No / This paper presents realistic avatar movements using a limited number of sensors. An inverse kinematics algorithm, SHAKF, is used to configure an articulated skeletal model, and a neural network is employed to predict the movement of joints not bearing sensors. The results show that the neural network is able to give a very close approximation to the actual rotation of the joints. This allows a substantial reduction in the number of sensors to configure an articulated human skeletal model.

Creating effective cellular interfaces that can provide specific cellular signals is important for a number of fields ranging from tissue engineering to biosensors. Electroconducting polymers, especially polypyrrole (PPy), have attracted much attention for use in numerous biomedical applications since they provide a potential platform for local delivery of electrical stimuli to target tissues. To effectively modulate cellular functions at neural interfaces, it is essential to incorporate a range of extracellular cues into conducting polymers according to specific applications, such as nerve guidance conduits and implantable neural probes. For nerve regeneration scaffolds, three dimensional forms are desired for control of critical properties, such as porosity, mechanical strength, and topography. However, most researchers have worked on conventional two-dimensional PPy films, which cannot mimic a native three-dimensional architecture. Thus, a portion of my work has focused on introducing three-dimensional nanofibrous features into PPy. I have investigated various coating conditions to obtain uniform and conductive nanofibers. Effectiveness of electrical stimulation through the conducting nanofibers was confirmed by in vitro PC12 cell culture. The effects of different conducting nanofiber topographies (random and aligned) on cell adhesion and neurite outgrowth were examined in conjunction with electrical stimulation. The benefits of immobilized-NGF could be combined with electrical stimuli, which could be an ideal platform for neural tissue engineering scaffolds. Thus, I have modified conducting polymers to display neurotrophic activity. Nerve growth factor (NGF) was chemically immobilized on two dimensional and three dimensional PPy substrates. Specific chemical conjugation was achieved and characterized using diverse techniques. Immobilized NGF was as effective as exogenous NGF in medium in inducing neurite development and extension. NGF immobilized on functionalized PPy substrates was stable in a physiological solution and under electrical stimulation, indicating effective prolonged activity. I also investigated another important application of conducting polymer-based materials for neural interfacing - passivating electrodes with a biocompatible polysaccharide, hyaluronic acid (HA). I synthesized electrically polymerizable HA by chemically conjugating amine-functionalized pyrrole derivatives with HA. This coating was stable under physiological conditions for three months and resistant to enzymatic degradation. In vitro studies have shown the minimal adhesion and migration of astrocytes on the HA-coated electrodes. Implantation of HA-coated commercial probes into rat cortices for three weeks revealed attenuated reactive astrocyte responses from the coated wires, and the importance of glial interaction with non-conducting sites was demonstrated. / text

Affective computing has gained significant attention from researchers in the last decade due to the wide variety of applications that can benefit from this technology. Often, researchers describe affect using emotional dimensions such as arousal and valence. Valence refers to the spectrum of negative to positive emotions while arousal determines the level of excitement. Describing emotions through continuous dimensions (e.g. valence and arousal) allows us to encode subtle and complex affects as opposed to discrete emotions, such as the basic six emotions: happy, anger, fear, disgust, sad and neutral. Recognizing spontaneous and subtle emotions remains a challenging problem for computers. In our work, we employ two modalities of information: video and audio. Hence, we extract visual and audio features using deep neural network models. Given that emotions are time-dependent, we apply the Temporal Convolutional Neural Network (TCN) to model the variations in emotions. Additionally, we investigate an alternative model that combines a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN). Given our inability to fit the latter deep model into the main memory, we divide the RNN into smaller segments and propose a scheme to back-propagate gradients across all segments. We configure the hyperparameters of all models using Gaussian processes to obtain a fair comparison between the proposed models. Our results show that TCN outperforms RNN for the recognition of the arousal and valence emotional dimensions. Therefore, we propose the adoption of TCN for emotion detection problems as a baseline method for future work. Our experimental results show that TCN outperforms all RNN based models yielding a concordance correlation coefficient of 0.7895 (vs. 0.7544) on valence and 0.8207 (vs. 0.7357) on arousal on the validation dataset of SEWA dataset for emotion prediction.

Despite considerable progress in mapping the fly’s connectome and elucidating the patterns of information flow in its brain, the complexity of the fly brain’s structure and the still-incomplete state of knowledge regarding its neural circuitry pose significant challenges beyond satisfying the computational resource requirements of current fly brain models that must be addressed to successfully reverse the information processing capabilities of the fly brain. These include the need to explicitly facilitate collaborative development of brain models by combining the efforts of multiple researchers, and the need to enable programmatic generation of brain models that effectively utilize the burgeoning amount of increasingly detailed publicly available fly connectome data. This thesis presents an open pipeline for modular construction of executable models of the fruit fly brain from incomplete biological brain data that addresses both of the above requirements. This pipeline consists of two major open-source components respectively called Neurokernel and NeuroArch. Neurokernel is a framework for collaborative construction of executable connectome-based fly brain models by integration of independently developed models of different functional units in the brain into a single emulation that can be executed upon multiple Graphics Processing Units (GPUs). Neurokernel enforces a programming model that enables functional unit models that comply with its interface requirements to communicate during execution regardless of their internal design. We demonstrate the power of this programming model by using it to integrate independently developed models of the fly retina and lamina into a single vision processing system. We also show how Neurokernel’s communication performance can scale over multiple GPUs, number of functional units in a brain emulation, and over the number of communication ports exposed by a functional unit model. Although the increasing amount of experimentally obtained biological data regarding the fruit fly brain affords brain modelers a potentially valuable resource for model development, the actual use of this data to construct executable neural circuit models is currently challenging because of the disparate nature of different data sources, the range of storage formats they use, and the limited query features of those formats complicates the process of inferring executable circuit designs from biological data. To overcome these limitations, we created a software package called NeuroArch that defines a data model for concurrent representation of both biological data and model structure and the relationships between them within a single graph database. Coupled with a powerful interface for querying both types of data within the database in a uniform high-level manner, this representation enables construction and dispatching of executable neural circuits to Neurokernel for execution and evaluation. We demonstrate the utility of the NeuroArch/Neurokernel pipeline by using the packages to generate an executable model of the central complex of the fruit fly brain from both published and hypothetical data regarding overlapping neuron arborizations in different regions of the central complex neuropils. We also show how the pipeline empowers circuit model designers to devise computational analogues to biological experiments such as parallel concurrent recording from multiple neurons and emulation of genetic mutations that alter the fly’s neural circuitry.

Population-wide synchronized rhythmic bursts of electrical activity are present in a variety of neural circuits. The proposed general mechanisms for rhythmogenesis are often attributed to intrinsic and synaptic properties. For example, the recurrent excitation through excitatory synaptic connections determines burst initiation, and the slower kinetics of ionic currents or synaptic depression results in burst termination. In such theories, a slow recovery process is essential for the slow dynamics associated with bursting. This thesis presents a new hypothesis that depends on the connectivity pattern among neurons rather than a slow kinetic process to achieve the network-wide bursting. The thesis begins with an introduction of bursts of electrical activity in a purely excitatory neural network and existing theories explaining this phenomenon. It then covers the small-world approach, which is applied to modify the network structure in the simulation, and the Morris-Lecar (ML) neuron model, which is used as the component cells in the network. Simulation results of the dependence of bursting activity on network connectivity, as well as the inherent network properties explaining this dependence are described. This work shows that the network-wide bursting activity emerges in the small-world network regime but not in the regular or random networks, and this small-world bursting primarily results from the uniform random distribution of long-range connections in the network, as well as the unique dynamics in the ML model. Both attributes foster progressive synchronization in firing activity throughout the network during a burst, and this synchronization may terminate a burst in the absence of an obvious slow recovery process. The thesis concludes with possible future work.

Amputation is a life-changing event that results in a drastic reduction in quality of life including extreme loss of function and severe mental, emotional and physical pain. In order to mitigate these negative outcomes, there is great interest in the design of ‘advanced/robotic’ prosthetics that cosmetically and functionally mimic the lost limb. While the robotics side of advanced prosthetics has seen many advances recently, they still provide only a fraction of the natural limbs’ functionality. At the heart of the issue is the interface between the robotic limb and the individual that needs significant development. Amputees retain significant function in their nerves post-amputation, which offers a unique opportunity to interface with the peripheral nerve. Here we evaluate a relatively new approach to peripheral nerve interfacing by using microchannels, which hold the intrinsic ability to record larger neural signals from nerves than previously developed peripheral nerve interfaces. We first demonstrate that microchannel scaffolds are well suited for chronic integration with amputated nerves and promote highly organized nerve regeneration. We then demonstrate the ability to record neural signals, specifically action potentials, using microchannels permanently integrated with electrodes after chronic implantation in a terminal study. Together these studies suggest that microchannels are well suited for chronic implantation and stable peripheral nerve interfacing. As a next step toward clinical translation, we developed fully-integrated high electrode count microchannel interfacing technology capable of functioning while implanted in awake and freely moving animal models as needed for pre-clinical evaluation. Importantly, fabrication techniques were developed that apply to a broad range of flexible devices/sensors benefiting from flexible interconnects, surface mount device (SMD) integration, and/or operation in aqueous environments. Examples include diabetic glucose sensors, flexible skin based health monitors, and the burgeoning flexible wearable technology industry. Finally, we successfully utilized the fully integrated microchannel interfaces to record action potentials in the challenging awake and freely moving animal model validating the microchannel approach for peripheral nerve interfacing. In the end, the findings of these studies help direct and give significant credence to future technology development enabling eventual clinical application of microchannels for peripheral nerve interfacing.

Artificial neural networks (ANN) can detect complex non-linear relationships between independent and dependent variables. Properly trained ANNs have repeatedly demonstrated superior predictive accuracy to other predictive technologies when applied to non-linear systems. Currently there are no studies that have been carried out on predicting survival of leukaemia patients at all. The neural network prediction method adopted in this study aims to provide a robust and accurate method for predicting survival of leukaemia patients for both censored and uncensored patient data. The aim of this research was also to find out the effectiveness of neural networks in modelling leukaemia prognosis and to determine the factors that have the most influence. There is ongoing research into finding ways and means of extending the life span of diseased patients. There is great interest in identifying factors that will yield better predictions of survival for terminally ill leukaemia patients. Prognostic factors generally differ with the treatment of leukaemia. Clinicians face the problem of how to choose the appropriate treatment regime, therefore an analysis of prognostic factors that predict success or failure may identify patients who require an alternative approach of specialist or targeted treatment. Being able to predict an individual patient’s prognosis will enable clinicians to categorise them into the relevant high and low risk treatment groups for conventional treatment or allow for the patients to be incorporated into specialised treatment schedules and clinical trials if available. In this study there is believed to be relationship that exists between the results gained on diagnosis and the period of survival. A patient’s health status is dependent on various symptoms and the complexity of the medical condition is dependent on an individual’s biological system. This complexity allows for the application of artificial neural networks (ANN) in predicting outcomes in medical application, especially in prognosis prediction and survival rate. This thesis contains contributions to the development of neural network models for survival analysis of leukaemia patients. The feed forward back propagation algorithm (BPA) modified to the gradient descent BPA was identified for the training and building of the neural network for predicting survival of leukaemia patients. The prognostic factors that affect survival have also been determined by the neural networks. The comparisons of models were based on using combined groups of leukaemia patients and comparing them with individual groups of the sub-types of leukaemia, i.e. acute lymphoid leukaemia (ALL), acute myeloid leukaemia (AML), chronic myeloid leukaemia (CLL) and chronic myeloid leukaemia (CML). A combination of 38 variables was used in the development of the neural networks. The variables were age, race, sex, gender, and results of full blood counts, differential tests and flow cytometry. The survival period of patients was based on the diagnosis date and the date of treatment. Those patients who status of mortality was known as of October 2008 were considered to be uncensored and were used for the 2-year and 3-year case studies. The patients with unknown mortality were considered as censored patients and used for the censored case study. The patient data was processed into a coded system and used to build the neural networks for each data set. The choice of patient groups used for the model building was prompted by the availability of uncensored data for analysis. For the group of combined leukaemia patients and the sub-group CML-CLL, it is recommended that the 2-year neural network model be used. The main prognostic factors affecting leukaemia survival were found to be the patient’s age, the mean haemoglobin concentration, % neutrophils and the markers CD13, CD20 and CD56. The race group, platelet count, % monocytes and the markers CD3, CD4, CD34 and LC lambda were found to significantly affect the CML-CLL group of patients. For the ALL and AML groups the 3-year neural network models were favoured. Prognostic factors for the survival of ALL patients were their age, the mean corpuscular haemoglobin concentration, % blasts and the markers CD8 and CD22. For the AML group the important prognostic factors were the patient’s age, the mean corpuscular haemoglobin concentration, the % neutrophils, % lymphocytes, and the markers CD7 and CD34. / Thesis (Ph.D.)-University of KwaZulu-Natal, Durban, 2009.

Description of the cell cycle features of neural progenitors during late stages of neurogenesis in a gyrencephalic mammal, the ferret.

Robot control systems evolved with genetic algorithms traditionally take the form of floating-point neural network models. This thesis proposes that digital control systems, such as quantised neural networks and logical networks, may also be used for the task of robot control. The inspiration for this is the observation that the dynamics of discrete networks may contain cyclic attractors which generate rhythmic behaviour, and that rhythmic behaviour underlies the central pattern generators which drive lowlevel motor activity in the biological world. To investigate this a series of experiments were carried out in a simulated physically realistic 3D world. The performance of evolved controllers was evaluated on two well known control tasks—pole balancing, and locomotion of evolved morphologies. The performance of evolved digital controllers was compared to evolved floating-point neural networks. The results show that the digital implementations are competitive with floating-point designs on both of the benchmark problems. In addition, the first reported evolution from scratch of a biped walker is presented, demonstrating that when all parameters are left open to evolutionary optimisation complex behaviour can result from simple components.

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