Noise Efficient Integrated Amplifier Designs for Biomedical Applications

The recording of neural signals with small monolithically integrated amplifiers is of high interest in research as well as in commercial applications, where it is common to acquire 100 or more channels in parallel. This paper reviews the recent developments in low-noise biomedical amplifier design based on CMOS technology, including lateral bipolar devices. Seven major circuit topology categories are identified and analyzed on a per-channel basis in terms of their noise-efficiency factor (NEF), input-referred absolute noise, current consumption, and area. A historical trend towards lower NEF is observed whilst absolute noise power and current consumption exhibit a widespread over more than five orders of magnitude. The performance of lateral bipolar transistors as amplifier input devices is examined by transistor-level simulations and measurements from five different prototype designs fabricated in 180 nm and 350 nm CMOS technology. The lowest measured noise floor is 9.9 nV/√Hz with a 10 µA bias current, which results in a NEF of 1.2

SpikeInterface, a unified framework for spike sorting

Much development has been directed toward improving the performance and automation of spike sorting. This continuous development, while essential, has contributed to an over-saturation of new, incompatible tools that hinders rigorous benchmarking and complicates reproducible analysis. To address these limitations, we developed SpikeInterface, a Python framework designed to unify preexisting spike sorting technologies into a single codebase and to facilitate straightforward comparison and adoption of different approaches. With a few lines of code, researchers can reproducibly run, compare, and benchmark most modern spike sorting algorithms; pre-process, post-process, and visualize extracellular datasets; validate, curate, and export sorting outputs; and more. In this paper, we provide an overview of SpikeInterface and, with applications to real and simulated datasets, demonstrate how it can be utilized to reduce the burden of manual curation and to more comprehensively benchmark automated spike sorters.ISSN:2050-084

Design of an Integrated CMOS Transceiver with Wireless Power and Data Telemetry with Application to Implantable Flexible Neural Probes

Recent developments in implantable medical devices (IMDs) have created a need for communication systems integrated directly into the implant with feedback data for various sensing systems. The need for modern communication techniques, power delivery systems, and usable interfaces for smart implants present an interesting challenge for engineers trying to provide doctors and medical professionals with the best resources available for medical research. This dissertation will cover the design of an integrated CMOS transceiver and near-field inductive link used for an IMD and the accompanying CMOS front end for the application space of neural recording in the brain of lab mice. The design process of the CMOS IC, along with thinning techniques, the nearfield inductive link, and the design of an external reading system will be discussed in detail. The various wireless power and data telemetry techniques applicable for IMDs and their strengths and weaknesses will also be described. Software techniques and implementation for real-time analysis of a high data rate communication system from the designed IMD will be covered. Finally, transceiver verification will be given for both power and data telemetry under various scenarios, with front end verification performed via controlled lab bench experiments using input sinusoidal wave forms

Scalable software and models for large-scale extracellular recordings

The brain represents information about the world through the electrical activity of populations of neurons. By placing an electrode near a neuron that is firing (spiking), it is possible to detect the resulting extracellular action potential (EAP) that is transmitted down an axon to other neurons. In this way, it is possible to monitor the communication of a group of neurons to uncover how they encode and transmit information. As the number of recorded neurons continues to increase, however, so do the data processing and analysis challenges. It is crucial that scalable software and analysis tools are developed and made available to the neuroscience community to keep up with the large amounts of data that are already being gathered. This thesis is composed of three pieces of work which I develop in order to better process and analyze large-scale extracellular recordings. My work spans all stages of extracellular analysis from the processing of raw electrical recordings to the development of statistical models to reveal underlying structure in neural population activity. In the first work, I focus on developing software to improve the comparison and adoption of different computational approaches for spike sorting. When analyzing neural recordings, most researchers are interested in the spiking activity of individual neurons, which must be extracted from the raw electrical traces through a process called spike sorting. Much development has been directed towards improving the performance and automation of spike sorting. This continuous development, while essential, has contributed to an over-saturation of new, incompatible tools that hinders rigorous benchmarking and complicates reproducible analysis. To address these limitations, I develop SpikeInterface, an open-source, Python framework designed to unify preexisting spike sorting technologies into a single toolkit and to facilitate straightforward benchmarking of different approaches. With this framework, I demonstrate that modern, automated spike sorters have low agreement when analyzing the same dataset, i.e. they find different numbers of neurons with different activity profiles; This result holds true for a variety of simulated and real datasets. Also, I demonstrate that utilizing a consensus-based approach to spike sorting, where the outputs of multiple spike sorters are combined, can dramatically reduce the number of falsely detected neurons. In the second work, I focus on developing an unsupervised machine learning approach for determining the source location of individually detected spikes that are recorded by high-density, microelectrode arrays. By localizing the source of individual spikes, my method is able to determine the approximate position of the recorded neuriii ons in relation to the microelectrode array. To allow my model to work with large-scale datasets, I utilize deep neural networks, a family of machine learning algorithms that can be trained to approximate complicated functions in a scalable fashion. I evaluate my method on both simulated and real extracellular datasets, demonstrating that it is more accurate than other commonly used methods. Also, I show that location estimates for individual spikes can be utilized to improve the efficiency and accuracy of spike sorting. After training, my method allows for localization of one million spikes in approximately 37 seconds on a TITAN X GPU, enabling real-time analysis of massive extracellular datasets. In my third and final presented work, I focus on developing an unsupervised machine learning model that can uncover patterns of activity from neural populations associated with a behaviour being performed. Specifically, I introduce Targeted Neural Dynamical Modelling (TNDM), a statistical model that jointly models the neural activity and any external behavioural variables. TNDM decomposes neural dynamics (i.e. temporal activity patterns) into behaviourally relevant and behaviourally irrelevant dynamics; the behaviourally relevant dynamics constitute all activity patterns required to generate the behaviour of interest while behaviourally irrelevant dynamics may be completely unrelated (e.g. other behavioural or brain states), or even related to behaviour execution (e.g. dynamics that are associated with behaviour generally but are not task specific). Again, I implement TNDM using a deep neural network to improve its scalability and expressivity. On synthetic data and on real recordings from the premotor (PMd) and primary motor cortex (M1) of a monkey performing a center-out reaching task, I show that TNDM is able to extract low-dimensional neural dynamics that are highly predictive of behaviour without sacrificing its fit to the neural data

Mikroelektroden für die chronische Ableitung und Stimulation neuronaler Aktivität im Kortex

For neuroscience an experimental access to the individual neurons is essential to investigate e.g. the interaction of different brain areas. A basic challenge therefor is the long-term stable, electrical interface to the neurons for in-vivo experiments over several months or even years. Due to the inflammatory response of the neural tissue to the foreign body a scar is created around the neural probe with the microelectrodes, so that the electrical access for recording and stimulation of neural activity in the cortex is hindered or even impossible. The present dissertation contains the development of novel neural probes with the goal to reduce the inflammatory response and enable a chronic neural interface. The therefor developed, silicon-based microfabrication process enables a monolithical integration of the neural probes with highly flexible, electrical conducting paths to reduce the mechanical coupling during micromotion of the cortex relative to the skull and thus the irritation of the neural tissue. The developed microfabrication process allows the integration of in total 18 microelectrodes on a linear probe with a rectangular cross section of only 130 Amicrometre x 30 Amicrometre and a highly flexible ribbon cable with a cross section of only 130 Amicrometre x 10 Amicrometre. As flexible insulation materials for the conducting paths to the microelectrodes the biocompatible polymers parylene-C and polyimide were investigated, in which parylene-C revealed as an unsuitable material for this purpose. To guarantee a safe electrical stimulation of neurons, electrode materials with sufficient charge injection capacity have to be used. For this purpose the promising, electrical conductive polymer PEDOT is investigated in the present dissertation, which is deposited on the microelectrodes using an electropolymerization process. In-vitro long-term tests could verify that a degradation of this polymer coating does not occur after short current pulses. Improvements of the polymerization process could furthermore increase the mechanical stability and charge injection capacity of ca. 2 mC/cm2 of this electrode coating. For the implantation of the neural probes an insertion tool was additionally designed and fabricated, which is used to enable a complete and precise insertion of the probe into the cortex. In-vivo experiments could verify the functionality for chronic recording and microstimulation of neural activity using the novel neural probes. Therefore these neural implants have a high potential also for medical applications