A novel Three-Dimensional Micro-Electrode Array for in-vitro electrophysiological applications

Microelectrode arrays (MEAs) represent a powerful and popular tool to study in vitro neuronal networks and acute brain slices. The research standard for MEAs is planar or 2D-MEAs, which have been in existence for over 30 years and used for extracellular recording and stimulation from cultured neuronal cells and tissue slices. However, planar MEAs suffer from rapid data attenuation in the z-direction when stimulating/recording from 3D in-vitro neuronal cultures or brain slices. The existing proposed 3D in-vitro neuronal models allow to record the electrophysiological activity of the 3D network only from the bottom layer (i.e. the one directly coupled to the planar MEAs). Thus, to further develop and optimize such 3D neuronal network systems and to study and understand how the 3D neuronal network dynamics changes in different layers of the 3D structure, new three-dimensional microelectrodes arrays (3D-MEAs) are required. Early attempts in this field resulted in interesting integrated approaches toward protruding or spiked 3D-MEAs. Although these first prototypes could be successfully employed with brain slices, the limited heights of the electrodes (up to max 70 \u3bcm) and the peculiar shape of the recording areas made them not an ideal solution for 3D neuronal cultures. Moreover, a convenient and versatile method for the fabrication of multilevel 3D microelectrode arrays has yet to be obtained, due to the usually complicated and expensive designs and a lack of a full compatibility with standard MEAs both in terms of materials and recording area dimensions. To overcome the afore-mentioned challenges, in this work, I present the design, microfabrication, and characterization of a new 3D-MEA composed of pillar-shaped gold 3D structures with heights of more than 100 \u3bcm that can be used, in principle, on every kind of MEA, both custom-made and commercial. I successfully demonstrate the capability and ability of such 3D-MEA to record electrophysiological spontaneous activity from 3D engineered in-vitro neuronal networks and both 4-AP-induced epileptiform-like and electrically-evoked activity from mouse acute brain slices. I also demonstrate how the developed 3D-MEA allows better recording and stimulating conditions while interfacing with acute brain slices as compared to planar electrode arrays and previously reported 3D MEA technologies

Digital neural circuits : from ions to networks

PhD ThesisThe biological neural computational mechanism is always fascinating to human beings since it shows several state-of-the-art characteristics: strong fault tolerance, high power efficiency and self-learning capability. These behaviours lead the developing trend of designing the next-generation digital computation platform. Thus investigating and understanding how the neurons talk with each other is the key to replicating these calculation features. In this work I emphasize using tailor-designed digital circuits for exactly implementing bio-realistic neural network behaviours, which can be considered a novel approach to cognitive neural computation. The first advance is that biological real-time computing performances allow the presented circuits to be readily adapted for real-time closed-loop in vitro or in vivo experiments, and the second one is a transistor-based circuit that can be directly translated into an impalpable chip for high-level neurologic disorder rehabilitations. In terms of the methodology, first I focus on designing a heterogeneous or multiple-layer-based architecture for reproducing the finest neuron activities both in voltage-and calcium-dependent ion channels. In particular, a digital optoelectronic neuron is developed as a case study. Second, I focus on designing a network-on-chip architecture for implementing a very large-scale neural network (e.g. more than 100,000) with human cognitive functions (e.g. timing control mechanism). Finally, I present a reliable hybrid bio-silicon closed-loop system for central pattern generator prosthetics, which can be considered as a framework for digital neural circuit-based neuro-prosthesis implications. At the end, I present the general digital neural circuit design principles and the long-term social impacts of the presented work

Advanced photonic and electronic systems WILGA 2016

Young Researchers Symposium WILGA on Photonics Applications and Web Engineering has been organized since 1998, two times a year. Subject area of the Wilga Symposium are advanced photonic and electronic systems in all aspects: theoretical, design and application, hardware and software, academic, scientific, research, development, commissioning and industrial, but also educational and development of research and technical staff. Each year, during the international Spring edition, the Wilga Symposium is attended by a few hundred young researchers, graduated M.Sc. students, Ph.D. students, young doctors, young research workers from the R&D institutions, universities, innovative firms, etc. Wilga, gathering through years the organization experience, has turned out to be a perfect relevant information exchange platform between young researchers from Poland with participation  of international guests, all active in the research areas of electron and photon technologies, electronics, photonics, telecommunications, automation, robotics and information technology, but also technical physics. The paper summarizes the achievements of the 38th Spring Edition of 2016 WILGA Symposium, organized in Wilga Village Resort owned by Warsaw University of technology

APPROXIMATE COMPUTING BASED PROCESSING OF MEA SIGNALS ON FPGA

The Microelectrode Array (MEA) is a collection of parallel electrodes that may measure the extracellular potential of nearby neurons. It is a crucial tool in neuroscience for researching the structure, operation, and behavior of neural networks. Using sophisticated signal processing techniques and architectural templates, the task of processing and evaluating the data streams obtained from MEAs is a computationally demanding one that needs time and parallel processing.This thesis proposes enhancing the capability of MEA signal processing systems by using approximate computing-based algorithms. These algorithms can be implemented in systems that process parallel MEA channels using the Field Programmable Gate Arrays (FPGAs). In order to develop approximate signal processing algorithms, three different types of approximate adders are investigated in various configurations. The objective is to maximize performance improvements in terms of area, power consumption, and latency associated with real-time processing while accepting lower output accuracy within certain bounds. On FPGAs, the methods are utilized to construct approximate processing systems, which are then contrasted with the precise system. Real biological signals are used to evaluate both precise and approximative systems, and the findings reveal notable improvements, especially in terms of speed and area. Processing speed enhancements reach up to 37.6%, and area enhancements reach 14.3% in some approximate system modes without sacrificing accuracy. Additional cases demonstrate how accuracy, area, and processing speed may be traded off. Using approximate computing algorithms allows for the design of real-time MEA processing systems with higher speeds and more parallel channels. The application of approximate computing algorithms to process biological signals on FPGAs in this thesis is a novel idea that has not been explored before

Towards energy-efficient hardware acceleration of memory-intensive event-driven kernels on a synchronous neuromorphic substrate

Spiking neural networks are increasingly becoming popular as low-power alternatives to deep learning architectures. To make edge processing possible in resource-constrained embedded devices, there is a requirement for reconfigurable neuromorphic accelerators that can cater to various topologies and neural dynamics typical to these networks. Subsequently, they also must consolidate energy consumption in emulating these dynamics. Since spike processing is essentially memory-intensive in nature, a significant proportion of the system\u27s power consumption can be reduced by eliminating redundant memory traffic to off-chip storage that holds the large synaptic data for the network. In this work, I will present CyNAPSE, a digital neuromorphic acceleration fabric that can emulate different types of spiking neurons and network topologies for efficient inference. The accelerator is functionally verified on a set of benchmarks that vary significantly in topology and activity while solving the same underlying task. By studying the memory access patterns, locality of data and spiking activity, we establish the core factors that limit conventional cache replacement policies from performing well. Accordingly, a domain-specific memory management scheme is proposed which exploits the particular use-case to attain visibility of future data-accesses in the event-driven simulation framework. To make it even more robust to variations in network topology and activity of the benchmark, we further propose static and dynamic network-specific enhancements to adaptively equip the scheme with more insight. The strategy is explored and evaluated with the set of benchmarks using a software simulation of the accelerator and an in-house cache simulator. In comparison to conventional policies, we observe up to 23% more reduction in net power consumption

Exploiting All-Programmable System on Chips for Closed-Loop Real-Time Neural Interfaces

High-density microelectrode arrays (HDMEAs) feature thousands of recording electrodes in a single chip with an area of few square millimeters. The obtained electrode density is comparable and even higher than the typical density of neuronal cells in cortical cultures. Commercially available HDMEA-based acquisition systems are able to record the neural activity from the whole array at the same time with submillisecond resolution. These devices are a very promising tool and are increasingly used in neuroscience to tackle fundamental questions regarding the complex dynamics of neural networks. Even if electrical or optical stimulation is generally an available feature of such systems, they lack the capability of creating a closed-loop between the biological neural activity and the artificial system. Stimuli are usually sent in an open-loop manner, thus violating the inherent working basis of neural circuits that in nature are constantly reacting to the external environment. This forbids to unravel the real mechanisms behind the behavior of neural networks. The primary objective of this PhD work is to overcome such limitation by creating a fullyreconfigurable processing system capable of providing real-time feedback to the ongoing neural activity recorded with HDMEA platforms. The potentiality of modern heterogeneous FPGAs has been exploited to realize the system. In particular, the Xilinx Zynq All Programmable System on Chip (APSoC) has been used. The device features reconfigurable logic, specialized hardwired blocks, and a dual-core ARM-based processor; the synergy of these components allows to achieve high elaboration performances while maintaining a high level of flexibility and adaptivity. The developed system has been embedded in an acquisition and stimulation setup featuring the following platforms: \u2022 3\ub7Brain BioCam X, a state-of-the-art HDMEA-based acquisition platform capable of recording in parallel from 4096 electrodes at 18 kHz per electrode. \u2022 PlexStim\u2122 Electrical Stimulator System, able to generate electrical stimuli with custom waveforms to 16 different output channels. \u2022 Texas Instruments DLP\uae LightCrafter\u2122 Evaluation Module, capable of projecting 608x684 pixels images with a refresh rate of 60 Hz; it holds the function of optical stimulation. All the features of the system, such as band-pass filtering and spike detection of all the recorded channels, have been validated by means of ex vivo experiments. Very low-latency has been achieved while processing the whole input data stream in real-time. In the case of electrical stimulation the total latency is below 2 ms; when optical stimuli are needed, instead, the total latency is a little higher, being 21 ms in the worst case. The final setup is ready to be used to infer cellular properties by means of closed-loop experiments. As a proof of this concept, it has been successfully used for the clustering and classification of retinal ganglion cells (RGCs) in mice retina. For this experiment, the light-evoked spikes from thousands of RGCs have been correctly recorded and analyzed in real-time. Around 90% of the total clusters have been classified as ON- or OFF-type cells. In addition to the closed-loop system, a denoising prototype has been developed. The main idea is to exploit oversampling techniques to reduce the thermal noise recorded by HDMEAbased acquisition systems. The prototype is capable of processing in real-time all the input signals from the BioCam X, and it is currently being tested to evaluate the performance in terms of signal-to-noise-ratio improvement

Hacia nuevas estrategias terapéuticas basadas en cannabinoides para el síndrome de Dravet

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Medicina, leída el 17-12-2021Dravet syndrome (DS) is a rare genetic epileptic encephalopathy affecting children which, in approximately 70-80% of patients, is caused by loss-of-function mutations in the Scn1a gene, which encodes the α1 subunit of the voltage-gated sodium channel (NaV1.1). Clinically, these patients present different types of epileptic seizures, which are frequently accompanied by some comorbidities such as developmental delay, cognitive impairment, hyperactivity, autistic traits and a rate of premature mortality of around 20%. Therapeutic strategies typically involve a complex polytherapy, with antiepileptic drugs whose action mechanisms are focused on correcting hyperexcitability, i.e., the imbalance between excitation and inhibition occurring in epilepsy. Current treatment algorithms often lead to tolerance issues as well as adverse effects, and around 30% of patients remain refractory. Therefore, there is an urgent need for new and effective therapeutic approaches...El síndrome de Dravet (SD) es una encefalopatía epiléptica rara y genética que afecta a niños y que, en alrededor del 70-80% de los pacientes, está causado por mutaciones con pérdida de función en el gen Scn1a, que codifica la subunidad α1 del canal de sodio dependiente de voltaje (NaV1.1). En cuanto a la clínica, estos pacientes presentan diversos tipos de crisis epilépticas, que a menudo se acompañan por algunas comorbilidades tales como retraso en el desarrollo, alteraciones cognitivas, hiperactividad, rasgos autistas y una tasa de muerte prematura de alrededor del 20%. Las estrategias terapéuticas normalmente implican una compleja politerapia, con fármacos antiepilépticos cuyos mecanismos de acción se centran en corregir la hiperexcitiblidad, es decir, el desequilibrio entre excitación e inhibición que ocurre en situaciones de epilepsia. Los algoritmos de tratamiento actuales a menudo conllevan problemas de tolerancia, así como efectos adversos, y alrededor del 30% de los pacientes permanecen refractarios. Por ello, es urgente la búsqueda de nuevas aproximaciones terapéuticas eficaces...Fac. de MedicinaTRUEunpu

On the development of slime mould morphological, intracellular and heterotic computing devices

The use of live biological substrates in the fabrication of unconventional computing (UC) devices is steadily transcending the barriers between science fiction and reality, but efforts in this direction are impeded by ethical considerations, the field’s restrictively broad multidisciplinarity and our incomplete knowledge of fundamental biological processes. As such, very few functional prototypes of biological UC devices have been produced to date. This thesis aims to demonstrate the computational polymorphism and polyfunctionality of a chosen biological substrate — slime mould Physarum polycephalum, an arguably ‘simple’ single-celled organism — and how these properties can be harnessed to create laboratory experimental prototypes of functionally-useful biological UC prototypes. Computing devices utilising live slime mould as their key constituent element can be developed into a) heterotic, or hybrid devices, which are based on electrical recognition of slime mould behaviour via machine-organism interfaces, b) whole-organism-scale morphological processors, whose output is the organism’s morphological adaptation to environmental stimuli (input) and c) intracellular processors wherein data are represented by energetic signalling events mediated by the cytoskeleton, a nano-scale protein network. It is demonstrated that each category of device is capable of implementing logic and furthermore, specific applications for each class may be engineered, such as image processing applications for morphological processors and biosensors in the case of heterotic devices. The results presented are supported by a range of computer modelling experiments using cellular automata and multi-agent modelling. We conclude that P. polycephalum is a polymorphic UC substrate insofar as it can process multimodal sensory input and polyfunctional in its demonstrable ability to undertake a variety of computing problems. Furthermore, our results are highly applicable to the study of other living UC substrates and will inform future work in UC, biosensing, and biomedicine