Potential differences in developmental trajectory were investigated in autism at both the macro- and micro-scopic scale, using regional volumetric measurements from in-vivo scans and measurements of minicolumnar organisation of the cortex in post-mortem tissue. In addition, a study was carried out to investigate the sensitivity of measures of cortical diffusion to cortical architecture. Three key regions of interest were studied throughout this thesis, orbital frontal cortex (BA11), primary auditory cortex (BA41) and part of the inferior parietal lobe (BA40). Subjects with ASD showed increases in grey matter in left parietal cortex and decreases in left BA11 compared to controls. In addition, subjects with ASD showed increased grey matter volume with age in both BA41 and the inferior parietal lobe, whereas controls only showed a negative correlation between grey matter volume in BA41 and age. Wider minicolumns were found in ASD in all regions, suggesting pathology is not restricted to higher order association areas. Differences seemed more pronounced at younger ages suggesting an altered developmental trajectory in ASD. Such an increase in minicolumnar width arguably underlies the feature-based processing style seen in ASD. A pilot study using post-mortem DTI scans of MS brains revealed a relationship between measures of the directionality of diffusion and the width of axonal bundles in the cortex, an aspect of the minicolumnar arrangement. When extending this investigation to a set of ASD and control brains, evidence was found for different relationships between axon bundle width and measures of the directionality of diffusion in the cortex, suggesting that although differences in axon bundle width were not seen between groups, there may be differences in the composition of the axon bundles between ASD and control groups.

Its known that the age-related decline in cognitive facilities is not due to the loss of neurons but more subtle changes in specific areas of the brain. Structural and morphological changes in cellular alignment in the minicolumns correlate with increased prevalence of neurological diseases and in aging. In the rhesus monkey, cognitive decline is similar to what humans experience in aging. In the monkey prefrontal cortex, Brodmann area 46, an important region for executive functioning, cognitive decline correlates with changes in cellular alignment or “columnar strength” as studies by Cruz et al., (2009). Using the density maps method in Area 46, the ventral bank was identified to be the most susceptible to structural changes. Minicolumns, are defined by the cellular alignment of neurons in the cortex and some believe that the dendritic bundles of neurons in the cortex is also considered an integral part of the columns. The functional role of apical dendrites, is not well understood, however, given the that repeated organized bundles transverse through the laminae could be further support for their inclusion in minicolumns with possible functional importance. If structural changes such as loss of columnar strength (neuronal displacement) that correlates with cognitive aging, it is possible that the dendritic organization may also be affected in this area. In this thesis, it is hypothesized that the dendritic bundles in this area could also be related to the cognitive deficits associated with normal aging. Using double- fluorescence labeling for dendrites (MAP-2) and neurons (Neu-N), 3D confocal reconstructions of the dorsal and ventral banks of Area 46 were used to investigate structural/morphological changes in the dendritic bundles in young and old rhesus monkeys. While cortical thickness and apical dendritic length between both banks did not change, we found a significant increase in inter-bundle spacing at layer 6A in the older monkeys in the ventral bank. Inter-bundle spacing for bundles in layer 5 was measured and showed that the young consistently have smaller inter-bundle spacing. Future studies with larger sample size will also investigate whether changes in dendritic bundles and their organization also correlate with age-related cognitive deficits.

This thesis presents an abstract model of the mammalian neocortex. The model was constructed by taking a top-down view on the cortex, where it is assumed that cortex to a first approximation works as a system with attractor dynamics. The model deals with the processing of static inputs from the perspectives of biological mapping, algorithmic, and physical implementation, but it does not consider the temporal aspects of these inputs. The purpose of the model is twofold: Firstly, it is an abstract model of the cortex and as such it can be used to evaluate hypotheses about cortical function and structure. Secondly, it forms the basis of a general information processing system that may be implemented in computers. The characteristics of this model are studied both analytically and by simulation experiments, and we also discuss its parallel implementation on cluster computers as well as in digital hardware. The basic design of the model is based on a thorough literature study of the mammalian cortex’s anatomy and physiology. We review both the layered and columnar structure of cortex and also the long- and short-range connectivity between neurons. Characteristics of cortex that defines its computational complexity such as the time-scales of cellular processes that transport ions in and out of neurons and give rise to electric signals are also investigated. In particular we study the size of cortex in terms of neuron and synapse numbers in five mammals; mouse, rat, cat, macaque, and human. The cortical model is implemented with a connectionist type of network where the functional units correspond to cortical minicolumns and these are in turn grouped into hypercolumn modules. The learning-rules used in the model are local in space and time, which make them biologically plausible and also allows for efficient parallel implementation. We study the implemented model both as a single- and multi-layered network. Instances of the model with sizes up to that of a rat-cortex equivalent are implemented and run on cluster computers in 23% of real time. We demonstrate on tasks involving image-data that the cortical model can be used for meaningful computations such as noise reduction, pattern completion, prototype extraction, hierarchical clustering, classification, and content addressable memory, and we show that also the largest cortex equivalent instances of the model can perform these types of computations. Important characteristics of the model are that it is insensitive to limited errors in the computational hardware and noise in the input data. Furthermore, it can learn from examples and is self-organizing to some extent. The proposed model contributes to the quest of understanding the cortex and it is also a first step towards a brain-inspired computing system that can be implemented in the molecular scale computers of tomorrow. The main contributions of this thesis are: (i) A review of the size, modularization, and computational structure of the mammalian neocortex. (ii) An abstract generic connectionist network model of the mammalian cortex. (iii) A framework for a brain-inspired self-organizing information processing system. (iv) Theoretical work on the properties of the model when used as an autoassociative memory. (v) Theoretical insights on the anatomy and physiology of the cortex. (vi) Efficient implementation techniques and simulations of cortical sized instances. (vii) A fixed-point arithmetic implementation of the model that can be used in digital hardware. / QC 20100903