Development of a Bio-Inspired Computational Astrocyte Model for Spiking Neural Networks

The mammalian brain is the most capable and complex computing entity known today. For many years there has been research focused on reproducing the brain\u27s processing capabilities. An early example of this endeavor was the perceptron which has become the core building block of neural network models in the deep learning era. Deep learning has had tremendous success in well-defined tasks like object detection, games like go and chess, and automatic speech recognition. In fact, some deep learning models can match and even outperform humans in specific situations. However, in general, they require much more training, have higher power consumption, are more susceptible to noise and adversarial perturbations, and have very different behavior than their biological counterparts. In contrast, spiking neural network models take a step closer to biology, and in some cases behave identically to measurements of real neurons. Though there has been advancement, spiking neural networks are far from reaching their full potential, in part because the full picture of their biological underpinnings is unclear. This work attempts to reduce that gap further by exploring a bio-inspired configuration of spiking neurons coupled with a computational astrocyte model. Astrocytes, initially thought to be passive support cells in the brain are now known to actively participate in neural processing. They are believed to be critical for some processes, such as neural synchronization, self-repair, and learning. The developed astrocyte model is geared towards synaptic plasticity and is shown to improve upon existing local learning rules, as well as create a generalized approach to local spike-timing-dependent plasticity. Beyond generalizing existing learning approaches, the astrocyte is able to leverage temporal and spatial integration to improve convergence, and tolerance to noise. The astrocyte model is expanded to influence multiple synapses and configured for a specific learning task. A single astrocyte paired with a single leaky integrate and fire neuron is shown to converge on a solution in 2, 3, and 4 synapse configurations. Beyond the more concrete improvements in plasticity, this work provides a foundation for exploring supervisory astrocyte-like elements in spiking neural networks, and a framework to implement and extend many three-factor learning rules. Overall, this work brings the field a bit closer to leveraging some of the distinct advantages of biological neural networks

3D CNN methods in biomedical image segmentation

A definite trend in Biomedical Imaging is the one towards the integration of increasingly complex interpretative layers to the pure data acquisition process. One of the most interesting and looked-forward goals in the field is the automatic segmentation of objects of interest in extensive acquisition data, target that would allow Biomedical Imaging to look beyond its use as a purely assistive tool to become a cornerstone in ambitious large-scale challenges like the extensive quantitative study of the Human Brain. In 2019 Convolutional Neural Networks represent the state of the art in Biomedical Image segmentation and scientific interests from a variety of fields, spacing from automotive to natural resource exploration, converge to their development. While most of the applications of CNNs are focused on single-image segmentation, biomedical image data -being it MRI, CT-scans, Microscopy, etc- often benefits from three-dimensional volumetric expression. This work explores a reformulation of the CNN segmentation problem that is native to the 3D nature of the data, with particular interest to the applications to Fluorescence Microscopy volumetric data produced at the European Laboratories for Nonlinear Spectroscopy in the context of two different large international human brain study projects: the Human Brain Project and the White House BRAIN Initiative

Configuring spiking neural network training algorithms

Spiking neural networks, based on biologically-plausible neurons with temporal information coding, are provably more powerful than widely used artificial neural networks based on sigmoid neurons (ANNs). However, training them is more challenging than training ANNs. Several methods have been proposed in the literature, each with its limitations: SpikeProp, NSEBP, ReSuMe, etc. And setting numerous parameters of spiking networks to obtain good accuracy has been largely ad hoc. In this work, we used automated algorithm configuration tools to determine optimal combinations of parameters for ANNs, artificial neural networks with components simulating glia cells (astrocytes), and for spiking neural networks with SpikeProp learning algorithm. This allowed us to achieve better accuracy on standard datasets (Iris and Wisconsin Breast Cancer), and showed that even after optimization augmenting an artificial neural network with glia results in improved performance. Guided by the experimental results, we have developed methods for determining values of several parameters of spiking neural networks, in particular weight and output ranges. These methods have been incorporated into a SpikeProp implementation

Méthodes d'apprentissage automatique pour la segmentation de tumeurs au cerveau

Abstract : Malignant brain tumors are the second leading cause of cancer related deaths in children under 20. There are nearly 700,000 people in the U.S. living with a brain tumor and 17,000 people are likely to loose their lives due to primary malignant and central nervous system brain tumor every year. To identify whether a patient is diagnosed with brain tumor in a non-invasive way, an MRI scan of the brain is acquired followed by a manual examination of the scan by an expert who looks for lesions (i.e. cluster of cells which deviate from healthy tissue). For treatment purposes, the tumor and its sub-regions are outlined in a procedure known as brain tumor segmentation . Although brain tumor segmentation is primarily done manually, it is very time consuming and the segmentation is subject to variations both between observers and within the same observer. To address these issues, a number of automatic and semi-automatic methods have been proposed over the years to help physicians in the decision making process. Methods based on machine learning have been subjects of great interest in brain tumor segmentation. With the advent of deep learning methods and their success in many computer vision applications such as image classification, these methods have also started to gain popularity in medical image analysis. In this thesis, we explore different machine learning and deep learning methods applied to brain tumor segmentation.Résumé: Les tumeurs malignes au cerveau sont la deuxième cause principale de décès chez les enfants de moins de 20 ans. Il y a près de 700 000 personnes aux États-Unis vivant avec une tumeur au cerveau, et 17 000 personnes sont chaque année à risque de perdre leur vie suite à une tumeur maligne primaire dans le système nerveu central. Pour identifier de façon non-invasive si un patient est atteint d'une tumeur au cerveau, une image IRM du cerveau est acquise et analysée à la main par un expert pour trouver des lésions (c.-à-d. un groupement de cellules qui diffère du tissu sain). Une tumeur et ses régions doivent être détectées à l'aide d'une segmentation pour aider son traitement. La segmentation de tumeur cérébrale et principalement faite à la main, c'est une procédure qui demande beaucoup de temps et les variations intra et inter expert pour un même cas varient beaucoup. Pour répondre à ces problèmes, il existe beaucoup de méthodes automatique et semi-automatique qui ont été proposés ces dernières années pour aider les praticiens à prendre des décisions. Les méthodes basées sur l'apprentissage automatique ont suscité un fort intérêt dans le domaine de la segmentation des tumeurs cérébrales. L'avènement des méthodes de Deep Learning et leurs succès dans maintes applications tels que la classification d'images a contribué à mettre de l'avant le Deep Learning dans l'analyse d'images médicales. Dans cette thèse, nous explorons diverses méthodes d'apprentissage automatique et de Deep Learning appliquées à la segmentation des tumeurs cérébrales

Brain Tumor Diagnosis Support System: A decision Fusion Framework

An important factor in providing effective and efficient therapy for brain tumors is early and accurate detection, which can increase survival rates. Current image-based tumor detection and diagnosis techniques are heavily dependent on interpretation by neuro-specialists and/or radiologists, making the evaluation process time-consuming and prone to human error and subjectivity. Besides, widespread use of MR spectroscopy requires specialized processing and assessment of the data and obvious and fast show of the results as photos or maps for routine medical interpretative of an exam. Automatic brain tumor detection and classification have the potential to offer greater efficiency and predictions that are more accurate. However, the performance accuracy of automatic detection and classification techniques tends to be dependent on the specific image modality and is well known to vary from technique to technique. For this reason, it would be prudent to examine the variations in the execution of these methods to obtain consistently high levels of achievement accuracy. Designing, implementing, and evaluating categorization software is the goal of the suggested framework for discerning various brain tumor types on magnetic resonance imaging (MRI) using textural features. This thesis introduces a brain tumor detection support system that involves the use of a variety of tumor classifiers. The system is designed as a decision fusion framework that enables these multi-classifier to analyze medical images, such as those obtained from magnetic resonance imaging (MRI). The fusion procedure is ground on the Dempster-Shafer evidence fusion theory. Numerous experimental scenarios have been implemented to validate the efficiency of the proposed framework. Compared with alternative approaches, the outcomes show that the methodology developed in this thesis demonstrates higher accuracy and higher computational efficiency

Machine learning based computational models with permeability for white matter microstructure imaging

Characterising tissue microstructure is of paramount importance for understanding neurological conditions such as Multiple Sclerosis. Therefore, there is a growing interest in imaging tissue microstructure non-invasively. One way to achieve this is by developing tissue models and fitting them to the diffusion-MRI signal. Nevertheless, some microstructure parameters, such as permeability, remain elusive because analytical models that incorporate them are intractable. Machine learning based computational models offer a promising alternative as they bypass the need for analytical expressions. The aim of this thesis is to develop the first machine learning based computational model for white matter microstructure imaging using two promising approaches: random forests and neural networks. To test the feasibility of this new approach, we provide for the first time a direct comparison of machine learning parameter estimates with histology. In this thesis, we demonstrate the idea by estimating permeability via the intra-axonal exchange time τ_i, a potential imaging biomarker for demyelinating pathologies. We use simulations of the diffusion-MRI signal to construct a mapping between signals and microstructure parameters including τ_i. We show for the first time that clinically viable diffusion-weighted sequences can probe exchange times up to approximately 1000 ms. Using healthy in-vivo human and mouse data, we show that our model's estimates are within the plausible range for white matter tissue and display well known trends such as the high-low-high intra-axonal volume fraction f across the corpus callosum. Using human and mouse data from demyelinated tissue, we show that our model detects trends in line with the expected MS pathology: a significant decrease in f and τ_i. Moreover, we show that our random forest estimates of f and τ_i correlate very strongly with histological measurements of f and myelin thickness. This thesis demonstrates that machine learning based computational models are a feasible approach for white matter microstructure imaging. The continually improving SNR in the clinical scanners and the availability of more realistic simulations open up possibilities of using such models as imaging biomarkers for demyelinating diseases such as Multiple Sclerosis

Modelos de procesamiento de la información en el cerebro aplicados a Sistemas Conexionistas: Redes NeuroGliales Artificiales y Deep Learning

Programa Oficial de Doutoramento en Tecnoloxías da Información e as Comunicacións. 5032V01[Resumen] En el campo de la Inteligencia Artificial, los sistemas conexionistas se han inspirado en las neuronas ya que, según la visión clásica de la Neurociencia, eran las únicas células con capacidad para procesar la información. Descubrimientos recientes de Neurociencia han demostrado que las células gliales tienen un papel clave en el procesamiento de la información en el cerebro. Basándose en estos descubrimientos se han desarrollado las Redes NeuroGliales Artificiales (RNGA) que cuentan con dos tipos de elementos de procesado, neuronas y astrocitos. En esta tesis se ha continuado con esta línea de investigación multidisciplinar que combina la Neurociencia y la Inteligencia Artificial. Para ello, se ha desarrollado un nuevo comportamiento de los astrocitos que actúan sobre la salida de las neuronas en las RNGA. Se ha realizado una comparación con las Redes de Neuronas Artificiales (RNA) en cinco problemas de clasificación y se ha demostrado que el nuevo comportamiento de los astrocitos mejora de manera significativa los resultados. Tras demostrar la capacidad de los astrocitos para procesar la información, en esta tesis se ha desarrollado además una nueva metodología que permite por primera vez la creación de redes Deep Learning conteniendo miles de neuronas y astrocitos, denominadas Deep Neuron-Astrocyte Networks (DANAN). Tras probarlas en un problema de regresión, las DANAN obtienen mejores resultados que las RNA. Esto permitirá evaluar comportamientos más complejos de los astrocitos en las redes de Deep Learning, pudiendo incluso crearse redes de astrocitos en un futuro próximo.[Resumo] No campo da Intelixencia Artificial, os sistemas conexionistas inspiráronse nas neuronas xa que, segundo a visión clásica da Neuronciencia, eran as únicas células con capacidade para procesar a información. Descubrimentos recentes de Neurociencia demostraron que as células gliais teñen un papel crave no procesamento da información no cerebro. Baseándose nestes descubrimentos desenvolvéronse as Redes NeuroGliales Artificiais (RNGA) que contan con dous tipos de elementos de procesado, neuronas e astrocitos. Nesta tese continuouse con esta liña de investigación multidisciplinar que combina a Neurociencia e a Intelixencia Artificial. Para iso, desenvolveuse un novo comportamento dos astrocitos que actúan sobre a saída das neuronas nas RNGA. Realizouse unha comparación coas Redes de Neuronas Artificiais (RNA) en cinco problemas de clasificación e demostrouse que o novo comportamento dos astrocitos mellora de xeito significativo os resultados. Tras demostrar a capacidade dos astrocitos para procesar a información, nesta tese desenvolveuse ademais unha nova metodoloxía que permite por primeira vez a creación de redes Deep Learning contendo miles de neuronas e astrocitos, denominadas Deep Neuron-Astrocyte Networks (DANAN). Tras probalas nun problema de regresión, as DANAN obteñen mellores resultados cas RNA. Isto permitirá avaliar comportamentos máis complexos dos astrocitos nas redes de Deep Learning, podendo ata crearse redes de astrocitos nun futuro próximo.[Abstract] In the field of Artificial Intelligence, connectionist systems have been inspired by neurons and, according to the classical view of neuroscience, they were the only cells capable of processing information. The latest advances in Neuroscience have shown that glial cells have a key role in the processing of information in the brain. Based on these discoveries, Artificial NeuroGlial Networks (RNGA) have been developed, which have two types of processing elements, neurons and astrocytes. In this thesis, this line of multidisciplinary research that combines Neuroscience and Artificial Intelligence has been continued. For this goal, a new behavior of the astrocytes that act on the output of the neurons in the RNGA has been developed. A comparison has been made with the Artificial Neuron Networks (ANN) in five classification problems and it has been demonstrated that the new behavior of the astrocytes significantly improves the results. After prove the capacity of astrocytes for information processing, in this thesis has been developed a new methodology that allows for the first time the creation of Deep Learning networks containing thousands of neurons and astrocytes, called Deep Neuron-Astrocyte Networks (DANAN). After testing them in a regression problem, the DANAN obtain better results than ANN. This allows testing more complexes astrocyte behaviors in Deep Learning networks, and even creates astrocyte networks in the near future