Physiologically-Based Vision Modeling Applications and Gradient Descent-Based Parameter Adaptation of Pulse Coupled Neural Networks

In this research, pulse coupled neural networks (PCNNs) are analyzed and evaluated for use in primate vision modeling. An adaptive PCNN is developed that automatically sets near-optimal parameter values to achieve a desired output. For vision modeling, a physiologically motivated vision model is developed from current theoretical and experimental biological data. The biological vision processing principles used in this model, such as spatial frequency filtering, competitive feature selection, multiple processing paths, and state dependent modulation are analyzed and implemented to create a PCNN based feature extraction network. This network extracts luminance, orientation, pitch, wavelength, and motion, and can be cascaded to extract texture, acceleration and other higher order visual features. Theorized and experimentally confirmed cortical information linking schemes, such as state dependent modulation and temporal synchronization are used to develop a PCNN-based visual information fusion network. The network is used to fuse the results of several object detection systems for the purpose of enhanced object detection accuracy. On actual mammograms and FLIR images, the network achieves an accuracy superior to any of the individual object detection systems it fused. Last, this research develops the first fully adaptive PCNN. Given only an input and a desired output, the adaptive PCNN will find all parameter values necessary to approximate that desired output

Temporal Influence on Awareness

Grossberg\u27s Motion Oriented Contrast Filter (MOC) was extensively analyzed (7). The output from the filter\u27s global motion neuronal layer was compared to a noncausal post-processing filter developed by AFIT. Both filters were shown to incorporate a weighted, noncausal temporal range of input data in processed output. The global motion framework was then implemented using a physiologically motivated pulsed neural model - the Pulse Coupled Neural Network (PCNN). By incorporating both spatial and temporal data, the PCNN was shown to exhibit a common visual illusion, apparent motion. The existence of a physiological temporal processing range was further investigated through implementation of two multi-modal experiments which integrated visual and auditory stimulus input channels. Results from the first experiment reinforce earlier findings from literature of a temporal window for perception of simultaneous activity. (Events occurring within this window are considered simultaneous; events which span more than one window are considered temporally separate.) Data collected from the second experiment suggests future inputs from an accessory auditory stimulus impact current perception of a visual stimulus. The influence of the auditory accessory stimulus decreases as the temporal delay between visual and auditory stimulus presentation is increased up to a maximum value of approximately 40 milliseconds. These tests results suggest the existence of perceptual noncausality in the mind - awareness as a function of past, current, and future perceptual inputs

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Stochastic resonance and finite resolution in a network of leaky integrate-and-fire neurons.

This thesis is a study of stochastic resonance (SR) in a discrete implementation of a leaky integrate-and-fire (LIF) neuron network. The aim was to determine if SR can be realised in limited precision discrete systems implemented on digital hardware. How neuronal modelling connects with SR is discussed. Analysis techniques for noisy spike trains are described, ranging from rate coding, statistical measures, and signal processing measures like power spectrum and signal-to-noise ratio (SNR). The main problem in computing spike train power spectra is how to get equi-spaced sample amplitudes given the short duration of spikes relative to their frequency. Three different methods of computing the SNR of a spike train given its power spectrum are described. The main problem is how to separate the power at the frequencies of interest from the noise power as the spike train encodes both noise and the signal of interest. Two models of the LIF neuron were developed, one continuous and one discrete, and the results compared. The discrete model allowed variation of the precision of the simulation values allowing investigation of the effect of precision limitation on SR. The main difference between the two models lies in the evolution of the membrane potential. When both models are allowed to decay from a high start value in the absence of input, the discrete model does not completely discharge while the continuous model discharges to almost zero. The results of simulating the discrete model on an FPGA and the continuous model on a PC showed that SR can be realised in discrete low resolution digital systems. SR was found to be sensitive to the precision of the values in the simulations. For a single neuron, we find that SR increases between 10 bits and 12 bits resolution after which it saturates. For a feed-forward network with multiple input neurons and one output neuron, SR is stronger with more than 6 input neurons and it saturates at a higher resolution. We conclude that stochastic resonance can manifest in discrete systems though to a lesser extent compared to continuous systems

Systems engineering approaches to safety in transport systems

openDuring driving, driver behavior monitoring may provide useful information to prevent road traffic accidents caused by driver distraction. It has been shown that 90% of road traffic accidents are due to human error and in 75% of these cases human error is the only cause. Car manufacturers have been interested in driver monitoring research for several years, aiming to enhance the general knowledge of driver behavior and to evaluate the functional state as it may drastically influence driving safety by distraction, fatigue, mental workload and attention. Fatigue and sleepiness at the wheel are well known risk factors for traffic accidents. The Human Factor (HF) plays a fundamental role in modern transport systems. Drivers and transport operators control a vehicle towards its destination in according to their own sense, physical condition, experience and ability, and safety strongly relies on the HF which has to take the right decisions. On the other hand, we are experiencing a gradual shift towards increasingly autonomous vehicles where HF still constitutes an important component, but may in fact become the "weakest link of the chain", requiring strong and effective training feedback. The studies that investigate the possibility to use biometrical or biophysical signals as data sources to evaluate the interaction between human brain activity and an electronic machine relate to the Human Machine Interface (HMI) framework. The HMI can acquire human signals to analyse the specific embedded structures and recognize the behavior of the subject during his/her interaction with the machine or with virtual interfaces as PCs or other communication systems. Based on my previous experience related to planning and monitoring of hazardous material transport, this work aims to create control models focused on driver behavior and changes of his/her physiological parameters. Three case studies have been considered using the interaction between an EEG system and external device, such as driving simulators or electronical components. A case study relates to the detection of the driver's behavior during a test driver. Another case study relates to the detection of driver's arm movements according to the data from the EEG during a driver test. The third case is the setting up of a Brain Computer Interface (BCI) model able to detect head movements in human participants by EEG signal and to control an electronic component according to the electrical brain activity due to head turning movements. Some videos showing the experimental results are available at https://www.youtube.com/channel/UCj55jjBwMTptBd2wcQMT2tg.openXXXIV CICLO - INFORMATICA E INGEGNERIA DEI SISTEMI/ COMPUTER SCIENCE AND SYSTEMS ENGINEERING - Ingegneria dei sistemiZero, Enric

DETECTING BRAIN-WIDE INTRINSIC CONNECTIVITY NETWORKS USING fMRI IN MICE

Functional neuroimaging methods in mice are essential for unraveling complex neuronal networks that underlie maladaptive behavior in neurological disorder models. By using fMRI to detect intrinsic connectivity networks in mice, we can examine large scale alteration in brain activity and functional connectivity to establish causal associations in brain network changes. The work presented in this dissertation is organized into five chapters. Chapter 1 provides the necessary background required to understand how functional neuroimaging tools such as fMRI detect signal changes attributed to spontaneous neuronal activity of intrinsic connectivity networks in mice. Chapter 2 describes the development of our isotropic fMRI acquisition sequence in mice and semi-automated pipeline for mouse fMRI data. Naïve mouse fMRI scans were used to validated the pipeline by reliably and reproducibly extracting intrinsic connectivity networks. Chapter 3 establishes the development and validation of a novel superparamagenetic iron-oxide nanoparticle to enhance fMRI signal sensitivity. Chapter 4 studies the effects norepinephrine released by locus coeruleus neurons on the default mode network in mice. Norepinephrine release selectively enhanced neuronal activity and connectivity in the Frontal module of the default mode network by suppressing information flow from the Retrosplenial-Hippocampal to the Association modules. Chapter 5 addresses the implications of our findings and addresses the limitations and future studies that can be conducted to expand on this research.Doctor of Philosoph

Intelligent Analysis of Cerebral Magnetic Resonance Images: Extracting Relevant Information from Small Datasets

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura : 21-09-2017Los metodos de machine learning aplicados a imagenes medicas se estan convirtiendo en potentes herramientas para el analisis y diagnostico de pacientes. La alta disponibilidad de repositorios de im agenes de diferentes modalidades ha favorecido el desarrollo de sistemas que aprenden a extraer caracteristicas relevantes y construyen modelos predictivos a partir de grandes cantidades de informacion, por ejemplo, los metodos de deep learning. Sin embargo, el analisis de conjuntos de imagenes provenientes de un menor numero de sujetos, como es el caso de las imagenes adquiridas en entornos de investigacion cl nica y pre-cl nica, ha recibido considerablemente menos atencion. El objetivo de esta tesis es implementar un conjunto de herramientas avanzadas para resolver este problema, permitiendo el analisis robusto de Im agenes de Resonancia Magn etica (MRI por sus siglas en ingl es) cuando se dispone de pocos sujetos de estudio. En este contexto, las herramientas propuestas se emplean para analizar de manera autom atica conjuntos de datos obtenidos de imagenes funcionales de MR del cerebro en estudios de regulacion del apetito en roedores y humanos, y de im agenes funcionales y estructurales de MR de desarrollos tumorales en modelos animales y humanos. Los metodos propuestos se derivan de la idea de considerar cada voxel del conjunto de im agenes como un patron, en lugar de la nocion convencional de considerar cada imagen como un patr on. El Cap tulo 1 describe la motivaci on de esta tesis, incluyendo los objetivos propuestos, la estructura general del documento y las contribuciones de esta investigaci on. El Capitulo 2 contiene una introduccion actualizada del estado del arte en MRI, los procedimientos mas usados en el pre-procesamiento de imagenes, y los algoritmos de machine learning m as utiles y sus aplicaciones en MRI. El Cap tulo 3 presenta el dise~no experimental y los pasos de pre-procesamiento aplicados a los conjuntos de datos de regulaci on de apetito y desarrollo tumoral. El Capitulo 4 implementa nuevos metodos de aprendizaje supervisados para el analisis de conjuntos de datos de MRI obtenidos de un conjunto peque~no de sujetos. Se ilustra este enfoque presentando primero la metodolog a Fisher Maps, que permite la visualizaci on cuantitativa y no invasiva de la circuiter a cerebral del apetito, mediante el an alisis autom atico de Im agenes Ponderadas en Difusi on (DWI por sus siglas en ingl es). Esta metodolog a se extiende a la clasi caci on de im agenes completas combinando las predicciones obtenidas de cada p xel. El Cap tulo 5 propone un nuevo algoritmo de aprendizaje no supervisado, ilustrando su desempe~no sobre datos sint eticos y datos provenientes de estudios de tumores cerebrales y crecimiento tumoral. Por ultimo, en el Cap tulo 6 se resumen las principales conclusiones de este trabajo y se plantean amplias v as para su desarrollo futuro. En resumen, esta tesis presenta un nuevo enfoque capaz de trabajar en contextos con baja disponibilidad de sujetos de estudio, proponiendo algoritmos de aprendizaje supervisado y no supervisado. Estos metodos pueden ser facilmente generalizados a otros paradigmas o patologias, e incluso, a distintas modalidades de imagenes

An investigation into adaptive power reduction techniques for neural hardware

In light of the growing applicability of Artificial Neural Network (ANN) in the signal processing field [1] and the present thrust of the semiconductor industry towards lowpower SOCs for mobile devices [2], the power consumption of ANN hardware has become a very important implementation issue. Adaptability is a powerful and useful feature of neural networks. All current approaches for low-power ANN hardware techniques are ‘non-adaptive’ with respect to the power consumption of the network (i.e. power-reduction is not an objective of the adaptation/learning process). In the research work presented in this thesis, investigations on possible adaptive power reduction techniques have been carried out, which attempt to exploit the adaptability of neural networks in order to reduce the power consumption. Three separate approaches for such adaptive power reduction are proposed: adaptation of size, adaptation of network weights and adaptation of calculation precision. Initial case studies exhibit promising results with significantpower reduction