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

Méthodes et systèmes pour la détection adaptative et temps réel d'activité dans les signaux biologiques

L intéraction entre la biologie et l électronique est une discpline en pleine essort. De nom-breux systèmes électroniques tentent de s interconnecter avec des tissus ou des cellules vivantesa n de décoder l information biologique. Le Potentiel d action (PA) est au coeur de codagebiologique et par conséquent il est nécéssaire de pouvoir les repérer sur tout type de signal bio-logique. Par conséquent, nous étudions dans ce manuscrit la possibilité de concevoir un circuitélectronique couplé à un système de microélectrodes capable d e ectuer une acquisition, unedétection des PAs et un enregistrement des signaux biologiques. Que ce soit en milieu bruitéou non, nous considérons le taux de détection de PA et la contrainte de temps réel commedes notions primordiales et la consommation en silicium comme un prix à payer. Initialementdéveloppés pour l étude de signaux neuronaux et pancréatiques, ces systèmes conviennent par-faitement pour d autres type de cellules.Interaction between biology and electronic is in expansion. Many electronic systems aretrying to interconnect with tissues or living cells to decode biological information. The ActionPotential (AP) is the heart of biological coding and therefore it is necessary to be able to locateit from any type of biological signal. Therefore, we study in this manuscript the possibility ofdesigning an electronic circuit coupled to microelectrodes capable of acquisition, detection ofPAs and recording of biological signals. Whether or not in a noisy environment, we consider thedetection rate of PA and the real time-computing constraint as an hard speci cationand andsilicon area as a price to pay. Initially developed for the study of neural signals and pancreatic,these systems are ideal for other types of cells.BORDEAUX1-Bib.electronique (335229901) / SudocSudocFranceF

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

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

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

Réseaux de neurones sur Silicium : une approche mixte analogique / numérique pour l'étude des phénomènes d'adaptation, d'apprentissage et de plasticité

Dans un contexte où l'usage de circuits neuromimétiques se généralise au sein des neurosciences, nous étudions ici leur intégration au sein de réseaux adaptatifs. Les circuits mis en oeuvre se basent sur un modèle proche de la biologie résolu en continu et en temps réel. Les calculs relatifs à l'adaptation du réseau sont réalisés en numérique temps réel, logiciel et/ou matériel. La partie logicielle est assurée par un ordinateur interfacé à travers le bus PCI, tandis que la partie matérielle utilise des EPGAS. Trois générations sont présentés avec une analyse critique sur leur utilisation comme système de simulation de réseau neuronal.This work addresses the integration issues for neurominetic integrated circuits de-dicated to computationnal neuroscience, in the context of adaptive neural networks. Those circuits implement a biologically realistic model. This model is continuously solved in real time. the network adaptation is also computed in real time by digital means using sofware and or hardware material. The software part of the system is handled by a computer, interfaced to the system through the PCI bus, whereas the hardware part uses FPGAS. Three generations of the simulation system are presented, followed by a critical analysis on their performance as analog neural network simulators

Réseaux de neurones sur silicium : une approche mixte, analogique / numérique, pour l'étude des phénomènes d'adaptation, d'apprentissage et de plasticité

This work addresses the integration issues for neurominetic integrated circuits de-dicated to computationnal neuroscience, in the context of adaptive neural networks. Those circuits implement a biologically realistic model. This model is continuously solved in real time. the network adaptation is also computed in real time by digital means using sofware and or hardware material. The software part of the system is handled by a computer, interfaced to the system through the PCI bus, whereas the hardware part uses FPGAS. Three generations of the simulation system are presented, followed by a critical analysis on their performance as analog neural network simulators.Dans un contexte où l'usage de circuits neuromimétiques se généralise au sein des neurosciences, nous étudions ici leur intégration au sein de réseaux adaptatifs. Les circuits mis en oeuvre se basent sur un modèle proche de la biologie résolu en continu et en temps réel. Les calculs relatifs à l'adaptation du réseau sont réalisés en numérique temps réel, logiciel et/ou matériel. La partie logicielle est assurée par un ordinateur interfacé à travers le bus PCI, tandis que la partie matérielle utilise des EPGAS. Trois générations sont présentés avec une analyse critique sur leur utilisation comme système de simulation de réseau neuronal

Réseaux de neurones sur silicium (une approche mixte, analogique/numérique, pour l'étude des phénomènes d'adaptation, d'apprentissage et de plasticité)

Dans un contexte où l'usage de circuits neuromimétiques se généralise au sein des neurosciences, nous étudions ici leur intégration au sein de réseaux adaptatifs. Les circuits mis en oeuvre se basent sur un modèle proche de la biologie résolu en continu et en temps réel. Les calculs relatifs à l'adaptation du réseau sont réalisés en numérique temps réel, logiciel et/ou matériel. La partie logicielle est assurée par un ordinateur interfacé à travers le bus PCI, tandis que la partie matérielle utilise des FPGAs. Trois générations sont présentées avec une analyse critique sur leur utilisation comme système de simulation de réseau neuronalBORDEAUX1-BU Sciences-Talence (335222101) / SudocSudocFranceF

Optimizing devices and processing at the bio-electronic interface

International audienceElectronic devices for implants face specific challenges related to the biomedical context. We present state-of-the art and advanced IC architectures addressing these challenges for biosignal recording, rpocessing and control: low S/N and low-frequency signals, multiple sources, safety, adaptability, signature identification,electrodes matching, data compression, data transmission. Key issues related to neural and cardiac implants are presented. Recent advance on the electronic artifical pancreas are presented