Investigating computational properties of a neurorobotic closed loop system

This work arises as an attempt to increase and deepen the knowledge of the encoding method of the information by the nervous system. In particular, this study focuses on computational properties of neuronal cultures grown in vitro. Through a neuro-robotic close-loop system composed of either cortical or hippocampal cultures (plated on micro-electrode arrays) on one side and of a robot controlled by the cultures on the other side, it has been possible to analyze experimental dataopenEmbargo per motivi di segretezza e/o di proprietà dei risultati e/o informazioni sensibil

Study of neural circuits using multielectrode arrays in movement disorders

Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2022-2023. Tutor/Director: Rodríguez Allué, Manuel JoséNeurodegenerative movement-related disorders are characterized by a progressive degeneration and loss of neurons, which lead to motor control impairment. Although the precise mechanisms underlying these conditions are still unknown, an increasing number of studies point towards the analysis of neural networks and functional connectivity to unravel novel insights. The main objective of this work is to understand cellular mechanisms related to dysregulated motor control symptoms in movement disorders, such as Chorea-Acanthocytosis (ChAc), by employing multielectrode arrays to analyze the electrical activity of neuronal networks in mouse models. We found no notable differences in cell viability between neurons with and without VPS13A knockdown, that is the only gene known to be implicated in the disease, suggesting that the absence of VPS13A in neurons may be partially compensated by other proteins. The MEA setup used to capture the electrical activity from neuron primary cultures is described in detail, pointing out its specific characteristics. At last, we present the alternative backup approach implemented to overcome the challenges faced during the research process and to explore the advanced algorithms for signal processing and analysis. In this report, we present a thorough account of the conception and implementation of our research, outlining the multiple limitations that have been encountered all along the course of the project. We provide a detailed analysis on the project’s economical and technical feasibility, as well as a comprehensive overview of the ethical and legal aspects considered during the execution

In vitro neuronal cultures on MEA: an engineering approach to study physiological and pathological brain networks

Reti neuronali accoppiate a matrici di microelettrodi: un metodo ingegneristico per studiare reti cerebrali in situazioni fisiologiche e patologich

Trends and challenges in neuroengineering: toward "Intelligent" neuroprostheses through brain-"brain inspired systems" communication

Future technologies aiming at restoring and enhancing organs function will intimately rely on near-physiological and energy-efficient communication between living and artificial biomimetic systems. Interfacing brain-inspired devices with the real brain is at the forefront of such emerging field, with the term "neurobiohybrids" indicating all those systems where such interaction is established. We argue that achieving a "high-level" communication and functional synergy between natural and artificial neuronal networks in vivo, will allow the development of a heterogeneous world of neurobiohybrids, which will include "living robots" but will also embrace “intelligent” neuroprostheses for augmentation of brain function. The societal and economical impact of intelligent neuroprostheses is likely to be potentially strong, as they will offer novel therapeutic perspectives for a number of diseases, and going beyond classical pharmaceutical schemes. However, they will unavoidably raise fundamental ethical questions on the intermingling between man and machine and more specifically, on how deeply it should be allowed that brain processing is affected by implanted "intelligent" artificial systems. Following this perspective, we provide the reader with insights on ongoing developments and trends in the field of neurobiohybrids. We address the topic also from a "community building" perspective, showing through a quantitative bibliographic analysis, how scientists working on the engineering of brain-inspired devices and brain-machine interfaces are increasing their interactions. We foresee that such trend preludes to a formidable technological and scientific revolution in brain-machine communication and to the opening of new avenues for restoring or even augmenting brain function for therapeutic purposes

Microstimulation and multicellular analysis: A neural interfacing system for spatiotemporal stimulation

Willfully controlling the focus of an extracellular stimulus remains a significant challenge in the development of neural prosthetics and therapeutic devices. In part, this challenge is due to the vast set of complex interactions between the electric fields induced by the microelectrodes and the complex morphologies and dynamics of the neural tissue. Overcoming such issues to produce methodologies for targeted neural stimulation requires a system that is capable of (1) delivering precise, localized stimuli a function of the stimulating electrodes and (2) recording the locations and magnitudes of the resulting evoked responses a function of the cell geometry and membrane dynamics. In order to improve stimulus delivery, we developed microfabrication technologies that could specify the electrode geometry and electrical properties. Specifically, we developed a closed-loop electroplating strategy to monitor and control the morphology of surface coatings during deposition, and we implemented pulse-plating techniques as a means to produce robust, resilient microelectrodes that could withstand rigorous handling and harsh environments. In order to evaluate the responses evoked by these stimulating electrodes, we developed microscopy techniques and signal processing algorithms that could automatically identify and evaluate the electrical response of each individual neuron. Finally, by applying this simultaneous stimulation and optical recording system to the study of dissociated cortical cultures in multielectode arrays, we could evaluate the efficacy of excitatory and inhibitory waveforms. Although we found that the proximity of the electrode is a poor predictor of individual neural excitation thresholds, we have shown that it is possible to use inhibitory waveforms to globally reduce excitability in the vicinity of the electrode. Thus, the developed system was able to provide very high resolution insight into the complex set of interactions between the stimulating electrodes and populations of individual neurons.Ph.D.Committee Chair: Stephen P. DeWeerth; Committee Member: Bruce Wheeler; Committee Member: Michelle LaPlaca; Committee Member: Robert Lee; Committee Member: Steve Potte

A combined experimental and computational approach to investigate emergent network dynamics based on large-scale neuronal recordings

Sviluppo di un approccio integrato computazionale-sperimentale per lo studio di reti neuronali mediante registrazioni elettrofisiologich

Plasticity and Adaptation in Neuromorphic Biohybrid Systems

Neuromorphic systems take inspiration from the principles of biological information processing to form hardware platforms that enable the large-scale implementation of neural networks. The recent years have seen both advances in the theoretical aspects of spiking neural networks for their use in classification and control tasks and a progress in electrophysiological methods that is pushing the frontiers of intelligent neural interfacing and signal processing technologies. At the forefront of these new technologies, artificial and biological neural networks are tightly coupled, offering a novel \u201cbiohybrid\u201d experimental framework for engineers and neurophysiologists. Indeed, biohybrid systems can constitute a new class of neuroprostheses opening important perspectives in the treatment of neurological disorders. Moreover, the use of biologically plausible learning rules allows forming an overall fault-tolerant system of co-developing subsystems. To identify opportunities and challenges in neuromorphic biohybrid systems, we discuss the field from the perspectives of neurobiology, computational neuroscience, and neuromorphic engineering. \ua9 2020 The Author(s

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

A modular multi electrode array system for electrogenic cell characterisation and cardiotoxicity applications

Multi electrode array (MEA) systems have evolved from custom-made experimental tools, exploited for neural research, into commercially available systems that are used throughout non-invasive electrophysiological study. MEA systems are used in conjunction with cells and tissues from a number of differing organisms (e.g. mice, monkeys, chickens, plants). The development of MEA systems has been incremental over the past 30 years due to constantly changing specific bioscientific requirements in research. As the application of MEA systems continues to diversify contemporary commercial systems are requiring increased levels of sophistication and greater throughput capabilities. [Continues.