinotopy, and analysis of retinotopy data from two-photon calcium imaging in mouse (provided by Tom Mrsic- Flogel, University College London) confirms this hypothesis. A self-organizing model is used to investigate how this disorder can arise via variation in either feed-forward connections to V1 or lateral connections within V1, and how the effects of disorder may vary between species. These results suggest that species with and without topographic maps implement similar visual algorithms differing only in the values of some key parameters, rather than having fundamental differences in architecture. Together, these results help us understand how and why neurons develop preferences for visual features such as orientation, and how maps of these neurons are formed. The resulting models represent a synthesis of a large body of experimental evidence about V1 anatomy and function, and offer a platform for developing a more complete explanation of cortical function in future work.

No description available.

The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Title from PDF of title page (University of Missouri--Columbia, viewed on September 15, 2009). Thesis advisor: Satish S. Nair. Includes bibliographical references.

No description available.

No description available.

No description available.

We examine the nature of causality as it exists within large-scale brain networks by first providing a rigorous conceptual analysis of probabilistic causality as distinct from deterministic causality. We then use information-theoretic methods, including the linear autoregressive modeling technique of Wiener-Granger causality (WGC), and Shannonian transfer entropy (TE), to explore and recover causal relations between two neural masses. Time series data were generated by Stefanescu-Jirsa 3D model of two coupled network nodes in The Virtual Brain (TVB), a novel neuroinformatics platform used to model resting state large-scale networks with neural mass models. We then extended this analysis to three nodes to investigate the equivalence of a concept in probabilistic causality known as ‘screening off’ with a method of statistical ablation known as conditional Granger causality. Finally, we review some of the empirical and theoretical work of nonlinear neurodynamics of Walter Freeman, as well as metastable coordination dynamics and investigate what impact they have had on consciousness research. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2018. / FAU Electronic Theses and Dissertations Collection

This thesis describes an investigation of retinal directional selectivity. We show intracellular (whole-cell patch) recordings in turtle retina which indicate that this computation occurs prior to the ganglion cell, and we describe a pre-ganglionic circuit model to account for this and other findings which places the non-linear spatio-temporal filter at individual, oriented amacrine cell dendrites. The key non-linearity is provided by interactions between excitatory and inhibitory synaptic inputs onto the dendrites, and their distal tips provide directionally selective excitatory outputs onto ganglion cells. Detailed simulations of putative cells support this model, given reasonable parameter constraints. The performance of the model also suggests that this computational substructure may be relevant within the dendritic trees of CNS neurons in general.

The work of this thesis concerns how cortical memories are stored and retrieved. In particular, large-scale simulations are used to investigate the extent to which associative attractor theory is compliant with known physiology and in vivo dynamics. The first question we ask is whether dynamical attractors can be stored in a network with realistic connectivity and activity levels. Using estimates of biological connectivity we demonstrated that attractor memories can be stored and retrieved in biologically realistic networks, operating on psychophysical timescales and displaying firing rate patterns similar to in vivo layer 2/3 cells. This was achieved in the presence of additional complexity such as synaptic depression and cellular adaptation. Fast transitions into attractor memory states were related to the self-balancing inhibitory and excitatory currents in the network. In order to obtain realistic firing rates in the network, strong feedback inhibition was used, dynamically maintaining balance for a wide range of excitation levels. The balanced currents also led to high spike train variability commonly observed in vivo. The feedback inhibition in addition resulted in emergent gamma oscillations associated with attractor retrieval. This is congruent with the view of gamma as accompanying active cortical processing. While dynamics during retrieval of attractor memories did not depend on the size of the simulated network, above a certain size the model displayed the presence of an emergent attractor state, not coding for any memory but active as a default state of the network. This default state was accompanied by oscillations in the alpha frequency band. Such alpha oscillations are correlated with idling and cortical inhibition in vivo and have similar functional correlates in the model. Both inhibitory and excitatory, as well as phase effects of ongoing alpha observed in vivo was reproduced in the model in a simulated threshold-stimulus detection task. / <p>At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper8: In press.</p>