System on chip design of the nerve centres of the human neuroregulatory system

Introducción: El sistema neurorregulador humano es un sistema nervioso complejo compuesto por un grupo heterogéneo de centros nerviosos distribuidos a lo largo de la médula espinal. Estos centros actúan de forma autónoma, se comunican mediante interconexiones nerviosas y gobiernan y regulan el comportamiento de órganos en los seres humanos. Por más de 20 años se viene estudiando el sistema neurorregulador del tracto urinario inferior, responsable de los órganos y sistemas que intervienen en el proceso de micción. El objetivo de la investigación ha sido comprender el papel individual de cada centro para crear un modelo general del sistema neurorregulador capaz de operar a nivel de centro nervioso. Métodos: El modelo creado se ha formalizado mediante la teoría de sistemas multiagente de forma que cada agente modele el comportamiento de un centro nervioso. Su granularidad ha abierto la posibilidad de actuar a nivel de centro, lo cual ha sido especialmente interesante en el tratamiento de disfunciones. Resultados y discusión: En este trabajo se enriqueció este modelo teórico con un modelo arquitectural que lo hiciera adecuado para su implementación en hardware. A partir del nuevo modelo, se propuso el diseño system on chip de un procesador específico capaz de desempeñar las funciones de un centro nervioso. En conclusión, la investigación supuso un enfoque original con el objetivo final de crear un chip parametrizable, capaz de desarrollar cualquier función neurorreguladora, que pudiera ser implantable en el cuerpo y con capacidad para trabajar de forma coordinada con el sistema neurorregulador biológico.Introduction: The human neuroregulatory system is a complex nervous system composed of a heterogeneous group of nerve centres distributed along the spinal cord. These centres act autonomously, communicate through neural interconnections, and govern and regulate the behavior of organs in humans. For more than twenty years, the neuroregulatory system of the lower urinary tract has been studied, which controls the organs and systems involved in the urination process. Based on the study of the behavior and composition of the lower urinary tract, we have succeeded in isolating the centres involved in its functioning. The goal has been to understand the individual role played by each centre to create a general model of the neuroregulatory system capable of operating at the level of the nerve centre. Methods: The model has been created and formalized based on Multi-Agent Systems theory: each agent thus models the behaviour of a nerve centre. Its granularity opens up the possibility of acting at the level of the centre, of particular interest to treat dysfunctions. Results and discussion: The present study enriches this theoretical model with an architectural model that makes it suitable to implement in hardware. Based on this new model, we propose a System on Chip (SoC) design of a specific processor capable of performing a nerve centre’s functions. Although this processor can be entirely configured and programmed to adjust to the functioning of the different centres, the present work aimed at facilitating the understanding and validation of the proposal. We thus focused on the cortical-diencephalic centre, responsible for voluntary micturition. As conclusions, the research adopted an original approach with the aim of creating a configurable chip, capable of developing any neuroregulatory function, implantable in the body and being able to function in a coordinated way with the biological neuroregulatory system

Field programmable gate array based sigmoid function implementation using differential lookup table and second order nonlinear function

Artificial neural network (ANN) is an established artificial intelligence technique that is widely used for solving numerous problems such as classification and clustering in various fields. However, the major problem with ANN is a factor of time. ANN takes a longer time to execute a huge number of neurons. In order to overcome this, ANN is implemented into hardware namely field-programmable-gate-array (FPGA). However, implementing the ANN into a field-programmable gate array (FPGA) has led to a new problem related to the sigmoid function implementation. Often used as the activation function for ANN, a sigmoid function cannot be directly implemented in FPGA. Owing to its accuracy, the lookup table (LUT) has always been used to implement the sigmoid function in FPGA. In this case, obtaining the high accuracy of LUT is expensive particularly in terms of its memory requirements in FPGA. Second-order nonlinear function (SONF) is an appealing replacement for LUT due to its small memory requirement. Although there is a trade-off between accuracy and memory size. Taking the advantage of the aforementioned approaches, this thesis proposed a combination of SONF and a modified LUT namely differential lookup table (dLUT). The deviation values between SONF and sigmoid function are used to create the dLUT. SONF is used as the first step to approximate the sigmoid function. Then it is followed by adding or deducting with the value that has been stored in the dLUT as a second step as demonstrated via simulation. This combination has successfully reduced the deviation value. The reduction value is significant as compared to previous implementations such as SONF, and LUT itself. Further simulation has been carried out to evaluate the accuracy of the ANN in detecting the object in an indoor environment by using the proposed method as a sigmoid function. The result has proven that the proposed method has produced the output almost as accurately as software implementation in detecting the target in indoor positioning problems. Therefore, the proposed method can be applied in any field that demands higher processing and high accuracy in sigmoid function outpu