The neural engine: a reprogrammable low power platform for closed-loop optogenetics

Brain-machine Interfaces (BMI) hold great potential for treating neurological disorders such as epilepsy. Technological progress is allowing for a shift from open-loop, pacemaker-class, intervention towards fully closed-loop neural control systems. Low power programmable processing systems are therefore required which can operate within the thermal window of 2° C for medical implants and maintain long battery life. In this work, we developed a low power neural engine with an optimized set of algorithms which can operate under a power cycling domain. By integrating with custom designed brain implant chip, we have demonstrated the operational applicability to the closed-loop modulating neural activities in in-vitro brain tissues: the local field potentials can be modulated at required central frequency ranges. Also, both a freely-moving non-human primate (24-hour) and a rodent (1-hour) in-vivo experiments were performed to show system long-term recording performance. The overall system consumes only 2.93mA during operation with a biological recording frequency 50Hz sampling rate (the lifespan is approximately 56 hours). A library of algorithms has been implemented in terms of detection, suppression and optical intervention to allow for exploratory applications in different neurological disorders. Thermal experiments demonstrated that operation creates minimal heating as well as battery performance exceeding 24 hours on a freely moving rodent. Therefore, this technology shows great capabilities for both neuroscience in-vitro/in-vivo applications and medical implantable processing units

Outan: An On-Head System for Driving micro-LED Arrays Implanted in Freely Moving Mice

In the intact brain, neural activity can be recorded using sensing electrodes and manipulated using light stimulation. Silicon probes with integrated electrodes and micro-LEDs enable the detection and control of neural activity using a single implanted device. Miniaturized solutions for recordings from small freely moving animals are commercially available, but stimulation is driven by large, stationary current sources. We designed and fabricated a current source chip and integrated it into a headstage PCB that weighs 1.37 g. The proposed system provides 10-bit resolution current control for 32 channels, driving micro-LEDs with up to 4.6 V and sourcing up to 0.9 mA at a refresh rate of 5 kHz per channel. When calibrated against a micro-LED probe, the system allows linear control of light output power, up to 10 micro-W per micro-LED. To demonstrate the capabilities of the system, synthetic sequences of neural spiking activity were produced by driving multiple micro-LEDs implanted in the hippocampal CA1 area of a freely moving mouse. The high spatial, temporal, and amplitude resolution of the system provides a rich variety of stimulation patterns. Combined with commercially available sampling headstages, the system provides an easy to use back-end, fully utilizing the bi-directional potential of integrated opto-electronic arrays.Comment: 11 pages, 9 figure

SenseBack - An implantable system for bidirectional neural interfacing

Chronic in-vivo neurophysiology experiments require highly miniaturized, remotely powered multi-channel neural interfaces which are currently lacking in power or flexibility post implantation. In this article, to resolve this problem we present the SenseBack system, a post-implantation reprogrammable wireless 32-channel bidirectional neural interfacing that can enable chronic peripheral electrophysiology experiments in freely behaving small animals. The large number of channels for a peripheral neural interface, coupled with fully implantable hardware and complete software flexibility enable complex in-vivo studies where the system can adapt to evolving study needs as they arise. In complementary ex-vivo and in-vivo preparations, we demonstrate that this system can record neural signals and perform high-voltage, bipolar stimulation on any channel. In addition, we demonstrate transcutaneous power delivery and Bluetooth 5 data communication with a PC. The SenseBack system is capable of stimulation on any channel with ±20 V of compliance and up to 315 μA of current, and highly configurable recording with per-channel adjustable gain and filtering with 8 sets of 10-bit ADCs to sample data at 20 kHz for each channel. To the best of our knowledge this is the first such implantable research platform offering this level of performance and flexibility post-implantation (including complete reprogramming even after encapsulation) for small animal electrophysiology. Here we present initial acute trials, demonstrations and progress towards a system that we expect to enable a wide range of electrophysiology experiments in freely behaving animals

Optogenetics and Computer Vision for C. elegans Neuroscience and Other Biophysical Applications

This work presents optogenetics and real-time computer vision techniques to non-invasively manipulate and monitor neural activity with high spatiotemporal resolution in awake behaving Caenorhabditis elegans. These methods were employed to dissect the nematode's mechanosensory and motor circuits and to elucidate the neural control of wave propagation during forward locomotion. Additionally, similar computer vision methods were used to automatically detect and decode fluorescing DNA origami nanobarcodes, a new class of fluorescent reporter constructs

Modulation of REI silencing transcription factor REST via DNA and RNA strategies

Modulation of REI silencing transcription factor REST via DNA and RNA strategie

Silicon Neural Probes for Stimulation of Neurons and the Excitation and Detection of Proteins in the Brain

This thesis describes the development of a number of novel microfabricated neural probes for a variety of specific neuroscience applications. These devices rely on single mode waveguides and grating couplers constructed from silicon nitride thin films, which allows the use of planar lightwave circuits to create advanced device geometries and functions. These probes utilize array waveguide gratings to select an individual emitter from a large array of emitters using the wavelength of incoming light, allowing for spatial multiplexing of optical stimulation. These devices were tested in the laboratory and in living tissue to verify their efficacy. This technology was then modified to create steerable beam forming for stimulation of neurons using optical phase arrays. This technology was also tested for use in fluoresence lifetime imaging microscopy and the first application of pulsed light through the photonic circuits. Finally, this technology was again modified to create laminar illumination patterns for light sheet fluorescence microscopy applications. These devices were further improved by adding embedded microfluidics to the probes. The process of creating embedded microfluidic channels by the dig and seal method is described in detail, including modifications to the procedure that were added to address potential pitfalls in the fabrication process. Next, two projects which combine microfluidics with the optical devices described in the previous chapter are detailed. One project involves combining the use of optical emitters with microfluidic injections containing caged neurotransmitters to stimulate neurons is described. The other project involves microfluidic sampling of the extracellular space for neuropeptides which are detected using ring resonator biosensors. The sensitivity of these biosensors was analyzed in detail, determining both the physical limit of detection and the effect of biological noise due to non-specific binding on the sensors

Antennas and Electromagnetics Research via Natural Language Processing.

Advanced techniques for performing natural language processing (NLP) are being utilised to devise a pioneering methodology for collecting and analysing data derived from scientific literature. Despite significant advancements in automated database generation and analysis within the domains of material chemistry and physics, the implementation of NLP techniques in the realms of metamaterial discovery, antenna design, and wireless communications remains at its early stages. This thesis proposes several novel approaches to advance research in material science. Firstly, an NLP method has been developed to automatically extract keywords from large-scale unstructured texts in the area of metamaterial research. This enables the uncovering of trends and relationships between keywords, facilitating the establishment of future research directions. Additionally, a trained neural network model based on the encoder-decoder Long Short-Term Memory (LSTM) architecture has been developed to predict future research directions and provide insights into the influence of metamaterials research. This model lays the groundwork for developing a research roadmap of metamaterials. Furthermore, a novel weighting system has been designed to evaluate article attributes in antenna and propagation research, enabling more accurate assessments of impact of each scientific publication. This approach goes beyond conventional numeric metrics to produce more meaningful predictions. Secondly, a framework has been proposed to leverage text summarisation, one of the primary NLP tasks, to enhance the quality of scientific reviews. It has been applied to review recent development of antennas and propagation for body-centric wireless communications, and the validation has been made available for comparison with well-referenced datasets for text summarisation. Lastly, the effectiveness of automated database building in the domain of tunable materials and their properties has been presented. The collected database will use as an input for training a surrogate machine learning model in an iterative active learning cycle. This model will be utilised to facilitate high-throughput material processing, with the ultimate goal of discovering novel materials exhibiting high tunability. The approaches proposed in this thesis will help to accelerate the discovery of new materials and enhance their applications in antennas, which has the potential to transform electromagnetic material research

Chromatin and Epigenetics

Genomics has gathered broad public attention since Lamarck put forward his top-down hypothesis of 'motivated change' in 1809 in his famous book "Philosophie Zoologique" and even more so since Darwin published his famous bottom-up theory of natural selection in "The Origin of Species" in 1859. The public awareness culminated in the much anticipated race to decipher the sequence of the human genome in 2002. Over all those years, it has become apparent that genomic DNA is compacted into chromatin with a dedicated 3D higher-order organization and dynamics, and that on each structural level epigenetic modifications exist. The book "Chromatin and Epigenetics" addresses current issues in the fields of epigenetics and chromatin ranging from more theoretical overviews in the first four chapters to much more detailed methodologies and insights into diagnostics and treatments in the following chapters. The chapters illustrate in their depth and breadth that genetic information is stored on all structural and dynamical levels within the nucleus with corresponding modifications of functional relevance. Thus, only an integrative systems approach allows to understand, treat, and manipulate the holistic interplay of genotype and phenotype creating functional genomes. The book chapters therefore contribute to this general perspective, not only opening opportunities for a true universal view on genetic information but also being key for a general understanding of genomes, their function, as well as life and evolution in general