A thesis submitted in fulfillment of the requirements for the degree of Doctor of Philosophy / African elephants are one of the iconic mammalian species of the continent, and are the largest terrestrial mammals on the planet. While being a well-known species, with intensive behavioural studies having been undertaken, studies of the elephant brain are limited. Given that elephants do show a unique and interesting set of behaviours, including infrasonic communication, unique control of the trunk, and that they eat around 500 kg of low quality plant matter each day, the current study aimed to investigate the neural underpinnings of these and many other behaviours exhibited by elephants. While not all aspects of elephant neuroanatomy are covered in the current set of studies, the results have provided a great deal of data for regions of the brain that have not been examined for almost 50 years, and applied modern neuroanatomical methods to this task. This thesis outlines how to obtain elephant brains amenable to modern neuroanatomical study, demonstrates that the ventricles are of a size predictable for a mammal with a 5 kg brain, and that the cerebellum is relatively the largest mammalian cerebellum studied to date. A microscopic examination of the cerebellar cortex revealed that the elephants have a greater amount of a potentially more complexly organized cerebellar cortex. In addition, an architectonic study of the diencephalon and brainstem revealed that elephants, while having a mostly standard mammalian diencephalon and brainstem, do show unique features that correlate to control of specialized behaviours. In summary, the current study shows that the system for motor timing, infrasound production and reception, and the systems for satiety and wakefulness are specialized in the elephant, all of which correlate to the overt behaviours previously studied. In addition, the current studies indicate potential paths to follow for the study of behaviour in these species that will hopefully lead to a better understanding of these animals. There is still much to explore and learn about the elephant brain and it is hoped this thesis creates a platform that provides the impetus for many future studies

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This thesis is concerned with the computational modelling and simulation of physiological structures and cognitive functions of brains through the use of artificial neural nets. While the structures of these models are loosely related to neurons and physiological structures observed in brains, the extent to which we can accept claims about how neurons and brains really function based on such models depends largely on judgments about the fitness of (virtual) computer experiments as empirical evidence. The thesis examines the computational foundations of neural models, neural nets, and some computational models of higher cognitive functions in terms of their ability to provide empirical support for theories within the framework of Parallel Distributed Processing (PDP). Models of higher cognitive functions in this framework are often presented in forms that hybridise top-down (e.g. employing terminology from Psychology or Linguistics) and bottom-up (neurons and neural circuits) approaches to cognition. In this thesis I argue that the use of terminology from either approach can blind us to the highly theory-laden nature of the models, and that this tends to produce overly optimistic evaluations of the empirical value of computer experiments on these models. I argue, further, that some classes of computational models and simulations based on methodologies that hybridise top-down and bottom-up approaches are ill-designed. Consequently, many of the theoretical claims based on these models cannot be supported by experiments with such models. As a result, I question the effectiveness of computer experiments with artificial neural nets as an empirical technique for cognitive modelling.

This thesis is concerned with the computational modelling and simulation of physiological structures and cognitive functions of brains through the use of artificial neural nets. While the structures of these models are loosely related to neurons and physiological structures observed in brains, the extent to which we can accept claims about how neurons and brains really function based on such models depends largely on judgments about the fitness of (virtual) computer experiments as empirical evidence. The thesis examines the computational foundations of neural models, neural nets, and some computational models of higher cognitive functions in terms of their ability to provide empirical support for theories within the framework of Parallel Distributed Processing (PDP). Models of higher cognitive functions in this framework are often presented in forms that hybridise top-down (e.g. employing terminology from Psychology or Linguistics) and bottom-up (neurons and neural circuits) approaches to cognition. In this thesis I argue that the use of terminology from either approach can blind us to the highly theory-laden nature of the models, and that this tends to produce overly optimistic evaluations of the empirical value of computer experiments on these models. I argue, further, that some classes of computational models and simulations based on methodologies that hybridise top-down and bottom-up approaches are ill-designed. Consequently, many of the theoretical claims based on these models cannot be supported by experiments with such models. As a result, I question the effectiveness of computer experiments with artificial neural nets as an empirical technique for cognitive modelling.

There has been a significant increase in the amount of connectomics data available at the level of single neurons and single synapses in the last few years. This increase enabled investigations into the structure and function of neural circuits in much greater detail than ever before. Thus, the next step in our quest to understand the brain's functional logic is the development of tools and methods to enable us to extract data from and model these new connectomics datasets, and their use to start to examine the brain computationally. Specifically, for Drosophila melanogaster, the fruit fly, a large amount of data on the connectome have become available in the last few years. In this dissertation, we start by introducing the tools we have built to extract information from the Drosophila connectome and to create spiking models of neuropils using this information to model sensory processing and associative learning circuits at single-synapse scale. We then use the toolkit we have introduced to explore sensory processing and associative learning in the brain. First, we introduce FlyBrainLab, an interactive computing environment designed to accelerate the discovery of functional logic of the Drosophila brain. Then, we propose a programmable ontology that expands the scope of the current Drosophila brain anatomy ontologies to encompass the functional logic of the fly brain, providing a language not only for modeling circuit motifs but also for programmatically exploring their functional logic; we introduce the FeedbackCircuits library for exploring the functional logic of the massive number of feedback loops (motifs) in the fruit fly brain, and NeuroNLP++, an application that supports free-form English queries for constructing functional brain circuits fully anchored on the available connectome/synaptome datasets. Thirdly, following up on the second, we explore the construction of antennal lobe circuits using models of glomeruli. We explore the composability of the connectivity of glomeruli with local neuron feedback loops, and quantitatively characterize the I/O of the AL as a function of feedback loop motifs in the one-glomerulus, two-glomerulus and 23-glomerulus scenarios. Lastly, in the final chapter, we consider the modeling of the mushroom body, a second order olfactory neuropil and a center of associative learning, to demonstrate how the architecture of the circuit interacts with the circuit mechanisms by which sensory inputs are represented and memories are updated. Thus, in this dissertation we introduce an approach for the analysis and modeling of neural circuits based on connectomics data, and apply this approach to neural circuits spanning multiple neuropils to extract and analyze the principles of computation in the brain. The methodology described here is designed to be applied to different sensory systems and organisms to infer the functional logic of connectome-based neural circuits.

The purpose of this thesis was to determine whether the kappa opioid receptors modulating ultrasonic vocalizations production are located in the central and/or peripheral nervous system.

Our understanding of the complexity of the brain is limited by the data we can collect and analyze. Because of experimental limitations and a desire for greater detail, most investigations focus on just one aspect of the brain. For example, brain function can be studied at many levels of abstraction including, but not limited to, gene expression, protein interactions, anatomical regions, neuronal connectivity, synaptic plasticity, and the electrical activity of neurons. By focusing on each of these levels, neuroscience has built up a detailed picture of how the brain works, but each level is understood mostly in isolation from the others. It is likely that interaction between all these levels is just as important. Therefore, a key hypothesis is that functional units spanning multiple levels of biological organization exist in the brain. This project attempted to combine neuronal circuitry analysis with functional proteomics and anatomical regions of the brain to explore this hypothesis, and took an evolutionary view of the results obtained. During the process we had to solve a number of technical challenges as the tools to undertake this type of research did not exist. Two informatics challenges for this research were to develop ways to analyze neurobiological data, such as brain protein expression patterns, to extract useful information, and how to share and present this data in a way that is fast and easy for anyone to access. This project contributes towards a more wholistic understanding of the fruit fly brain in three ways. Firstly, a screen was conducted to record the expression of proteins in the brain of the fruit fly, Drosophila melanogaster. Protein expression patterns in the fruit fly brain were recorded from 535 protein trap lines using confocal microscopy. A total of 884 3D images were annotated and made available on an easy to use website database, BrainTrap, available at fruitfly.inf.ed.ac.uk/braintrap. The website allows 3D images of the protein expression to be viewed interactively in the web browser, and an ontology-based search tool allows users to search for protein expression patterns in specific areas of interest. Different expression patterns mapped to a common template can be viewed simultaneously in multiple colours. This data bridges the gap between anatomical and biomolecular levels of understanding. Secondly, protein trap expression patterns were used to investigate the properties of the fruit fly brain. Thousands of protein-protein interactions have been recorded by methods such as yeast two-hybrid, however many of these protein pairs do not express in the same regions of the fruit fly brain. Using 535 protein expression patterns it was possible to rule out 149 protein-protein interactions. Also, protein expression patterns registered against a common template brain were used to produce new anatomical breakdowns of the fruit fly brain. Clustering techniques were able to naturally segment brain regions based only on the protein expression data. This is just one example of how, by combining proteomics with anatomy, we were able to learn more about both levels of understanding. Results are analysed further in combination with networks such as genetic homology networks, and connectivity networks. We show how the wealth of biological and neuroscience data now available in public databases can be combined with the Brain- Trap data to reveal similarities between areas of the fruit fly and mammalian brain. The BrainTrap data also informs us on the process of evolution and we show that genes found in fruit fly, yeast and mouse are more likely to be generally expressed throughout the brain, whereas genes found only in fruit fly and mouse, but not yeast, are more likely to have a specific expression pattern in the fruit fly brain. Thus, by combining data from multiple sources we can gain further insight into the complexity of the brain. Neural connectivity data is also analyzed and a new technique for enhanced motifs is developed for the combined analysis of connectivity data with other information such as neuron type data and potentially protein expression data. Thirdly, I investigated techniques for imaging the protein trap lines at higher resolution using electron microscopy (EM) and developed new informatics techniques for the automated analysis of neural connectivity data collected from serial section transmission electron microscopy (ssTEM). Measurement of the connectivity between neurons requires high resolution imaging techniques, such as electron microscopy, and images produced by this method are currently annotated manually to produce very detailed maps of cell morphology and connectivity. This is an extremely time consuming process and the volume of tissue and number of neurons that can be reconstructed is severely limited by the annotation step. I developed a set of computer vision algorithms to improve the alignment between consecutive images, and to perform partial annotation automatically by detecting membrane, synapses and mitochondria present in the images. Accuracy of the automatic annotation was evaluated on a small dataset and 96% of membrane could be identified at the cost of 13% false positives. This research demonstrates that informatics technology can help us to automatically analyze biological images and bring together genetic, anatomical, and connectivity data in a meaningful way. This combination of multiple data sources reveals more detail about each individual level of understanding, and gives us a more wholistic view of the fruit fly brain.

INTRODUÇÃO: Existem alterações na morfologia encefálica durante o envelhecimento, que vão além da atrofia cerebral. Ainda deve ser considerado se essas alterações estão presentes em indivíduos sem comprometimento cognitivo e quais são os fatores associados a elas. OBJETIVO: Identificar se existem alterações morfométricas crânio - encefálicas em indivíduos sem comprometimento cognitivo e se essas alterações podem ser correlacionadas com fatores sócio- demográficos e clínicos, em uma série brasileira de casos autopsiados. METODOLODIA: Foi conduzido um estudo no Serviço de Verificação de Óbitos da Capital, onde 414 indivíduos necropsiados, com 50 anos ou mais de idade, foram submetidos à avaliação clínica completa e à análise morfométrica crânioencefálica (perímetro cefálico, peso, volume e densidade encefálicos). As correlações entre as alterações morfométricas cerebrais e os fatores associados (variáveis sócio demográficas e clínicas) foram obtidos por meio de análise uni e multivariadas. RESULTADOS: Amostra composta por 39,6% de mulheres e 60,4% de homens, com idade média de 68,5 (± 11,9 DP) e 66,2 (± 10,2 DP), respectivamente; maioria branca. Foi observada redução do perímetro cefálico com a idade, significante entre as mulheres, e associação discreta entre os homens. Peso e volume encefálicos diminuem com a idade. O peso médio do encéfalo da amostra toda foi de 1219,2 g (± 140,9 DP), e o volume médio foi de 1217,1 mL (± 152,3 DP). Homens apresentaram valores maiores de peso e volume encefálicos, e a redução foi mais pronunciada entre as mulheres. A densidade encefálica não se alterou em função da idade. Houve redução nos valores totais e corrigidos de peso e volume encefálicos, em algumas condições clínicas, mas apenas algumas se mostraram associadas com as reduções de peso e volume de acordo com a análise multivariada. A escolaridade se mostrou um fator protetor contra a redução de peso e volume encefálicos. CONCLUSÕES: Observouse que existem alterações morfométricas cerebrais no envelhecimento normal e, dentre os fatores associados a essas alterações, a maioria esta relacionada com o estilo de vida. Estes resultados permitem demonstrar que hábitos adequados devem ser implementados ao longo da vida visando o envelhecimento saudável / INTRODUCTION: Previous studies have led to the consensus that there are changes in brain morphology during aging, that go beyond brain atrophy. One important aspect to understand is wether there are morfometric brain changes in subjects without cognitive impairments and what are their correlations. OBJECTIVE: To describe whether there are morfometric brain and skull changes in cognitively normal elderly subjects, and if they can be correlated to some selected socio-demographic and clinical factors, in a large autopsy series from Brazil. METHODS: A cross sectional study was conducted in São Paulo Autopsy Service, where 414 autopsied subjects, 50 years and older, were clinically assessed and morphometrical encephalic and skull measurements (cephalic perimeter, brain weight, volume and density) were taken. Correlations among brain and skull changes and factors associated were obtained through, univariate and multivariated analysis. RESULTS: Sample was composed by 39,6% of females and 60,4% of males, with mean age of 68,5 (± 11,9 SD) and 66,2 (± 10,2 SD), respectivelly; mostly caucasians. There is a reduction of cephalic perimeter with age in females and a discrete decrease among men. Brain weight and brain volume decresed with aging. The mean brain weight was 1219,2 g (± 140,9 DP), and the mean brain volume was 1217,1 mL (± 152,3 DP), men presented with higher values for brain weight and volume than women, and the decrease in brain weight and volume were more pronounced in women than in men. Density has not decreased with aging. It has been observed a reduction in total and corrected brain weight and volume in some clinical conditions, but only some of them were estatistically significant in the multivariate analysys. Litteracy has shown to be a protective factor against the reduction of weight and volume. CONCLUSIONS: It was observed that there are morphometrical brain and skull changes during ageing, most related to lyfe style along lifetime. Results indicates that adequate habits must be implemented during lifetime aiming successfull ageing

O estudo foi realizado através de avaliação de 214 hemisférios cerebrais, de 107 espécimes humanos, com a idade variando desde 12 semanas de gestação até 8 meses pós-natal. A idade gestacional dos fetos foi calculada através do seu peso corpóreo. Os fetos com malformações congênitas ou com encéfalos danificados foram excluídos. Após a fixação do encéfalo em solução de formol a 10%, foi removida a aracnóide para a análise dos sulcos do cérebro, os quais foram então estudados, desde o seu aparecimento, até sua formação completa. A principal finalidade desse estudo foi estabelecer os padrões de desenvolvimento morfológico dos sulcos cerebrais característicos de cada idade gestacional. Tendo como base a análise dos resultados, foram estabelecidas tabelas de referências cronológicas pertinentes à formação de cada sulco em toda superfície do cérebro. / The study was done through the analysis of 214 brain hemispheres of 107 human brains, with their ages ranging from 12 weeks of gestation to 8 months of postnatal life. The gestational age was calculated from their body weight. The fetuses with congenital abnormalities and or damaged brains were excluded from the study. After the brain fixation with 10% formalin, the arachnoid was removed for the study of the sulci and fissures of the brain, since their appearance until their complete development. The aim of this anatomical study was to establish a reliable sulci morphological pattern characteristic of each gestational age. Based in our findings, reference tables pertinent to the appearance of each sulci of all brain surface were built and are here presented.