Medical image enhancement

Each image acquired from a medical imaging system is often part of a two-dimensional (2-D) image set whose total presents a three-dimensional (3-D) object for diagnosis. Unfortunately, sometimes these images are of poor quality. These distortions cause an inadequate object-of-interest presentation, which can result in inaccurate image analysis. Blurring is considered a serious problem. Therefore, “deblurring” an image to obtain better quality is an important issue in medical image processing. In our research, the image is initially decomposed. Contrast improvement is achieved by modifying the coefficients obtained from the decomposed image. Small coefficient values represent subtle details and are amplified to improve the visibility of the corresponding details. The stronger image density variations make a major contribution to the overall dynamic range, and have large coefficient values. These values can be reduced without much information loss

Algorithms for VLSI stereo vision circuits applied to autonomous robots

Since the inception of Robotics, visual information has been incorporated in order to allow the robots to perform tasks that require an interaction with their environment, particularly when it is a changing environment. Depth perception is a most useful information for a mobile robot to navigate in its environment and interact with its surroundings. Among the different methods capable of measuring the distance to the objects in the scene, stereo vision is the most advantageous for a small, mobile robot with limited energy and computational power. Stereoscopy implies a low power consumption because it uses passive sensors and it does not require the robot to move. Furthermore, it is more robust, because it does not require a complex optic system with moving elements. On the other hand, stereo vision is computationally intensive. Objects in the scene have to be detected and matched across images. Biological sensory systems are based on simple computational elements that process information in parallel and communicate among them. Analog VLSI chips are an ideal substrate to mimic the massive parallelism and collective computation present in biological nervous systems. For mobile robotics they have the added advantage of low power consumption and high computational power, thus freeing the CPU for other tasks. This dissertation discusses two stereoscopic methods that are based on simple, parallel cal- culations requiring communication only among neighboring processing units (local communication). Algorithms with these properties are easy to implement in analog VLSI and they are also very convenient for digital systems. The first algorithm is phase-based. Disparity, i.e., the spatial shift between left and right images, is recovered as a phase shift in the spatial-frequency domain. Gábor functions are used to recover the frequency spectrum of the image because of their optimum joint spatial and spatial-frequency properties. The Gábor-based algorithm is discussed and tested on a Khepera miniature mobile robot. Two further approximations are introduced to ease the analog VLSI and digital implementations. The second stereoscopic algorithm is difference-based. Disparity is recovered by a simple calculation using the image differences and their spatial derivatives. The algorithm is simulated on a digital system and an analog VLSI implementation is proposed and discussed. The thesis concludes with the description of some tools used in this research project. A stereo vision system has been developed for the Webots mobile robotics simulator, to simplify the testing of different stereo algorithms. Similarly, two stereo vision turrets have been built for the Khepera robot

Closed-loop approaches for innovative neuroprostheses

The goal of this thesis is to study new ways to interact with the nervous system in case of damage or pathology. In particular, I focused my effort towards the development of innovative, closed-loop stimulation protocols in various scenarios: in vitro, ex vivo, in vivo

Mathematics and Digital Signal Processing

Modern computer technology has opened up new opportunities for the development of digital signal processing methods. The applications of digital signal processing have expanded significantly and today include audio and speech processing, sonar, radar, and other sensor array processing, spectral density estimation, statistical signal processing, digital image processing, signal processing for telecommunications, control systems, biomedical engineering, and seismology, among others. This Special Issue is aimed at wide coverage of the problems of digital signal processing, from mathematical modeling to the implementation of problem-oriented systems. The basis of digital signal processing is digital filtering. Wavelet analysis implements multiscale signal processing and is used to solve applied problems of de-noising and compression. Processing of visual information, including image and video processing and pattern recognition, is actively used in robotic systems and industrial processes control today. Improving digital signal processing circuits and developing new signal processing systems can improve the technical characteristics of many digital devices. The development of new methods of artificial intelligence, including artificial neural networks and brain-computer interfaces, opens up new prospects for the creation of smart technology. This Special Issue contains the latest technological developments in mathematics and digital signal processing. The stated results are of interest to researchers in the field of applied mathematics and developers of modern digital signal processing systems

A Neuromorphic Prosthesis to Restore Communication in Neuronal Networks

Recent advances in bioelectronics and neural engineering allowed the development of brain machine interfaces and neuroprostheses, capable of facilitating or recovering functionality in people with neurological disability. To realize energy-efficient and real-time capable devices, neuromorphic computing systems are envisaged as the core of next-generation systems for brain repair. We demonstrate here a real-time hardware neuromorphic prosthesis to restore bidirectional interactions between two neuronal populations, even when one is damaged or missing. We used in vitro modular cell cultures to mimic the mutual interaction between neuronal assemblies and created a focal lesion to functionally disconnect the two populations. Then, we employed our neuromorphic prosthesis for bidirectional bridging to artificially reconnect two disconnected neuronal modules and for hybrid bidirectional bridging to replace the activity of one module with a real-time hardware neuromorphic Spiking Neural Network. Our neuroprosthetic system opens avenues for the exploitation of neuromorphic-based devices in bioelectrical therapeutics for health care

Quantifying activity in nascent neuronal networks derived from embryonic stem cells

PhD ThesisThe relationship between spatiotemporal patterns of spontaneous activity and functional specialisation in developing neuronal networks is complex and its study is crucial to our understanding of how network communication is initiated. This project quantifies transitions between structural and functional states in embryonic stem cell cultures during differentiation. The work also focussed on the role of γ-aminobutyric acid (GABA), known to be vital for neuronal network development. The work used many techniques, including carbon nanotube (CNT) -patterned substrates to manipulate network architecture, multi-electrode arrays (MEAs) and calcium imaging to quantify function. An embryonic stem cell line (CC9) was used to generate ‘de novo’ neuronal networks and these were monitored over 13 – 22 days in vitro (DIV), while network structure forms and stabilizes. On CNT-patterned arrays, differentiating CC9s migrated and sub-clustered on CNT islands showing that network structure could be manipulated. No spontaneous electrophysiological (unit) activity was found in these cultures. However, intracellular calcium responses were readily induced and seen spontaneously at 13-20 DIV. Activity rate, kinetics and number of active cells increased between 16-18 DIV, correlating with changes in network clustering. Post 17 DIV, activity transformed from near-random to periodic and synchronous. Many events were initiated by ‘hubs’ and degrees of critical behaviour were observed, moving towards more efficient information processing states with development. Blockade of GABAA receptors lead to elevated spontaneous activity and supercritical behaviour, depending on developmental stage. Application of exogenous GABA induced large, slow calcium transients in a developmental stage-dependent manner, suggestive of a mixed excitatory/inhibitory role. These findings begin to show how activity develops as stem cells differentiate to form neuronal networks. GABA’s role in controlling patterns of activity was more complex that previously reported for neuronal networks in situ, but GABA clearly played a vital role in shaping population behaviour to optimise information processing properties in early, developing networks

A Closed-Loop Bidirectional Brain-Machine Interface System For Freely Behaving Animals

A brain-machine interface (BMI) creates an artificial pathway between the brain and the external world. The research and applications of BMI have received enormous attention among the scientific community as well as the public in the past decade. However, most research of BMI relies on experiments with tethered or sedated animals, using rack-mount equipment, which significantly restricts the experimental methods and paradigms. Moreover, most research to date has focused on neural signal recording or decoding in an open-loop method. Although the use of a closed-loop, wireless BMI is critical to the success of an extensive range of neuroscience research, it is an approach yet to be widely used, with the electronics design being one of the major bottlenecks. The key goal of this research is to address the design challenges of a closed-loop, bidirectional BMI by providing innovative solutions from the neuron-electronics interface up to the system level. Circuit design innovations have been proposed in the neural recording front-end, the neural feature extraction module, and the neural stimulator. Practical design issues of the bidirectional neural interface, the closed-loop controller and the overall system integration have been carefully studied and discussed.To the best of our knowledge, this work presents the first reported portable system to provide all required hardware for a closed-loop sensorimotor neural interface, the first wireless sensory encoding experiment conducted in freely swimming animals, and the first bidirectional study of the hippocampal field potentials in freely behaving animals from sedation to sleep. This thesis gives a comprehensive survey of bidirectional BMI designs, reviews the key design trade-offs in neural recorders and stimulators, and summarizes neural features and mechanisms for a successful closed-loop operation. The circuit and system design details are presented with bench testing and animal experimental results. The methods, circuit techniques, system topology, and experimental paradigms proposed in this work can be used in a wide range of relevant neurophysiology research and neuroprosthetic development, especially in experiments using freely behaving animals

Analysis of resting-state neurovascular coupling and locomotion-associated neural dynamics using wide-field optical mapping

Understanding the relationship between neural activity and cortical hemodynamics, or neurovascular coupling is the foundation to interpret neuroimaging signals such as functional magnetic resonance imaging (fMRI) which measure local changes in hemodynamics as a proxy for underlying neural activity. Even though the stereotypical stimulus-evoked hemodynamic response pattern with increased concentration of oxy- and total-hemoglobin and decrease in concentration of deoxy-hemoglobin has been well-recognized, the linearity of neurovascular coupling and its variances depending on brain state and tasks haven’t been thoroughly evaluated. To directly assess the cortical neurovascular coupling, simultaneous recordings of neural and hemodynamic activity were imaged by wide-field optical mapping (WFOM) over the bilateral dorsal surface of the mouse brain through a bilateral thinned-skull cranial window. Neural imaging is achieved through wide-field fluorescence imaging in animals expressing genetically encoded calcium sensor (Thy1-GCaMP). Hemodynamics are recorded via simultaneous imaging of multi-spectral reflectance. Significant hemodynamic crosstalk was found in the detected fluorescence signal and the physical model of the contamination, methods of correction as well as electrophysiological verification are presented. A linear model between neural and hemodynamic signals was used to fit spatiotemporal hemodynamics can be predicted by convolving local fluorescence changes with hemodynamic response functions derived through both deconvolution and gamma-variate fitting. Beyond confirming that the resting-state hemodynamics in the awake and anesthetized brain are coupled to underlying neural activity, the patterns of bilaterally symmetric spontaneous neural activity observed by WFOM emulate the functionally connected networks detected by fMRI. This result provides reassurance that resting-state functional connectivity has neural origins. With the access to cortical neural activity at mesoscopic level, we further explore the cortical neural representations preceding and during spontaneous locomotion

Materials and neuroscience: validating tools for large-scale, high-density neural recording

Extracellular recording remains the only technique capable of measuring the activity of many neurons simultaneously with a sub-millisecond precision, in multiple brain areas, including deep structures. Nevertheless, many questions about the nature of the detected signal and the limitations/capabilities of this technique remain unanswered. The general goal of this work is to apply the methodology and concepts of materials science to answer some of the major questions surrounding extracellular recording, and thus take full advantage of this seminal technique. We start out by quantifying the effect of electrode impedance on the amplitude of measured extracellular spikes and background noise. Can we improve data quality by lowering electrode impedance? We demonstrate that if the proper recording system is used, then the impedance of a microelectrode, within the range typical of standard polytrodes (~ 0.1 to 2 MΩ), does not significantly affect a neural spike amplitude or the background noise, and therefore spike sorting. In addition to improving the performance of each electrode, increasing the number of electrodes in a single neural probe has also proven advantageous for simultaneously monitoring the activity of more neurons with better spatiotemporal resolution. How can we achieve large-scale, highdensity extracellular recordings without compromising brain tissue? Here we report the design and in vivo validation of a complementary metal–oxide–semiconductor (CMOS)-based scanning probe with 1356 electrodes arranged along approximately 8 mm of a thin shaft (50 μm thick and 100 μm wide). Additionally, given the ever-shrinking dimensions of CMOS technology, there is a drive to fabricate sub-cellular electrodes (< 10 μm). Therefore, to evaluate electrode configurations for future probe designs, several recordings from many different brain regions were performed with an ultra-dense probe containing 255 electrodes, each with a geometric area of 5 x 5 μm and a pitch of 6 μm. How can we validate neural probes with different electrode materials/configurations and different sorting algorithms? We describe a new procedure for precisely aligning two probes for in vivo “paired-recordings” such that the spiking activity of a single neuron is monitored with both a dense extracellular silicon polytrode and a juxtacellular micro-pipette. We gathered a dataset of paired-recordings, which is available online. The “ground truth” data, for which one knows exactly when a neuron in the vicinity of an extracellular probe generates an action potential, has been used for several groups to validate and quantify the performance of new algorithms to automatically detect/sort single-units