Neuromorphic hardware for somatosensory neuroprostheses

In individuals with sensory-motor impairments, missing limb functions can be restored using neuroprosthetic devices that directly interface with the nervous system. However, restoring the natural tactile experience through electrical neural stimulation requires complex encoding strategies. Indeed, they are presently limited in effectively conveying or restoring tactile sensations by bandwidth constraints. Neuromorphic technology, which mimics the natural behavior of neurons and synapses, holds promise for replicating the encoding of natural touch, potentially informing neurostimulation design. In this perspective, we propose that incorporating neuromorphic technologies into neuroprostheses could be an effective approach for developing more natural human-machine interfaces, potentially leading to advancements in device performance, acceptability, and embeddability. We also highlight ongoing challenges and the required actions to facilitate the future integration of these advanced technologies

Mediating Retinal Ganglion Cell Spike Rates Using High-Frequency Electrical Stimulation

Recent retinal studies have directed more attention to sophisticated stimulation strategies based on high-frequency (>1.0 kHz) electrical stimulation (HFS). In these studies, each retinal ganglion cell (RGC) type demonstrated a characteristic stimulus-strength-dependent response to HFS, offering the intriguing possibility of focally targeting retinal neurons to provide useful visual information by retinal prosthetics. Ionic mechanisms are known to affect the responses of electrogenic cells during electrical stimulation. However, how these mechanisms affect RGC responses is not well understood at present, particularly when applying HFS. Here, we investigate this issue via an in silico model of the RGC. We calibrate and validate the model using an in vitro retinal preparation. An RGC model based on accurate biophysics and realistic representation of cell morphology, was used to investigate how RGCs respond to HFS. The model was able to closely replicate the stimulus-strength-dependent suppression of RGC action potentials observed experimentally. Our results suggest that spike inhibition during HFS is due to local membrane hyperpolarization caused by outward membrane currents near the stimulus electrode. In addition, the extent of HFS-induced inhibition can be largely altered by the intrinsic properties of the inward sodium current. Finally, stimulus-strength-dependent suppression can be modulated by a wide range of stimulation frequencies, under generalized electrode placement conditions. In vitro experiments verified the computational modeling data. This modeling and experimental approach can be extended to further our understanding on the effects of novel stimulus strategies by simulating RGC stimulus-response profiles over a wider range of stimulation frequencies and electrode locations than have previously been explored

Connecting the Brain to Itself through an Emulation.

Pilot clinical trials of human patients implanted with devices that can chronically record and stimulate ensembles of hundreds to thousands of individual neurons offer the possibility of expanding the substrate of cognition. Parallel trains of firing rate activity can be delivered in real-time to an array of intermediate external modules that in turn can trigger parallel trains of stimulation back into the brain. These modules may be built in software, VLSI firmware, or biological tissue as in vitro culture preparations or in vivo ectopic construct organoids. Arrays of modules can be constructed as early stage whole brain emulators, following canonical intra- and inter-regional circuits. By using machine learning algorithms and classic tasks known to activate quasi-orthogonal functional connectivity patterns, bedside testing can rapidly identify ensemble tuning properties and in turn cycle through a sequence of external module architectures to explore which can causatively alter perception and behavior. Whole brain emulation both (1) serves to augment human neural function, compensating for disease and injury as an auxiliary parallel system, and (2) has its independent operation bootstrapped by a human-in-the-loop to identify optimal micro- and macro-architectures, update synaptic weights, and entrain behaviors. In this manner, closed-loop brain-computer interface pilot clinical trials can advance strong artificial intelligence development and forge new therapies to restore independence in children and adults with neurological conditions

Information processing in dissociated neuronal cultures of rat hippocampal neurons

One of the major aims of Systems Neuroscience is to understand how the nervous system transforms sensory inputs into appropriate motor reactions. In very simple cases sensory neurons are immediately coupled to motoneurons and the entire transformation becomes a simple reflex, in which a noxious signal is immediately transformed into an escape reaction. However, in the most complex behaviours, the nervous system seems to analyse in detail the sensory inputs and is performing some kind of information processing (IP). IP takes place at many different levels of the nervous system: from the peripheral nervous system, where sensory stimuli are detected and converted into electrical pulses, to the central nervous system, where features of sensory stimuli are extracted, perception takes place and actions and motions are coordinated. Moreover, understanding the basic computational properties of the nervous system, besides being at the core of Neuroscience, also arouses great interest even in the field of Neuroengineering and in the field of Computer Science. In fact, being able to decode the neural activity can lead to the development of a new generation of neuroprosthetic devices aimed, for example, at restoring motor functions in severely paralysed patients (Chapin, 2004). On the other side, the development of Artificial Neural Networks (ANNs) (Marr, 1982; Rumelhart & McClelland, 1988; Herz et al., 1981; Hopfield, 1982; Minsky & Papert, 1988) has already proved that the study of biological neural networks may lead to the development and to the design of new computing algorithms and devices. All nervous systems are based on the same elements, the neurons, which are computing devices which, compared to silicon components, are much slower and much less reliable. How are nervous systems of all living species able to survive being based on slow and poorly reliable components? This obvious and na\uefve question is equivalent to characterizing IP in a more quantitative way. In order to study IP and to capture the basic computational properties of the nervous system, two major questions seem to arise. Firstly, which is the fundamental unit of information processing: 2 single neurons or neuronal ensembles? Secondly, how is information encoded in the neuronal firing? These questions - in my view - summarize the problem of the neural code. The subject of my PhD research was to study information processing in dissociated neuronal cultures of rat hippocampal neurons. These cultures, with random connections, provide a more general view of neuronal networks and assemblies, not depending on the circuitry of a neuronal network in vivo, and allow a more detailed and careful experimental investigation. In order to record the activity of a large ensemble of neurons, these neurons were cultured on multielectrode arrays (MEAs) and multi-site stimulation was used to activate different neurons and pathways of the network. In this way, it was possible to vary the properties of the stimulus applied under a controlled extracellular environment. Given this experimental system, my investigation had two major approaches. On one side, I focused my studies on the problem of the neural code, where I studied in particular information processing at the single neuron level and at an ensemble level, investigating also putative neural coding mechanisms. On the other side, I tried to explore the possibility of using biological neurons as computing elements in a task commonly solved by conventional silicon devices: image processing and pattern recognition. The results reported in the first two chapters of my thesis have been published in two separate articles. The third chapter of my thesis represents an article in preparation

Analysis of Factors Affecting the Performance of Retinal Prostheses Using Finite Element Modelling of Electric Field Distribution in the Retina

This dissertation proposes a computational framework targeted at improving the design of currently employed retinal prostheses. The framework was used for analysing factors impacting the performance of prostheses in terms of electrical stimulation for retinal neurons, which might lead to a perception of pixelated vision. Despite their demonstrated effectiveness, the chronic and safe usage of these retinal prostheses in human and animal trials is jeopardised due to high stimulation thresholds. This is related to the distance between the stimulating electrodes and the retinal neurons resulting from the implantation procedure. The major goal of this dissertation was to evaluate the stimulation efficacy in current implantable planar microelectrode-based retinal prostheses and consequently demonstrate their weakness, thereby providing scope for the development of future implants. The effect of geometrical factors i.e., electrode-retina distance and electrode size on stimulation applied to the retina by retinal prostheses was studied. To this end, a finite element method based simulation framework to compute electric field distribution in the retina was constructed. An electrical model of the retina was an integral part of the framework, essentially represented by a resistivity profile of the multi-layered retina. The elements of a retinal prosthesis were modelled by incorporating realistic electrode sizes, an anatomical and electrical model of the retina, a precise positioning of stimulation and return electrodes and the location of the implant with respect to the retina representing the epiretinal and subretinal stimulation schemes. The simulations were carried out both in quasi-static and direct current (DC) modes. It was observed that electrode-electrolyte interface and tissue capacitance could be safely neglected in our model based on the magnitude of the applied voltage stimulus and frequencies under consideration. Therefore, all simulations were conducted in DC mode. Thresholds and lateral extents of the stimulation were computed for electrode sizes corresponding to existing and self-fabricated implants. The values and trends obtained were in agreement with experiments from literature and our collaborators at the les Hôpitaux Universitaires de Genève (HUG). In the subretinal stimulation scheme, the computed variation of impedance with electrode-retina distance correlated well with time varying in vivo impedance measurements in rats conducted in collaboration with the Institut de la Vision, INSERM, Paris. Finally, it was also reiterated that the currently employed retinal prostheses are not very efficient due to a significant distance between the stimulation electrode and the retinal cells. In addition, I present a new experimental technique for measuring the absolute and local resistivity profile in high-resolution along the retinal depth, based on impedance spectroscopy using a bipolar microprobe. This experiment was devised to extract the resistivity profile of an embryonic chick retina to construct an electrical model for the simulation framework to simulate in vitro retinal stimulation experiments conducted by HUG collaborators. We validated the capability of the technique in rat and embryonic chick retinas. In conclusion, the computational framework presented in this dissertation is more realistic than those found in literature, but represents only a preliminary step towards an accurate model of a real implantation scenario in vivo. The simulation results are in agreement with results from clinical trials in humans for epiretinal configuration (literature) and with in vitro results for epiretinal and subretinal stimulation applied to chick retinas (HUG). The developed simulation framework computes quantities that can form a reference for quality control during surgery while inserting implants in the eye and functionality checks by electrophysiologists. Furthermore, this framework is useful in deciding the specifications of stimulation electrodes such as optimal size, shape, material, array density, and the position of the reference electrode to name a few. The work presented here offers to aid in optimising retinal prostheses and implantation procedures for patients and eventually contributes towards improving their quality of life

A role for sensory areas in coordinating active sensing motions

Active sensing, which incorporates closed-loop behavioral selection of information during sensory acquisition, is an important feature of many sensory modalities. We used the rodent whisker tactile system as a platform for studying the role cortical sensory areas play in coordinating active sensing motions. We examined head and whisker motions of freely moving mice performing a tactile search for a randomly located reward, and found that mice select from a diverse range of available active sensing strategies. In particular, mice selectively employed a strategy we term contact maintenance, where whisking is modulated to counteract head motion and sustain repeated contacts, but only when doing so is likely to be useful for obtaining reward. The context dependent selection of sensing strategies, along with the observation of whisker repositioning prior to head motion, suggests the possibility of higher level control, beyond simple reflexive mechanisms. In order to further investigate a possible role for primary somatosensory cortex (SI) in coordinating whisk-by-whisk motion, we delivered closed-loop optogenetic feedback to SI, time locked to whisker motions estimated through facial electromyography. We found that stimulation regularized whisking (increasing overall periodicity), and shifted whisking frequency, changes that emulate behaviors of rodents actively contacting objects. Importantly, we observed changes to whisk timing only for stimulation locked to whisker protractions, possibly encoding that natural contacts are more likely during forward motion of the whiskers. Simultaneous neural recordings from SI show cyclic changes in excitability, specifically that responses to excitatory stimulation locked to whisker retractions appeared suppressed in contrast to stimulation during protractions that resulted in changes to whisk timing. Both effects are evident within single whisks. These findings support a role for sensory cortex in guiding whisk-by-whisk motor outputs, but suggest a coupling that depends on behavioral context, occurring on multiple timescales. Elucidating a role for sensory cortex in motor outputs is important to understanding active sensing, and may further provide novel insights to guide the design of sensory neuroprostheses that exploit active sensing context

The Effects of Intracortical Microstimulation Parameters on Neural Responses

RÉSUMÉ Les microstimulations de tissues nerveux du cerveau sont utilisés dans un grand nombre de prothèses sensorielles, de thérapies cliniques et autres activités de recherche se servant de la stimulation électrique. Actuellement, les paramètres de stimulation sont adaptés à chaque application via des tests itératifs. Les méthodes d'optimisation cherchent à améliorer les stimuli développés pour des objectifs spécifiques de stimulation, mais la compréhension fondamentale de la façon dont les paramètres de stimulation influencent les circuits neuronaux qu’ils activent reste largement incomplète. Ce déficit retarde l'optimisation de protocoles existants et rend le développement de nouvelles applications de stimulation difficile. À ce jour, un certain nombre de dispositifs prothétiques validés dès les années 1970 restent en développement, principalement en raison de l'incapacité de ces dispositifs à communiquer efficacement avec le cerveau. Pour utiliser la stimulation électrique afin de transmettre des messages au système nerveux central, une meilleure conception du patron du signal de stimulation est nécessaire. Dans cette thèse, nous étudions l'influence que chaque paramètre du signal (un courant constant, symétrique carré biphasique) exerce sur les réponses qu'il évoquées au travers des microstimulations de la zone intracorticale caudale du membre antérieur dans le cortex moteur chez le rat. Les paramètres de ce signal sont l'amplitude du courant, la fréquence et la durée d'impulsion, l’intervalle d'interphase et la durée du train. Leurs effets ont été évalués par un examen des réponses électromyographiques évoquées dans les muscles des membres antérieurs du rat en réponse à chaque stimulus. Les principaux résultats décrivent comment chaque paramètre de stimulation influence l'amplitude, la latence d’apparition et la durée de la réponse. Une composante jusque-là inexplorée du signal de la réponse (que nous appelons 'activation résiduelle') est aussi analysée pour la première fois. Les théories quant à l'origine et le mécanisme neuronal sous-jacent de ce phénomène sont proposés et les paramètres de stimulation touchant son apparition, la prévalence et la durée sont décrits. La fiabilité des signaux de stimulation pour évoquer des réponses cohérentes est également évaluée par rapport aux variations de paramètres. Une méthodologie pour la conception optimisée des signaux de stimulation est proposée en utilisant un modèle de calcul simple, représentant les relations d'entrée-sortie entre les paramètres de stimulation et les réponses qu'ils évoquent. Ce modèle utilise une approche de réseau neuronal artificiel et peut être utilisé pour prédire les propriétés de la réponse lorsque les paramètres du stimulus sont connus. Compte tenu de la prévalence de la stimulation cérébrale dans les applications cliniques, de recherche et thérapeutiques, les procédures méthodologiques et de modélisation proposées ont des implications importantes dans l'optimisation des paradigmes de stimulation actuels et le développement de protocoles de stimulation pour de nouvelles applications. ----------ABSTRACT Microstimulation of brain tissue plays a key role in a variety of sensory prosthetics, clinical therapies and research applications. At present, stimulus parameters are tailored to each application via iterative testing. Computational optimization methods seek to improve tried and tested waveforms developed for specific purposes, however the fundamental understanding of how stimulation parameters influence the neural circuits they activate remains widely unknown. This deficit hinders both the optimization of existing protocols and the development of new stimulation applications. To date, a number of prosthetic devices validated as early as the 1970’s linger in the development stages largely due to the inability to effectively interface these devices with the brain. In order to use electrical stimulation to convey messages to the central nervous system, a better understanding of stimulus signal design is required. In this thesis, I investigate the influence that each parameter of the constant-current, symmetric, biphasic square waveform exerts on the responses it evokes through intracortical microstimulation of the caudal forelimb area of the rat motor cortex. The parameters under investigation include the current amplitude, pulse frequency, pulse duration, interphase interval and train duration of the stimulus and effects were assessed by examining the electromyographic responses evoked in the rat forelimb muscles in response to each stimulus. The major findings describe how each parameter of the stimulus signal influences the magnitude, onset latency, and duration of the response. A previously unexplored component of the response signal (which we called ‘residual activation’) is analyzed for the first time. Hypotheses as to the origin and underlying neural mechanism of this phenomenon are proposed and the stimulus parameters affecting its occurrence, prevalence and duration are described. The reliability of stimulation signals for evoking consistent responses is also assessed with respect to parameter variations. A methodology for the informed design of stimulation signals is proposed and aided by the development of a simple computational model representing the input-output relationships between stimulation parameters and the responses they evoke. This model uses an artificial neural network approach and can be used to predict the properties of the response when the parameters of the stimulus are known. Given the prevalence of brain stimulation in clinical, research and therapeutic applications the proposed methodological and modeling procedures have important implications in the optimization of current stimulation paradigms and the development of stimulation protocols for new applications

Organizational Posthumanism

Building on existing forms of critical, cultural, biopolitical, and sociopolitical posthumanism, in this text a new framework is developed for understanding and guiding the forces of technologization and posthumanization that are reshaping contemporary organizations. This ‘organizational posthumanism’ is an approach to analyzing, creating, and managing organizations that employs a post-dualistic and post-anthropocentric perspective and which recognizes that emerging technologies will increasingly transform the kinds of members, structures, systems, processes, physical and virtual spaces, and external ecosystems that are available for organizations to utilize. It is argued that this posthumanizing technologization of organizations will especially be driven by developments in three areas: 1) technologies for human augmentation and enhancement, including many forms of neuroprosthetics and genetic engineering; 2) technologies for synthetic agency, including robotics, artificial intelligence, and artificial life; and 3) technologies for digital-physical ecosystems and networks that create the environments within which and infrastructure through which human and artificial agents will interact. Drawing on a typology of contemporary posthumanism, organizational posthumanism is shown to be a hybrid form of posthumanism that combines both analytic, synthetic, theoretical, and practical elements. Like analytic forms of posthumanism, organizational posthumanism recognizes the extent to which posthumanization has already transformed businesses and other organizations; it thus occupies itself with understanding organizations as they exist today and developing strategies and best practices for responding to the forces of posthumanization. On the other hand, like synthetic forms of posthumanism, organizational posthumanism anticipates the fact that intensifying and accelerating processes of posthumanization will create future realities quite different from those seen today; it thus attempts to develop conceptual schemas to account for such potential developments, both as a means of expanding our theoretical knowledge of organizations and of enhancing the ability of contemporary organizational stakeholders to conduct strategic planning for a radically posthumanized long-term future