Multimed: An Integrated, Multi-Application Platform for the Real-Time Recording and Sub-Millisecond Processing of Biosignals.

Enhanced understanding and control of electrophysiology mechanisms are increasingly being hailed as key knowledge in the fields of modern biology and medicine. As more and more excitable cell mechanics are being investigated and exploited, the need for flexible electrophysiology setups becomes apparent. With that aim, we designed Multimed, which is a versatile hardware platform for the real-time recording and processing of biosignals. Digital processing in Multimed is an arrangement of generic processing units from a custom library. These can freely be rearranged to match the needs of the application. Embedded onto a Field Programmable Gate Array (FPGA), these modules utilize full-hardware signal processing to lower processing latency. It achieves constant latency, and sub-millisecond processing and decision-making on 64 channels. The FPGA core processing unit makes Multimed suitable as either a reconfigurable electrophysiology system or a prototyping platform for VLSI implantable medical devices. It is specifically designed for open- and closed-loop experiments and provides consistent feedback rules, well within biological microseconds timeframes. This paper presents the specifications and architecture of the Multimed system, then details the biosignal processing algorithms and their digital implementation. Finally, three applications utilizing Multimed in neuroscience and diabetes research are described. They demonstrate the system’s configurability, its multi-channel, real-time processing, and its feedback control capabilities.

Slow potentials encode intercellular coupling and insulin demand in pancreatic beta cells.

AIMS/HYPOTHESIS: Ion fluxes constitute a major integrative signal in beta cells that leads to insulin secretion and regulation of gene expression. Understanding these electrical signals is important for deciphering the endogenous algorithms used by islets to attain homeostasis and for the design of new sensors for monitoring beta cell function. METHODS: Mouse and human islets were cultured on multielectrode arrays (MEAs) for 3-13 days. Extracellular electrical activities received on each electrode were continuously amplified and recorded for offline characterisation. RESULTS: Differential band-pass filtering of MEA recordings of mouse islets showed two extracellular voltage waveforms: action potentials (lasting 40-60 ms) and very robust slow potentials (SPs, lasting 800-1,500 ms), the latter of which have not been described previously. The frequency of SPs directly correlated with glucose concentration, peaked at 10 mmol/l glucose and was further augmented by picomolar concentrations of glucagon-like peptide-1. SPs required the closure of ATP-dependent potassium channels as they were induced by glucose or glibenclamide but were not elicited by KCl-induced depolarisation. Pharmacological tools and the use of beta cell specific knockout mice showed that SPs reflected cell coupling via connexin 36. Moreover, increasing and decreasing glucose ramps showed hysteresis with reduced glucose sensitivity during the decreasing phase. SPs were also observed in human islets and could be continuously recorded over 24 h. CONCLUSIONS/INTERPRETATION: This novel electrical signature reflects the syncytial function of the islets and is specific to beta cells. Moreover, the observed hysteresis provides evidence for an endogenous algorithm naturally present in islets to protect against hypoglycaemia.

NRV: An open framework for in silico evaluation of peripheral nerve electrical stimulation strategies.

Electrical stimulation of peripheral nerves has been used in various pathological contexts for rehabilitation purposes or to alleviate the symptoms of neuropathologies, thus improving the overall quality of life of patients. However, the development of novel therapeutic strategies is still a challenging issue requiring extensive in vivo experimental campaigns and technical development. To facilitate the design of new stimulation strategies, we provide a fully open source and self-contained software framework for the in silico evaluation of peripheral nerve electrical stimulation. Our modeling approach, developed in the popular and well-established Python language, uses an object-oriented paradigm to map the physiological and electrical context. The framework is designed to facilitate multi-scale analysis, from single fiber stimulation to whole multifascicular nerves. It also allows the simulation of complex strategies such as multiple electrode combinations and waveforms ranging from conventional biphasic pulses to more complex modulated kHz stimuli. In addition, we provide automated support for stimulation strategy optimization and handle the computational backend transparently to the user. Our framework has been extensively tested and validated with several existing results in the literature.

BIMMS: A versatile and portable system for biological tissue and electrode-tissue interface electrical characterization.

The presented design is a low-cost, compact, and open-source USB-controlled platform for biological tissue and electrode-tissue interface electrical measurements, capable of potentiostatic and galvanostatic electrical impedance spectroscopy up to 10 MHz and cyclic voltammetry with voltage compliance of +-8 V and up to 2.4 mA while ensuring tissue-safety conditions. The data acquisition and generation are based on an Analog Discovery 2 platform (Digilent, USA). We provide accuracy analysis and comparisons with a commercially available calibrated impedance analyzer. Impedance measurements are demonstrated on implanted electrodes for neural stimulation and on an isolated ex-vivo calf brain as an example use case of the presented design.

Non-invasive long-term and real-time analysis of endocrine cells on micro-electrode arrays.

Non-invasive high-throughput and long-term monitoring of endocrine cells is important for drug research, phenotyping, tissue engineering and pre-transplantation quality control. Here we report a novel approach to obtain simultaneous long-term electrical recordings of different islet cell types using multi-electrode arrays. We implemented wavelet transforms to resolve the low signal/noise ratio inherent to these measurements and extracted on-line a signature specific of cell activity. The architecture employed allows multiplexing a large number of electrodes for high-throughput screening. This method should be of considerable advantage in endocrine research and may be extended to other excitable cells previously not accessible to the technique.

Integrating an Islet-Based Biosensor in the Artificial Pancreas: In Silico Proof-of-Concept.

OBJECTIVE: Current treatment of type 1 diabetes by closed-loop therapy depends on continuous glucose monitoring. However, glucose readings alone are insufficient for an artificial pancreas to truthfully restore nutrient homeostasis where additional physiological regulators of insulin secretion play a considerable role. Previously, we have developed an electrophysiological biosensor of pancreatic islet activity, which integrates these additional regulators through electrical measurements. This work aims at investigating the performance of the biosensor in a blood glucose control loop as potential in silico proof-of-concept. METHODS: Two islet algorithm models were identified on experimental data recorded with the biosensor. First, we validated electrical measurement as a means to exploit the inborn regulation capabilities of islets for intravenous glucose measurement and insulin infusion. Subsequently, an artificial pancreas integrating the islet-based biosensor was compared to standard treatment approaches using subcutaneous routes. The closed-loop simulations were performed in the UVA/Padova T1DM Simulator where a series of realistic meal scenarios were applied to virtual diabetic patients. RESULTS: With intravenous routes, the endogenous islet algorithms successfully restored glucose homeostasis for all patient categories (mean time in range exceeds 90%) while mitigating the risk of adverse glycaemic events (mean BGI < 2). Using subcutaneous routes, the biosensor-based artificial pancreas was as efficient as standard treatments, and outperformed them under challenging conditions. CONCLUSION: This work validates the concept of using inborn pancreatic islets algorithms in an artificial pancreas in silico. SIGNIFICANCE: Pancreatic islet endogenous algorithms obtained via an electrophysiological biosensor successfully regulate blood glucose levels of virtual type 1 diabetic patients.

Neuromorphic-Based Neuroprostheses for Brain Rewiring: State-of-the-Art and Perspectives in Neuroengineering.

Neuroprostheses are neuroengineering devices that have an interface with the nervous system and supplement or substitute functionality in people with disabilities. In the collective imagination, neuroprostheses are mostly used to restore sensory or motor capabilities, but in recent years, new devices directly acting at the brain level have been proposed. In order to design the next-generation of neuroprosthetic devices for brain repair, we foresee the increasing exploitation of closed-loop systems enabled with neuromorphic elements due to their intrinsic energy efficiency, their capability to perform real-time data processing, and of mimicking neurobiological computation for an improved synergy between the technological and biological counterparts. In this manuscript, after providing definitions of key concepts, we reviewed the first exploitation of a real-time hardware neuromorphic prosthesis to restore the bidirectional communication between two neuronal populations in vitro. Starting from that 'case-study', we provide perspectives on the technological improvements for real-time interfacing and processing of neural signals and their potential usage for novel in vitro and in vivo experimental designs. The development of innovative neuroprosthetics for translational purposes is also presented and discussed. In our understanding, the pursuit of neuromorphic-based closed-loop neuroprostheses may spur the development of novel powerful technologies, such as 'brain-prostheses', capable of rewiring and/or substituting the injured nervous system.

Towards the Integration of an Islet-Based Biosensor in Closed-Loop Therapies for Patients With Type 1 Diabetes.

In diabetes mellitus (DM) treatment, Continuous Glucose Monitoring (CGM) linked with insulin delivery becomes the main strategy to improve therapeutic outcomes and quality of patients' lives. However, Blood Glucose (BG) regulation with CGM is still hampered by limitations of algorithms and glucose sensors. Regarding sensor technology, current electrochemical glucose sensors do not capture the full spectrum of other physiological signals, i.e., lipids, amino acids or hormones, relaying the general body status. Regarding algorithms, variability between and within patients remains the main challenge for optimal BG regulation in closed-loop therapies. This work highlights the simulation benefits to test new sensing and control paradigms which address the previous shortcomings for Type 1 Diabetes (T1D) closed-loop therapies. The UVA/Padova T1DM Simulator is the core element here, which is a computer model of the human metabolic system based on glucose-insulin dynamics in T1D patients. That simulator is approved by the US Food and Drug Administration (FDA) as an alternative for pre-clinical testing of new devices and closed-loop algorithms. To overcome the limitation of standard glucose sensors, the concept of an islet-based biosensor, which could integrate multiple physiological signals through electrical activity measurement, is assessed here in a closed-loop insulin therapy. This investigation has been addressed by an interdisciplinary consortium, from endocrinology to biology, electrophysiology, bio-electronics and control theory. In parallel to the development of an islet-based closed-loop, it also investigates the benefits of robust control theory against the natural variability within a patient population. Using 4 meal scenarios, numerous simulation campaigns were conducted. The analysis of their results then introduces a discussion on the potential benefits of an Artificial Pancreas (AP) system associating the islet-based biosensor with robust algorithms.

Tuning of an Artificial Pancreas Controller: an in silico methodology based on clinically-relevant criteria.

This paper presents a methodology to tune an artificial pancreas controller by minimizing the time spent in endangering glycaemic ranges (hypo- and hyperglycaemia). The risk associated to the patient's glycaemia is evaluated with an objective metric (the blood glucose risk index), which has an established clinical relevance. The tuned controller is validated in the UVA/Padova environment where the resulting artificial pancreas achieves minimal glucose risk index in realistic 24-hour long scenarios with unannounced glucose intake.

Improving an open-source commercial system to reliably perform activity-dependent stimulation.

OBJECTIVE: Activity-dependent stimulation (ADS) is designed to strengthen the connections between neuronal circuits and therefore may be a promising tool for promoting neurophysiological reorganization following a brain injury. To successfully perform this technique, two criteria must be met: (1) spikes in the extracellular electrical field potential must be detected accurately at one site of interest, and (2) stimulation pulses generated at fixed (<1 ms jitter), low-latency (<10 ms) intervals relative to each detected spike must be delivered reliably to a second site of interest. Here, we aimed to improve noise rejection in a low-cost commercial system to reliably perform ADS in awake, behaving rats, while maintaining latency requirements. APPROACH: We implemented a spike detection state machine on a field-programmable gate array (FPGA). Because the accuracy of spike detection can be heavily reduced in awake and behaving animals due to biological artifacts such as movement and chewing, the state machine tracks candidate spike waveforms, checking them against multiple programmable thresholds and rejecting any spikes that fail to meet a programmed threshold criterion. MAIN RESULTS: A series of offline analyses showed that our implementation was able to appropriately trigger stimulation during epochs of biological artifacts with an overall accuracy between 72% and 97%, fixed computational latency of 167 µs, and an algorithmic latency of 300 µs to 800 µs. SIGNIFICANCE: Our improvements have been made open-source and are freely available to all scientists working on closed-loop neuroprosthetic devices. Importantly, the improvements are easily incorporated into existing workflows that utilize the Intan Stimulation and Recording Controller.

A Neuromorphic Prosthesis to Restore Communication in Neuronal Networks.

Recent advances in bioelectronics and neural engineering allowed the development of brain machine interfaces and neuroprostheses, capable of facilitating or recovering functionality in people with neurological disability. To realize energy-efficient and real-time capable devices, neuromorphic computing systems are envisaged as the core of next-generation systems for brain repair. We demonstrate here a real-time hardware neuromorphic prosthesis to restore bidirectional interactions between two neuronal populations, even when one is damaged or missing. We used in vitro modular cell cultures to mimic the mutual interaction between neuronal assemblies and created a focal lesion to functionally disconnect the two populations. Then, we employed our neuromorphic prosthesis for bidirectional bridging to artificially reconnect two disconnected neuronal modules and for hybrid bidirectional bridging to replace the activity of one module with a real-time hardware neuromorphic Spiking Neural Network. Our neuroprosthetic system opens avenues for the exploitation of neuromorphic-based devices in bioelectrical therapeutics for health care.

An IC-based controllable stimulator for respiratory muscle stimulation investigations.

Functional Electrical Stimulation can be used to restore motor functions loss consecutive to spinal cord injury, such as respiratory deficiency due to paralysis of ventilatory muscles. This paper presents a fully configurable IC-centered stimulator designed to investigate muscle stimulation paradigms. It provides 8 current stimulation channels with high-voltage compliance and real-time operation capabilities, to enable a wide range of FES applications. The stimulator can be used in a standalone mode, or within a closed-loop setup. Primary in vivo results show successful drive of respiratory muscles stimulation using a computer-based dedicated controller.

Bio-Inspired Controller on an FPGA Applied to Closed-Loop Diaphragmatic Stimulation.

Cervical spinal cord injury can disrupt connections between the brain respiratory network and the respiratory muscles which can lead to partial or complete loss of ventilatory control and require ventilatory assistance. Unlike current open-loop technology, a closed-loop diaphragmatic pacing system could overcome the drawbacks of manual titration as well as respond to changing ventilation requirements. We present an original bio-inspired assistive technology for real-time ventilation assistance, implemented in a digital configurable Field Programmable Gate Array (FPGA). The bio-inspired controller, which is a spiking neural network (SNN) inspired by the medullary respiratory network, is as robust as a classic controller while having a flexible, low-power and low-cost hardware design. The system was simulated in MATLAB with FPGA-specific constraints and tested with a computational model of rat breathing; the model reproduced experimentally collected respiratory data in eupneic animals. The open-loop version of the bio-inspired controller was implemented on the FPGA. Electrical test bench characterizations confirmed the system functionality. Open and closed-loop paradigm simulations were simulated to test the FPGA system real-time behavior using the rat computational model. The closed-loop system monitors breathing and changes in respiratory demands to drive diaphragmatic stimulation. The simulated results inform future acute animal experiments and constitute the first step toward the development of a neuromorphic, adaptive, compact, low-power, implantable device. The bio-inspired hardware design optimizes the FPGA resource and time costs while harnessing the computational power of spike-based neuromorphic hardware. Its real-time feature makes it suitable for in vivo applications.

Generation of Locomotor-Like Activity in the Isolated Rat Spinal Cord Using Intraspinal Electrical Microstimulation Driven by a Digital Neuromorphic CPG.

Neural prostheses based on electrical microstimulation offer promising perspectives to restore functions following lesions of the central nervous system (CNS). They require the identification of appropriate stimulation sites and the coordination of their activation to achieve the restoration of functional activity. On the long term, a challenging perspective is to control microstimulation by artificial neural networks hybridized to the living tissue. Regarding the use of this strategy to restore locomotor activity in the spinal cord, to date, there has been no proof of principle of such hybrid approach driving intraspinal microstimulation (ISMS). Here, we address a first step toward this goal in the neonatal rat spinal cord isolated ex vivo, which can display locomotor-like activity while offering an easy access to intraspinal circuitry. Microelectrode arrays were inserted in the lumbar region to determine appropriate stimulation sites to elicit elementary bursting patterns on bilateral L2/L5 ventral roots. Two intraspinal sites were identified at L1 level, one on each side of the spinal cord laterally from the midline and approximately at a median position dorso-ventrally. An artificial CPG implemented on digital integrated circuit (FPGA) was built to generate alternating activity and was hybridized to the living spinal cord to drive electrical microstimulation on these two identified sites. Using this strategy, sustained left-right and flexor-extensor alternating activity on bilateral L2/L5 ventral roots could be generated in either whole or thoracically transected spinal cords. These results are a first step toward hybrid artificial/biological solutions based on electrical microstimulation for the restoration of lost function in the injured CNS.

A novel bioelectronic glucose sensor to process distinct electrical activities of pancreatic beta-cells.

Glucose sensors have improved and facilitated therapy for type 1 diabetes. However, they are still not capable to sense all physiological signals and to act in a closed-loop. Pancreatic ß-cells have been shaped during evolution as biological sensors and offer the advantage to integrate all physiological signals in addition to glucose. Moreover, biosensors based on these cells may also serve for non-invasive and continuous long-term characterization of ß-cells, drug research, tissue engineering and pre-transplantation quality control. ß-cells alter their electrical activity upon exposure to glucose and physiological hormones and we have used these properties to design a biosensor. To this end signals were recorded extracellularly from islet cells kept on multi-electrode arrays. Slow and rapid oscillations were observed, both modulated by glucose. Especially slow oscillations are very robust and have an excellent signal/noise ratio. Signal processing functions were designed to separate the two activities to extract and analyze relevant parameters. These parameters correlate very well with either increasing or decreasing glucose concentrations. An electronic device is under construction, based on an embedded FPGA capable of processing multiple channels in parallel. In the future, such a device shall be used as a portable real-time biosensor regulating insulin delivery from a pump.

In vitro large-scale experimental and theoretical studies for the realization of bi-directional brain-prostheses.

Brain-machine interfaces (BMI) were born to control "actions from thoughts" in order to recover motor capability of patients with impaired functional connectivity between the central and peripheral nervous system. The final goal of our studies is the development of a new proof-of-concept BMI-a neuromorphic chip for brain repair-to reproduce the functional organization of a damaged part of the central nervous system. To reach this ambitious goal, we implemented a multidisciplinary "bottom-up" approach in which in vitro networks are the paradigm for the development of an in silico model to be incorporated into a neuromorphic device. In this paper we present the overall strategy and focus on the different building blocks of our studies: (i) the experimental characterization and modeling of "finite size networks" which represent the smallest and most general self-organized circuits capable of generating spontaneous collective dynamics; (ii) the induction of lesions in neuronal networks and the whole brain preparation with special attention on the impact on the functional organization of the circuits; (iii) the first production of a neuromorphic chip able to implement a real-time model of neuronal networks. A dynamical characterization of the finite size circuits with single cell resolution is provided. A neural network model based on Izhikevich neurons was able to replicate the experimental observations. Changes in the dynamics of the neuronal circuits induced by optical and ischemic lesions are presented respectively for in vitro neuronal networks and for a whole brain preparation. Finally the implementation of a neuromorphic chip reproducing the network dynamics in quasi-real time (10 ns precision) is presented.

Wavelet transform for real-time detection of action potentials in neural signals.

We present a study on wavelet detection methods of neuronal action potentials (APs). Our final goal is to implement the selected algorithms on custom integrated electronics for on-line processing of neural signals; therefore we take real-time computing as a hard specification and silicon area as a price to pay. Using simulated neural signals including APs, we characterize an efficient wavelet method for AP extraction by evaluating its detection rate and its implementation cost. We compare software implementation for three methods: adaptive threshold, discrete wavelet transform (DWT), and stationary wavelet transform (SWT). We evaluate detection rate and implementation cost for detection functions dynamically comparing a signal with an adaptive threshold proportional to its SD, where the signal is the raw neural signal, respectively: (i) non-processed; (ii) processed by a DWT; (iii) processed by a SWT. We also use different mother wavelets and test different data formats to set an optimal compromise between accuracy and silicon cost. Detection accuracy is evaluated together with false negative and false positive detections. Simulation results show that for on-line AP detection implemented on a configurable digital integrated circuit, APs underneath the noise level can be detected using SWT with a well-selected mother wavelet, combined to an adaptive threshold.

Large-scale, high-resolution data acquisition system for extracellular recording of electrophysiological activity.

A platform for high spatial and temporal resolution electrophysiological recordings of in vitro electrogenic cell cultures handling 4096 electrodes at a full frame rate of 8 kHz is presented and validated by means of cardiomyocyte cultures. Based on an active pixel sensor device implementing an array of metallic electrodes, the system provides acquisitions at spatial resolutions of 42 microm on an active area of 2.67 mm x 2.67 mm, and in the zooming mode, temporal resolutions down to 8 micros on 64 randomly selected electrodes. The low-noise performances of the integrated amplifier (11 microV (rms)) combined with a hardware implementation inspired by image/video processing concepts enable high-resolution acquisitions with real-time preprocessing capabilities adapted to the handling of the large amount of acquired data.

Analog-digital simulations of full conductance-based networks of spiking neurons with spike timing dependent plasticity.

We introduce and test a system for simulating networks of conductance-based neuron models using analog circuits. At the single-cell level, we use custom-designed analog circuits (ASICs) that simulate two types of spiking neurons based on Hodgkin-Huxley like dynamics: "regular spiking" excitatory neurons with spike-frequency adaptation, and "fast spiking" inhibitory neurons. Synaptic interactions are mediated by conductance-based synaptic currents described by kinetic models. Connectivity and plasticity rules are implemented digitally through a real time interface between a computer and a PCI board containing the ASICs. We show a prototype system of a few neurons interconnected with synapses undergoing spike-timing dependent plasticity (STDP), and compare this system with numerical simulations. We use this system to evaluate the effect of parameter dispersion on the behavior of small circuits of neurons. It is shown that, although the exact spike timings are not precisely emulated by the ASIC neurons, the behavior of small networks with STDP matches that of numerical simulations. Thus, this mixed analog-digital architecture provides a valuable tool for real-time simulations of networks of neurons with STDP. They should be useful for any real-time application, such as hybrid systems interfacing network models with biological neurons.