Real-time encoding and compression of neuronal spikes by metal-oxide memristors

Advanced brain-chip interfaces with numerous recording sites bear great potential for investigation of neuroprosthetic applications. The bottleneck towards achieving an efficient bio-electronic link is the real-time processing of neuronal signals, which imposes excessive requirements on bandwidth, energy and computation capacity. Here we present a unique concept where the intrinsic properties of memristive devices are exploited to compress information on neural spikes in real-time. We demonstrate that the inherent voltage thresholds of metal-oxide memristors can be used for discriminating recorded spiking events from background activity and without resorting to computationally heavy off-line processing. We prove that information on spike amplitude and frequency can be transduced and stored in single devices as non-volatile resistive state transitions. Finally, we show that a memristive device array allows for efficient data compression of signals recorded by a multi-electrode array, demonstrating the technology’s potential for building scalable, yet energy-efficient on-node processors for brain-chip interfaces

MAPPING LOW-FREQUENCY FIELD POTENTIALS IN BRAIN CIRCUITS WITH HIGH-RESOLUTION CMOS ELECTRODE ARRAY RECORDINGS

Neurotechnologies based on microelectronic active electrode array devices are on the way to provide the capability to record electrophysiological neural activity from a thousands of closely spaced microelectrodes. This generates increasing volumes of experimental data to be analyzed, but also offers the unprecedented opportunity to observe bioelectrical signals at high spatial and temporal resolutions in large portions of brain circuits. The overall aim of this PhD was to study the application of high-resolution CMOS-based electrode arrays (CMOS-MEAs) for electrophysiological experiments and to investigate computational methods adapted to the analysis of the electrophysiological data generated by these devices. A large part of my work was carried out on cortico-hippocampal brain slices by focusing on the hippocampal circuit. In the history of neuroscience, a major technological advance for hippocampal research, and also for the field of neurobiology, was the development of the in vitro hippocampal slice preparation. Neurobiological principles that have been discovered from work on in vitro hippocampal preparations include, for instance, the identification of excitatory and inhibitory synapses and their localization, the characterization of transmitters and receptors, the discovery of long-term potentiation (LTP) and long-term depression (LTD) and the study of oscillations in neuronal networks. In this context, an initial aim of my work was to optimize the preparation and maintenance of acute cortico-hippocampal brain slices on planar CMOS-MEAs. At first, I focused on experimental methods and computational data analysis tools for drug-screening applications based on LTP quantifications. Although the majority of standard protocols still use two electrodes platforms for quantifying LTP, in my PhD I investigate the potential advantages of recording the electrical activity from many electrodes to spatiotemporally characterized electrically induced responses. This work also involved the collaboration with 3Brain AG and a CRO involved in drug-testing, and led to a software tool that was licensed for developing its exploitation. In a second part of my work I focused on exploiting the recording resolution of planar CMOS-MEAs to study the generation of sharp wave ripples (SPW-Rs) in the hippocampal circuit. This research activity was carried out also by visiting the laboratory of Prof. A. Sirota (Ludwig Maximilians University, Munich). In addition to set-up the experimental conditions to record SPW-Rs from planar CMOS-MEAs integrating 4096 microelectrodes, I also explored the implementation of a data analysis pipeline to identify spatiotemporal features that might characterize different type of in-vitro generated SPW-R events. Finally, I also contributed to the initial implementation of high-density implantable CMOS-probes for in-vivo electrophysiology with the aim of evaluating in vivo the algorithms that I developed and investigated on brain slices. With this aim, in the last period of my PhD I worked on the development of a Graphical User Interface for controlling active dense CMOS probes (or SiNAPS probes) under development in our laboratory. I participated to preliminary experimental recordings using 4-shank CMOS-probes featuring 1024 simultaneously recording electrodes and I contributed to the development of a software interface for executing these experiments

CMOS MULTI-MODAL INTEGRATED SYSTEMS FOR FUTURE BIOELECTRONICS AND BIOSENSORS

Cells are the basic structural biological units of all known living organisms. They are highly sophisticated system with thousands of molecules operating in hundreds of pathways to maintain their proper functions, phenotypes, and physiological behaviors. With this scale of complexity, cells often exhibit multi-physiological properties as their cellular fingerprints from external stimulations. In order to further advance the frontiers in bioscience and biotechnologies such as stem cell manufacturing, synthetic biology, and regenerative medicine, it is required to comprehend complex cell physiology of living cells. Therefore, a comprehensive set of technologies is needed to harvest quantitative biological data from given cell samples. Such demands have stimulated extensive research on new bioelectronics and biosensors to characterize their functional information by converting their biological activities to electrical signals. As a result, various bioelectronics and biosensors are reported and employed in many in vivo and in vitro applications. Since sensing electrodes of the devices are physically in touch with biological/chemical samples and record their signals, long-term biocompatibility and chemical/mechanical stability is of paramount importance in numerous biological applications. Furthermore, the devices should achieve high sensitivity/resolution/linearity, large field-of-view (FoV), multi-modal sensing, and real-time monitoring, while maintaining small feature size of devices to use small volume of biological/chemical samples and reduce cost. As a result, My Ph.D research aims to study interfacial electrochemical impedance spectroscopy (EIS) of electrodes with different combination of materials/sizes and to design novel multi-modal sensing/actuation array architectures with CMOS compatible in-house post-processing to address the design challenges of the bioelectronics and biosensors.Ph.D

Neuronal Population Encoding of Sensory Information in the Rat Barrel Cortex: Local Field Potential Recording and Characterization by an Innovative High-Resolution Brain-Chip Interface

Neuronal networks are at the base of information processing in the brain. They are series of interconnected neurons whose activation defines a recognizable linear pathway. The main goal of studying neural ensembles is to characterize the relationship between the stimulus and the individual or general neuronal responses and the relation amongst the electrical activities of neurons within the network, also understanding how topology and connectivity relates to their function. Many techniques have been developed to study these complex systems: single-cell approaches aim to investigate single neurons and their connections with a limited number of other nerve cells; on the opposite side, low-resolution large-scale approaches, such as functional MRI (Magnetic Resonance Imaging) or electroencephalography (EEG), record signal changes in the brain that are generated by large populations of cells. More recently, multisite recording techniques have been developed to overcome the limitations of previous approaches, allowing to record simultaneously from huge neuronal ensembles with high spatial resolution and in different brain regions, i.e. by using implantable semiconductor chips. Local Field Potentials (LFPs), the part of electrophysiological signals that has frequencies below 500 Hz, capture key integrative synaptic processes that cannot be measured by analyzing the spiking activity of few neurons alone. Several studies have used LFPs to investigate cortical network mechanisms involved in sensory processing, motor planning and higher cognitive processes, like memory and perception. LFPs are also promising signals for steering neuroprosthetic devices and for monitoring neural activity even in human beings, since they are more easily and stably recorded in chronic settings than neuronal spikes. In this work, LFP profiles recorded in the rat barrel cortex through high-resolution CMOS-based needle chips are presented and compared to those obtained by means of conventional Ag/AgCl electrodes inserted into glass micropipettes, which are widely used tools in electrophysiology. The rat barrel cortex is a well-known example of topographic mapping where each of the whiskers on the snout of the animal is mapped onto a specific cortical area, called a barrel. The barrel cortex contains the somatosensory representation of the whiskers and forms an early stage of cortical processing for tactile information, along with the trigeminal ganglion and the thalamus. It is an area of great importance for understanding how the cerebral cortex works, since the cortical columns that form the basic building blocks of the neocortex can be actually seen within the barrel. Moreover, the barrel cortex has served as a test-bed system for several new methodologies, partly because of its unique and instantly identifiable form, and partly because the species that have barrels, i.e. rodents, are the most commonly used laboratory mammal. The barrel cortex, the whiskers that activate it and the intervening neural pathways have been increasingly the subject of focus by a growing number of research groups for quite some time. Nowadays, studies (such this one) are directed not only at understanding the barrel cortex itself but also at investigating issues in related fields using the barrel cortex as a base model. In this study, LFP responses were evoked in the target barrel by repeatedly deflecting the corresponding whisker in a controlled fashion, by means of a specifically designed closed-loop piezoelectric bending system triggered by a custom LabView acquisition software. Evoked LFPs generated in the barrel cortex by many consecutive whiskers' stimulations show large variability in shapes and timings. Moreover, anesthetics can deeply affect the profile of evoked responses. This work presents preliminary results on the variability and the effect of commonly used anesthetics on these signals, by comparing the distributions of evoked responses recorded from rats anesthetized with tiletamine-xylazine, which mainly blocks the excitatory NMDA receptors, and urethane, which conversely affects both the excitatory and inhibitory system, in a complex and balanced way yet preserving the synaptic plasticity. Representative signal shape characteristics (e.g., latencies and amplitude of events) extracted from evoked responses acquired from different cortical layers are analyzed and discussed. Statistical distributions of these parameters are estimated for all the different depths, in order to assess the variability of LFPs generated by individual mechanical stimulations of single whiskers along the entire cortical column. Preliminary results showed a great variability in cortical responses, which varied both in latency and amplitude across layers. We found significant difference in the latency of the first principal peak of the responses: under tiletamine-xylazine anesthetic, the responses or events of the evoked LFPs occurred later than the ones recorded while urethane was administered. Furthermore, the distributions of this parameter in all cortical layers were narrower in case of urethane. This behavior should be attributed to the different effects of these two anesthetics on specific synaptic receptors and thus on the encoding and processing of the sensory input information along the cortical pathway. The role of the ongoing basal activity on the modulation of the evoked response was also investigated. To this aim, spontaneous activity was recorded in different cortical layers of the rat barrel cortex under the two types of anesthesia and analyzed in the statistical context of neuronal avalanches. A neuronal avalanche is a cascade of bursts of activity in neural networks, whose size distribution can be approximated by a power law. The event size distribution of neuronal avalanches in cortical networks has been reported to follow a power law of the type P(s)= s^-a, with exponent a close to 1.5, which represent a reflection of long-range spatial correlations in spontaneous neuronal activity. Since negative LFP peaks (nLFPs) originates from the sum of synchronized Action Potentials (AP) from neurons within the vicinity of the recording electrode, we wondered if it were possible to model single nLFPs recorded in the basal activity traces by means of only one electrode as the result of local neuronal avalanches, and thus we analyzed the size (i.e. the amplitude in uV) distribution of these peaks so as to identify a suitable power-law distribution that could describe also these single-electrode records. Finally, the results of the first ever measurements of evoked LFPs within an entire column of the barrel cortex obtained by means of the latest generation of CMOS-based implantable needles, having 256 recording sites arranged into two different array topologies (i.e. 16 x 16 or 4 x 64, pitches in the x- and y-direction of 15 um and 33 um respectively), are presented and discussed. A propagation dynamics of the LFP can be already recognized in these first cortical profiles. In the next future, the use of these semiconductor devices will help, among other things, to understand how degenerating syndromes like Parkinson or Alzheimer evolve, by coupling detected behaviors and symptoms of the disease to neuronal features. Implantable chips could then be used as 'electroceuticals', a newly coined term that describes one of the most promising branch of bioelectronic medicine: they could help in reverting the course of neurodegenerative diseases, by constituting the basis of neural prostheses that physically supports or even functionally trains impaired neuronal ensembles. High-resolution extraction and identification of neural signals will also help to develop complex brain-machine interfaces, which can allow intelligent prostheses to be finely controlled by their wearers and to provide sophisticated feedbacks to those who have lost part of their body or brain functions

Noise, coherent activity and network structure in neuronal cultures

In this thesis we apply a multidisciplinary approach, based on statistical physics and complex systems, to the study of neuronal dynamics. We focus on understanding, using theoretical and computational tools, how collective neuronal activity emerges in a controlled system, a neuronal culture. We show how the interplay between noise and network structure defines the emergent collective behavior of the system. We build, using theory and simulation, a framework that takes carefully describes spontaneous activity in neuronal cultures by taking into account the underlying network structure of neuronal cultures and use an accurate, yet simple, model for the individual neuronal dynamics. We show that the collective behavior of young cultures is dominated by the nucleation and propagations of activity fronts (bursts) throughout the system. These bursts nucleate at specific sites of the culture, called nucleation points, which result in a highly heterogeneous probability distribution of nucleation. We are able to explain the nucleation mechanism theoretically as a mechanism of noise propagation and amplification called noise focusing. We also explore the internal structure of activity avalanches by using well--defined regular networks, in which all the neurons have the same connectivity rules (motifs). Within these networks, we are able to associate to the avalanches an effective velocity and topological size and relate it to specific motifs. We also devise a continuum description of a neuronal culture at the mesoscale, i.e., we move away from the single neuron dynamics into a coarse--grained description that is able to capture most of the characteristic observables presented in previous chapters. This thesis also studies the spontaneous activity of neuronal cultures within the framework of quorum percolation. We study the effect of network structure within quorum percolation and propose a new model, called stochastic quorum percolation, that includes dynamics and the effect of internal noise. Finally, we use tools from information theory, namely transfer entropy, to show how to reliably infer the connectivity of a neuronal network from its activity, and how to distinguish between different excitatory and inhibitory connections purely from the activity, with no prior knowledge of the different neuronal types. The technique works directly on the fluorescence traces obtained in calcium imaging experiments, without the need to infer the underlying spike trains

Advances in Bioengineering

The technological approach and the high level of innovation make bioengineering extremely dynamic and this forces researchers to continuous updating. It involves the publication of the results of the latest scientific research. This book covers a wide range of aspects and issues related to advances in bioengineering research with a particular focus on innovative technologies and applications. The book consists of 13 scientific contributions divided in four sections: Materials Science; Biosensors. Electronics and Telemetry; Light Therapy; Computing and Analysis Techniques

Neutralisation of myoelectric interference from recorded nerve signals using models of the electrode impedance

Any form of paralysis due to spinal cord injury or other medical condition, can have a significant impact on the quality and life expectancy of an individual. Advances in medicine and surgery have offered solutions that can improve the condition of a patient, however, most of the times an individual’s life does not dramatically improve. Implanted neuroprosthetic devices can partially restore the lost functionalities by means of functional electrical stimulation techniques. This involves applying patterns of electrical current pulses to innervate the neural pathways between the brain and the affected muscles/organs, while recording of neural information from peripheral nerves can be used as feedback to improve performance. Recording naturally occurring nerve signals via implanted electrodes attached to tripolar amplifier configurations is an approach that has been successfully used for obtaining desired information in non-acute preparations since the mid-70s. The neural signal (i.e. ENG), which can be exploited as feedback to another system (e.g. a stimulator), or simply extracted for further processing, is then intrinsically more reliable in comparison to signals obtained by artificial sensors. Sadly, neural recording of this type can be greatly compromised by myoelectric (i.e. EMG) interference, which is present at the neural interface and registered by the recording amplifier. Although current amplifier configurations reduce myoelectric interference this is suboptimal and therefore there is room for improvement. The main difficulty exists in the frequency-dependence of the electrode-tissue interface impedance which is complex. The simplistic Quasi-Tripole amplifier configuration does not allow for the complete removal of interference but it is the most power efficient because it uses only one instrumentation amplifier. Conversely, the True-Tripole and its developed automatic counterpart the Adaptive-Tripole, although minimise interference and provide means of compensating for the electrode asymmetries and changes that occur to the neural interface (e.g. due to tissue growth), they do not remove interference completely as the insignificant electrode impedance is still important. Additionally, removing interference apart from being dependent on the frequency of the interfering source, it is also subject to its proximity and orientation with respect to the recording electrodes, as this affects the field. Hence neutralisation with those two configurations, in reality, is not achieved in the entire bandwidth of the neural signal in the interfering spectrum. As both are less power efficient than the Quasi-Tripole an alternative configuration offering better performance in terms of interference neutralisation (i.e. frequency-independent, insensitive to the external interference fields) and, if possible, consume less power, is considered highly attractive. The motivation of this work is based on the following fact: as there are models that can mimic the frequency response of metal electrodes it should be possible, by constructing a network of an equivalent arrangement to the impedance of electrodes, to fit the characteristic neutralisation impedance – the impedance needed to balance a recording tripole – and ideally require no adjustment for removing interference. The validity of this postulation is proven in a series of in-vitro preparations using a modified version of the Quasi-Tripole made out of discrete circuit components where an impedance is placed at either side of the outer electrodes for balancing the recording arrangement. Various models were used in place of that impedance. In particular, representing the neutralisation impedance as a parallel RC reduced interference by a factor of 10 at all frequencies in the bandwidth of the neural signal while removed it completely at a spot frequency. Conversely, modelling the effect of the constant phase angle impedance of highly polarisable electrodes using a 20 stages non-uniform RC ladder network resulted in the minimisation of interference without the initial requirement of continuous adjustment. It is demonstrated that with a model that does not perfectly fit the impedance profile of a monopolar electrochemical cell an average reduction in interference of about 100 times is achieved, with the cell arranged as a Wheatstone bridge that can be balanced in the ENG band

Experimental demonstration of single neuron specificity during underactuated neurocontrol

Population-level neurocontrol has been advanced predominately through the miniaturization of hardware, such as MEMS-based electrodes. However, miniaturization alone may not be viable as a method for single-neuron resolution control within large ensembles, as it is typically infeasible to create electrode densities approaching 1:1 ratios with the neurons whose control is desired. That is, even advanced neural interfaces will likely remain underactuated, in that there will be fewer inputs (electrodes) within a given area than there are outputs (neurons). A complementary “software” approach could allow individual electrodes to independently control multiple neurons simultaneously, to improve performance beyond naïve hardware limits. An underactuated control schema, demonstrated in theoretical analysis and simulation (Ching & Ritt, 2013), uses stimulus strength-duration tradeoffs to activate a target neuron while leaving non-targets inactive. Here I experimentally test this schema in vivo, by independently controlling pairs of cortical neurons receiving common optogenetic input, in anesthetized mice. With this approach, neurons could be specifically and independently controlled following a short (~3 min) identification procedure. However, drift in neural responsiveness limited the performance over time. I developed an adaptive control procedure that fits stochastic Integrate and Fire (IAF) models to blocks of neural recordings, based on the deviation of expected from observed spiking, and selects optimal stimulation parameters from the updated models for subsequent blocks. I find the adaptive approach can maintain control over long time periods (>20 minutes) in about 30% of tested candidate neuron pairs. Because stimulation distorts the observation of neural activity, I further analyzed the influence of various forms of spike sorting corruption, and proposed methods to compensate for their effects on neural control systems. Overall, these results demonstrate the feasibility of underactuated neurocontrol for in vivo applications as a method for increasing the controllable population of high density neural interfaces

Spacelab Science Results Study

Beginning with OSTA-1 in November 1981 and ending with Neurolab in March 1998, a total of 36 Shuttle missions carried various Spacelab components such as the Spacelab module, pallet, instrument pointing system, or mission peculiar experiment support structure. The experiments carried out during these flights included astrophysics, solar physics, plasma physics, atmospheric science, Earth observations, and a wide range of microgravity experiments in life sciences, biotechnology, materials science, and fluid physics which includes combustion and critical point phenomena. In all, some 764 experiments were conducted by investigators from the U.S., Europe, and Japan. The purpose of this Spacelab Science Results Study is to document the contributions made in each of the major research areas by giving a brief synopsis of the more significant experiments and an extensive list of the publications that were produced. We have also endeavored to show how these results impacted the existing body of knowledge, where they have spawned new fields, and if appropriate, where the knowledge they produced has been applied