This work characterized an in vitro 3-D neural co-culture model electrophysiologically via multi electrode arrays (MEAs), and morphologically via immunocytochemistry. Since MEA surface insulation SU-8 2000 can be used in neural micro- and multi- electrode arrays, this investigation first developed techniques to make SU-8 2000 cytocompatible. The in vitro 3-D neural co-culture model was then used to study viability and electrophysiological responses to physical injury as well as drugs known to affect network signaling. 1) SU-8 2000 cytotoxicity to neuronal cultures was linked to both poor adhesive properties and toxic components, such as solvents and photo acid generator elements. Surface treatments of oxygen plasma or parylene coating following optimal combinations of heat and isopropanol sonication showed improvement in SU-8 2000 cytocompatibility. 2) The 3-D neural networks within the 3-D co-cultures maintained considerable process outgrowth and complex 3-D structure. The cultures were viable up to three weeks in vitro with functional synaptic connections and spontaneous electrophysiological activity that was responsive to chemical modulation. This electrophysiological activity was modulated by synaptic inhibition. 3) Injury experiments demonstrated that both shear and compression loading significantly increased acute membrane permeability of cells in a strain rate dependent manner. Cell death correlated with higher membrane permeability, and shear resulted in more death than compression in these 3-D cultures. While techniques were developed for making a major micro-fabrication material cytocompatible, engineering the 3-D neural co-culture resulted in a more physiologically-representative neural tissue platform, allowing an increased understanding of structure-function relationships. Overall, this research established and characterized a neural culture system for the mechanistic study of cell growth, cell-cell and cell-matrix interactions, as well as the responses to chemical or mechanical perturbations. This is the first investigation of the network-level electrophysiological activity of 3-D dissociated cultures. This system can be used to model various pathological states in vitro, testing various reparative drugs; cell-, and tissue-engineering based strategies; as well as for pre-animal and pre-clinical testing of neural implants.

In this thesis we use computational neural network models to examine the dynamics and functionality of the CA3 region of the mammalian hippocampus. The emphasis of the project is to investigate how the dynamic control structures provided by inhibitory circuitry and cellular modification may effect the CA3 region during the recall of previously stored information. The CA3 region is commonly thought to work as a recurrent auto-associative neural network due to the neurophysiological characteristics found, such as, recurrent collaterals, strong and sparse synapses from external inputs and plasticity between coactive cells. Associative memory models have been developed using various configurations of mathematical artificial neural networks which were first developed over 40 years ago. Within these models we can store information via changes in the strength of connections between simplified model neurons (two-state). These memories can be recalled when a cue (noisy or partial) is instantiated upon the net. The type of information they can store is quite limited due to restrictions caused by the simplicity of the hard-limiting nodes which are commonly associated with a binary activation threshold. We build a much more biologically plausible model with complex spiking cell models and with realistic synaptic properties between cells. This model is based upon some of the many details we now know of the neuronal circuitry of the CA3 region. We implemented the model in computer software using Neuron and Matlab and tested it by running simulations of storage and recall in the network. By building this model we gain new insights into how different types of neurons, and the complex circuits they form, actually work. The mammalian brain consists of complex resistive-capacative electrical circuitry which is formed by the interconnection of large numbers of neurons. A principal cell type is the pyramidal cell within the cortex, which is the main information processor in our neural networks. Pyramidal cells are surrounded by diverse populations of interneurons which have proportionally smaller numbers compared to the pyramidal cells and these form connections with pyramidal cells and other inhibitory cells. By building detailed computational models of recurrent neural circuitry we explore how these microcircuits of interneurons control the flow of information through pyramidal cells and regulate the efficacy of the network. We also explore the effect of cellular modification due to neuronal activity and the effect of incorporating spatially dependent connectivity on the network during recall of previously stored information. In particular we implement a spiking neural network proposed by Sommer and Wennekers (2001). We consider methods for improving associative memory recall using methods inspired by the work by Graham and Willshaw (1995) where they apply mathematical transforms to an artificial neural network to improve the recall quality within the network. The networks tested contain either 100 or 1000 pyramidal cells with 10% connectivity applied and a partial cue instantiated, and with a global pseudo-inhibition.We investigate three methods. Firstly, applying localised disynaptic inhibition which will proportionalise the excitatory post synaptic potentials and provide a fast acting reversal potential which should help to reduce the variability in signal propagation between cells and provide further inhibition to help synchronise the network activity. Secondly, implementing a persistent sodium channel to the cell body which will act to non-linearise the activation threshold where after a given membrane potential the amplitude of the excitatory postsynaptic potential (EPSP) is boosted to push cells which receive slightly more excitation (most likely high units) over the firing threshold. Finally, implementing spatial characteristics of the dendritic tree will allow a greater probability of a modified synapse existing after 10% random connectivity has been applied throughout the network. We apply spatial characteristics by scaling the conductance weights of excitatory synapses which simulate the loss in potential in synapses found in the outer dendritic regions due to increased resistance. To further increase the biological plausibility of the network we remove the pseudo-inhibition and apply realistic basket cell models with differing configurations for a global inhibitory circuit. The networks are configured with; 1 single basket cell providing feedback inhibition, 10% basket cells providing feedback inhibition where 10 pyramidal cells connect to each basket cell and finally, 100% basket cells providing feedback inhibition. These networks are compared and contrasted for efficacy on recall quality and the effect on the network behaviour. We have found promising results from applying biologically plausible recall strategies and network configurations which suggests the role of inhibition and cellular dynamics are pivotal in learning and memory.

Development of a three dimensional (3D) HYBROT model with targeted in vivo like intact cellular circuitry in thick brain slices for multi-site stimulation and recording will provide a useful in vitro model to study neuronal dynamics at network level. In order to make this in vitro model feasible, we need to develop several associated technologies. These technologies include development of a thick organotypic brain slice culturing method, a three dimensional (3D) micro-fluidic multielectrode Neural Interface system (µNIS) and the associated electronic interfaces for stimulation and recording of/from tissue, development of targeted stimulation patterns for closed-loop interaction with a robotic body, and a deep-tissue non-invasive imaging system. To make progress towards this goal, I undertook two projects: (i) to develop a method to culture thick organotypic brain slices, and (ii) construct a multiphoton imaging system that allows long-term and deep-tissue imaging of two dimensional and three dimensional cultures. Organotypic brain slices preserve cytoarchitecture of the brain. Therefore, they make more a realistic reduced model for various network level investigations. However, current culturing methods are not successful for culturing thick brain slices due to limited supply of nutrients and oxygen to inner layers of the culture. We developed a forced-convection based perfusion method to culture viable 700µm thick brain slices. Multiphoton microscopy is ideal for imaging living 2D or 3D cultures at submicron resolution. We successfully fabricated a custom-designed high efficiency multiphoton microscope that has the desired flexibility to perform experiments using multiple technologies simultaneously. This microscope was used successfully for 3D and time-lapse imaging. Together these projects have contributed towards the progress of development of a 3D HYBROT. ----- 3D Hybrot: A hybrid system of a brain slice culture embodied with a robotic body.

[Truncated abstract] The goal of this research was to investigate some of the ways that neurons located in the primary motor cortex (MI) code for skilled movement. The task-related and temporally correlated spike activity that occurred during the performance of a goal-directed reaching and retrieval task invloving multiple motion elements and limbs was evaluated in cats. The contributions made by different neuronal subtypes loctaed in MI (which were identified based upon extracellular spiking features0 to the coding of movement was also investigated. Spike activity was simulateously recorded from microelectrodes that were chronically implanted into the motor cortex of both cerebral hemispheres. Task-related neurons modulated their activity during the reaching and retrieval movements of one forelimb, or the postural reactions of the contralateral forelimb and ipsilateral hindlimb. Spike durations and baseline firing rates of neurons were used to distinguish between putative excitatory (Regular Spiking; RS) and inhibitory (Fast Spiking; FS) neurons in the cortex. Frame by frame video analysis of the task was used to subdivide each task trial into stages (e.g. premovement, reach, withdraw and feed) and relate modulations in neural activity to the individual task stages. Task-related neurons were classified as either narrowly tuned or broadly tuned depending on whether their activity modulated during a single task stage or more than one stage respectively. Recordings were made from 163 task-related neurons, and temporal correlations in the spike activity of simultaneously recorded neurons were identified using shuffle corrected cross-correlograms on 662 different neuronal pairs.... The results of this research suggest that temporally correlated activity may reflect the activation of intracortical and callosal connections between a variety of efferent zones involved in task performance, playing a role in the coordination of muscles and limbs during motor tasks. The differences in the patterns of task-related activity, and in the incidence of significant neuronal interactions that were observed between the RS and FS neuronal populations implies that they make different contributions to the coding of movement in MI.

Esta dissertação apresenta o desenvolvimento de um sistema em forma de armband para a captura de sinais de eletromiográficos de superfície para o reconhecimento de movimentos do braço. São apresentadas todas as suas etapas de projeto, desde a construção física, projeto de circuitos, sistema de aquisição, processamento e classificação por meio de Redes Neurais Artificiais. Foi construído um bracelete contendo oito canais para a captação do sinal de eletromiografia e um sistema auxiliar (giroscópio) de referência foi utilizado para indicar o instante em que o braço foi movimentado. Foram adquiridos dados dos grupos musculares do bíceps e do tríceps. Por meio da fusão de dados de sensores, os sinais foram processados por meio de rotinas no software LabVIEWTM. Após a extração de características do sinal, as amostras foram encaminhadas para uma Rede Neural Multi-Layer Perceptron para a classificação dos movimentos de flexão e extensão do braço. A mesma armband foi inserida na região do antebraço e os sinais de eletromiografia foram comparados com os sinais obtidos pelo dispositivo comercial MyoTM. O sistema apresentou como resultado altas taxas de classificação, acima de 95% e os sinais obtidos na região do antebraço apresentaram semelhanças com os obtidos pelo dispositivo comercial. / This master thesis presents the development of an armband system to capture of superficial electromyography signals to arm movement recognition. All project steps, since the physical building, project of the circuits, acquisition system, processing and classification by Artificial Neural Networks are presented. An armband with eight channel to capture the electromyography signal was constructed and an auxiliary system (gyroscope) was used to indicate the instant when the arm was moved. The muscle acquired groups were the biceps and triceps. By sensor data fusion, the signals were processed by LabVIEWTM routines. After the signal characteristic extraction, the samples were forwarded to a Multi-Layer Perceptron Neural Network to movement classification of arm flexion and extension. The same armband was inserted on the forearm and the electromyography signals were compared with the signals obtained by the commercial device MyoTM. The system presented, as results, high classification rates, above of 95% and the obtained signals on the region of forearm showed similarities with the obtained ones by commercial device.

Esta dissertação apresenta o desenvolvimento de um sistema em forma de armband para a captura de sinais de eletromiográficos de superfície para o reconhecimento de movimentos do braço. São apresentadas todas as suas etapas de projeto, desde a construção física, projeto de circuitos, sistema de aquisição, processamento e classificação por meio de Redes Neurais Artificiais. Foi construído um bracelete contendo oito canais para a captação do sinal de eletromiografia e um sistema auxiliar (giroscópio) de referência foi utilizado para indicar o instante em que o braço foi movimentado. Foram adquiridos dados dos grupos musculares do bíceps e do tríceps. Por meio da fusão de dados de sensores, os sinais foram processados por meio de rotinas no software LabVIEWTM. Após a extração de características do sinal, as amostras foram encaminhadas para uma Rede Neural Multi-Layer Perceptron para a classificação dos movimentos de flexão e extensão do braço. A mesma armband foi inserida na região do antebraço e os sinais de eletromiografia foram comparados com os sinais obtidos pelo dispositivo comercial MyoTM. O sistema apresentou como resultado altas taxas de classificação, acima de 95% e os sinais obtidos na região do antebraço apresentaram semelhanças com os obtidos pelo dispositivo comercial. / This master thesis presents the development of an armband system to capture of superficial electromyography signals to arm movement recognition. All project steps, since the physical building, project of the circuits, acquisition system, processing and classification by Artificial Neural Networks are presented. An armband with eight channel to capture the electromyography signal was constructed and an auxiliary system (gyroscope) was used to indicate the instant when the arm was moved. The muscle acquired groups were the biceps and triceps. By sensor data fusion, the signals were processed by LabVIEWTM routines. After the signal characteristic extraction, the samples were forwarded to a Multi-Layer Perceptron Neural Network to movement classification of arm flexion and extension. The same armband was inserted on the forearm and the electromyography signals were compared with the signals obtained by the commercial device MyoTM. The system presented, as results, high classification rates, above of 95% and the obtained signals on the region of forearm showed similarities with the obtained ones by commercial device.

Acrolein is produced endogenously after traumatic brain injury (TBI) and is considered a primary mechanism for secondary damage occurring after TBI. We are using frontal cortex networks derived from mouse embryos and grown on microelectrode arrays in vitro to monitor the spontaneous activity of networks and the changes that occur after acrolein application. Networks exposed to acrolein exhibit a biphasic response profile. An initial increase in network activity, followed by a decrease to 100% activity loss in applications ≥ 50 µM. In applications below 50 µM, acrolein was not toxic but generated activity instability with coordinated but irregular population busts lasting for up to 6 days. The increase in activity preceding toxicity may be linked to a decrease in free spermine, a free radical scavenger that modulates Na+, K+, Ca+ channels as well as NMDA, Kainate, and AMPA receptors. Action potential wave shape analysis after 20 and 30 µM acrolein application revealed a concentration-dependent 15-33% increase in peak to peak amplitude within minutes after exposure. For the same concentrations of acrolein (50 µM), the time required to reach 100% activity loss (IT100) was longer in serum-free medium than in medium with 5% serum, in which IT100 values were reduced by a factor of 4. The greater toxicity in the presence of serum may be explained by acrolein adducts on serum proteins. These reaction products have been shown by other labs to be toxic in cell culture. This in vitro system could be used to expand biochemical analyses such as acrolein-induced spermine depletion and may provide an effective platform for investigating cell culture correlates of secondary TBI damage.

This thesis explores the use of artificial neural networks for modelling cognitive processes. It presents an exposition of the neural network paradigm, and evaluates its viability in relation to the classical, symbolic approach in cognitive science. Classical researchers have approached the description of cognition by concentrating mainly on an abstract, algorithmic level of description in which the information processing properties of cognitive processes are emphasised. The approach is founded on seminal ideas about computation, and about algorithmic description emanating, amongst others, from the work of Alan Turing in mathematical logic. In contrast to the classical conception of cognition, neural network approaches are based on a form of neurocomputation in which the parallel distributed processing mechanisms of the brain are highlighted. Although neural networks are generally accepted to be more neurally plausible than their classical counterparts, some classical researchers have argued that these networks are best viewed as implementation models, and that they are therefore not of much relevance to cognitive researchers because information processing models of cognition can be developed independently of considerations about implementation in physical systems. In the thesis I argue that the descriptions of cognitive phenomena deriving from neural network modelling cannot simply be reduced to classical, symbolic theories. The distributed representational mechanisms underlying some neural network models have interesting properties such as similarity-based representation, content-based retrieval, and coarse coding which do not have straightforward equivalents in classical systems. Moreover, by placing emphasis on how cognitive processes are carried out by brain-like mechanisms, neural network research has not only yielded a new metaphor for conceptualising cognition, but also a new methodology for studying cognitive phenomena. Neural network simulations can be lesioned to study the effect of such damage on the behaviour of the system, and these systems can be used to study the adaptive mechanisms underlying learning processes. For these reasons, neural network modelling is best viewed as a significant theoretical orientation in the cognitive sciences, instead of just an implementational endeavour. / Psychology / D. Litt. et Phil. (Psychology)

Avec le besoin de développer un organe artificiel remplaçant le doigt humain dans le cas d'un déficit et la nécessité de comprendre le fonctionnement de ce système physiologique, un modèle physique inverse du système doigt, permettant de chercher les activations neuronales à partir du mouvement, est nécessaire. Malgré le grand nombre d'études dans la modélisation de la main humaine, presque il n'existe aucun modèle physique inverse du système doigt majeur qui s'intéresse à chercher les activations neuronales. Presque tous les modèles existants se sont intéressés à la recherche des forces et des activations musculaires. L'objectif de la thèse est de présenter un modèle neuromusculo-squelettique du système doigt majeur humain permettant d'obtenir les activations neuronales, les activations musculaires et les forces musculaires des tous les muscles agissants sur le système doigt d'après l'analyse du mouvement. Le but de ce type des modèles est de représenter les caractéristiques essentielles du mouvement avec le plus de réalisme possible. Notre travail consiste à étudier, modéliser et à simuler le mouvement du doigt humain. L'innovation du modèle proposé est le couplage entre la biomécanique et les aspects neurophysiologiques afin de simuler la chaine inverse complet du mouvement en allant des données dynamiques du doigt aux intentions neuronales qui contrôlent les activations musculaires. L'autre innovation est la conception d'un protocole expérimental spécifique qui traite à la fois les données sEMG multicanal et les données cinématiques d'après une procédure de capture de mouvement. / With the need to develop an artificial organ replacing the human finger in the case of a deficiency and the need to understand how this physiological system works, an inverse physical model of the finger system for estimating neuronal activations from the movement, is necessary. Despite the large number of studies in the human hand modeling, almost there is no inverse physical model of the middle finger system that focuses on search neuronal activations. Al most all existing models have focused on the research of the muscle forces and muscle activations. The purpose of the manuscript is to present a neuromusculoskeletal model of the human middle finger system for estimating neuronal activations, muscle activations and muscle forces of all the acting muscles after movement analysis. The aim of such models is to represent the essential characteristics of the movement with the best possible realism. Our job is to study, model and simulate the movement of the human finger. The innovation of the proposed model is the coupling between the biomechanical and neurophysiological aspects to simulate the complete inverse movement chain from dynamic finger data to neuronal intents that control muscle activations. Another innovation is the design of a specific experimental protocol that treats both the multichannel sEMG and kinematic data from a data capture procedure of the movement.

This thesis explores the use of artificial neural networks for modelling cognitive processes. It presents an exposition of the neural network paradigm, and evaluates its viability in relation to the classical, symbolic approach in cognitive science. Classical researchers have approached the description of cognition by concentrating mainly on an abstract, algorithmic level of description in which the information processing properties of cognitive processes are emphasised. The approach is founded on seminal ideas about computation, and about algorithmic description emanating, amongst others, from the work of Alan Turing in mathematical logic. In contrast to the classical conception of cognition, neural network approaches are based on a form of neurocomputation in which the parallel distributed processing mechanisms of the brain are highlighted. Although neural networks are generally accepted to be more neurally plausible than their classical counterparts, some classical researchers have argued that these networks are best viewed as implementation models, and that they are therefore not of much relevance to cognitive researchers because information processing models of cognition can be developed independently of considerations about implementation in physical systems. In the thesis I argue that the descriptions of cognitive phenomena deriving from neural network modelling cannot simply be reduced to classical, symbolic theories. The distributed representational mechanisms underlying some neural network models have interesting properties such as similarity-based representation, content-based retrieval, and coarse coding which do not have straightforward equivalents in classical systems. Moreover, by placing emphasis on how cognitive processes are carried out by brain-like mechanisms, neural network research has not only yielded a new metaphor for conceptualising cognition, but also a new methodology for studying cognitive phenomena. Neural network simulations can be lesioned to study the effect of such damage on the behaviour of the system, and these systems can be used to study the adaptive mechanisms underlying learning processes. For these reasons, neural network modelling is best viewed as a significant theoretical orientation in the cognitive sciences, instead of just an implementational endeavour. / Psychology / D. Litt. et Phil. (Psychology)