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Introduction La technologie des implants cérébraux promet des applications de type interfaces cerveau-ordinateur (« Brain Computer-Interfaces », BCI), pour aider au quotidien des personnes atteintes de certaines paralysies sévères. Il s’agit d’une technologie intracraniale, donc invasive, pour enregistrer des signaux provenant de l’activité électrique des neurones et, éventuellement, délivrer des stimulations électriques pour engendrer des activités. Le procédé consiste à introduire, dans le ...

Neuroprosthetic technology aims to restore nervous system functionality in cases of severe damage or degeneration by recording and stimulating the electrical activity of the neural tissue. One of the key factors determining the quality of the neuroprostheses is the electrode material used to establish electrical communication with the neural tissue, which is subject to strict electrical, electrochemical, and mechanical specifications as well as biological and microfabrication compatibility requirements. This work presents a nanoporous graphene-based thin film technology and its engineering to form flexible neural implants. Bench measurements show that the developed microelectrodes offer low impedance and high charge injection capacity throughout millions of pulses. In vivo electrode performance was assessed in rodents both from brain surface and intracortically showing high-fidelity recording performance, while stimulation performance was assessed with an intrafascicular implant that demonstrated low current thresholds and high selectivity for activating subsets of axons within the sciatic nerve. Furthermore, the tissue biocompatibility of the devices was validated by chronic epicortical and intraneural implantation. Overall, this works describes a novel graphene-based thin film microelectrode technology and demonstrates its potential for high-precision neural interfacing in both recording and stimulation applications.

BACKGROUND Investigating brain dynamics underlying vocal production in animals is a powerful way to inform on the neural bases of human speech. In particular, brain networks underlying vocal production in non-human primates show striking similarities with the human speech production network. However, despite increasing findings also in birds and more recently in rodents, the extent to which the primate vocal cortical network model generalizes to other non-primate mammals remains unclear. Especially, no domestic species has yet been proposed to investigate vocal brain activity using electrophysiological approaches. NEW METHOD In the present study, we introduce a novel experimental paradigm to identify the cortical dynamics underlying vocal production in behaving minipigs. A key problem to chronically implant cortical probes in pigs is the presence and growth of frontal sinuses extending caudally to the parietal bone and preventing safe access to neural structures with conventional craniotomy in adult animals. RESULTS Here we first show that implantations of soft ECoG grids can be done safely using conventional craniotomy in minipigs younger than 5 months, a period when sinuses are not yet well developed. Using wireless recordings in behaving animals, we further show activation of the motor and premotor cortex around the onset of vocal production of grunts, the most common vocalization of pigs. CONCLUSION These results suggest that minipigs, which are very loquacious and social animals, can be a good experimental large animal model to study the cortical bases of vocal production.

Local field potentials (LFPs) have better long-term stability compared with spikes in brain-machine interfaces (BMIs). Many studies have shown promising results of LFP decoding, but the high-dimensional feature of LFP still hurdle the development of the BMIs to low-cost. In this paper, we proposed a framework of a 1D convolution neural network (CNN) to reduce the dimensionality of the LFP features. For evaluating the performance of this architecture, the reduced LFP features were decoded to cursor position (Center-out task) by a Kalman filter. The Principal components analysis (PCA) was also performed as a comparison. The results showed that the CNN model could reduce the dimensionality of LFP features to a smaller size without significant performance loss. The decoding result based on the CNN features outperformed that based on the PCA features. Moreover, the reduced features by CNN also showed robustness across different sessions. These results demonstrated that the LFP features reduced by the CNN model achieved low cost without sacrificing high-performance and robustness, suggesting that this method could be used for portable BMI systems in the future.

Intraoperative electrocorticography (ECoG) captures neural information from the surface of the cerebral cortex during surgeries such as resections for intractable epilepsy and tumors. Current clinical ECoG grids come in evenly spaced, millimeter‐sized electrodes embedded in silicone rubber. Their mechanical rigidity and fixed electrode spatial resolution are common shortcomings reported by the surgical teams. Here, advances in soft neurotechnology are leveraged to manufacture conformable subdural, thin‐film ECoG grids, and evaluate their suitability for translational research. Soft grids with 0.2 to 10 mm electrode pitch and diameter are embedded in 150 µm silicone membranes. The soft grids are compatible with surgical handling and can be folded to safely interface hidden cerebral surface such as the Sylvian fold in human cadaveric models. It is found that the thin‐film conductor grids do not generate diagnostic‐impeding imaging artefacts (, Current clinical electrocorticography (ECoGs) grids are made from stiff materials and only offer sparse brain sampling during epilepsy or tumor surgeries. Here conformable ECoG grids made from silicone membranes permit easy implantation in hidden anatomical locations in cadaveric specimen and imaging compatibility with 3T magnetic resonance imaging. In vivo in a minipig model the conformable ECoG grids show high‐resolution brain recordings.

Intracortical brain-computer interfaces (BCIs) can enable individuals to control effectors, such as a computer cursor, by directly decoding the user’s movement intentions from action potentials and local field potentials (LFPs) recorded within the motor cortex. However, the accuracy and complexity of effector control achieved with such “biomimetic” BCIs will depend on the degree to which the intended movements used to elicit control modulate the neural activity. In particular, channels that do not record distinguishable action potentials and only record LFP modulations may be of limited use for BCI control. In contrast, a biofeedback approach may surpass these limitations by letting the participants generate new control signals and learn strategies that improve the volitional control of signals used for effector control. Here, we show that, by using a biofeedback paradigm, three individuals with tetraplegia achieved volitional control of gamma LFPs (40–400 Hz) recorded by a single microelectrode implanted in the precentral gyrus. Control was improved over a pair of consecutive sessions up to 3 days apart. In all but one session, the channel used to achieve control lacked distinguishable action potentials. Our results indicate that biofeedback LFP-based BCIs may potentially contribute to the neural modulation necessary to obtain reliable and useful control of effectors. NEW & NOTEWORTHY Our study demonstrates that people with tetraplegia can volitionally control individual high-gamma local-field potential (LFP) channels recorded from the motor cortex, and that this control can be improved using biofeedback. Motor cortical LFP signals are thought to be both informative and stable intracortical signals and, thus, of importance for future brain-computer interfaces.

Neuroprosthetic speech devices are an emerging technology that can offer the possibility of communication to those who are unable to speak. Patients with ‘locked in syndrome,’ aphasia, or other such pathologies can use covert speech—vividly imagining saying something without actual vocalization—to trigger neural controlled systems capable of synthesizing the speech they would have spoken, but for their impairment.We provide an analysis of the mechanisms and outputs involved in speech mediated by neuroprosthetic devices. This analysis provides a framework for accounting for the ethical significance of accuracy, control, and pragmatic dimensions of prosthesis-mediated speech. We first examine what it means for the output of the device to be accurate, drawing a distinction between technical accuracy on the one hand and semantic accuracy on the other. These are conceptual notions of accuracy.Both technical and semantic accuracy of the device will be necessary (but not yet sufficient) for the user to have sufficient control over the device. Sufficient control is an ethical consideration: we place high value on being able to express ourselves when we want and how we want. Sufficient control of a neural speech prosthesis requires that a speaker can reliably use their speech apparatus as they want to, and can expect their speech to authentically represent them. We draw a distinction between two relevant features which bear on the question of whether the user has sufficient control: voluntariness of the speech and the authenticity of the speech. These can come apart: the user might involuntarily produce an authentic output (perhaps revealing private thoughts) or might voluntarily produce an inauthentic output (e.g., when the output is not semantically accurate). Finally, we consider the role of the interlocutor in interpreting the content and purpose of the communication.These three ethical dimensions raise philosophical questions about the nature of speech, the level of control required for communicative accuracy, and the nature of ‘accuracy’ with respect to both natural and prosthesis-mediated speech.

A current challenge of neurotechnologies is the development of speech brain-computer interfaces to restore communication in people unable to speak. To achieve a proof of concept of such system, neural activity of patients implanted for clinical reasons can be recorded while they speak. Using such simultaneously recorded audio and neural data, decoders can be built to predict speech features using features extracted from brain signals. A typical neural feature is the spectral power of field potentials in the high-gamma frequency band (between 70 and 200 Hz), a range that happens to overlap the fundamental frequency of speech. Here, we analyzed human electrocorticographic (ECoG) and intracortical recordings during speech production and perception as well as rat microelectrocorticographic (µ-ECoG) recordings during sound perception. We observed that electrophysiological signals, recorded with different recording setups, often contain spectrotemporal features highly correlated with those of the sound, especially within the high-gamma band. The characteristics of these correlated spectrotemporal features support a contamination of electrophysiological recordings by sound. In a recording showing high contamination, using neural features within the high-gamma frequency band dramatically increased the performance of linear decoding of acoustic speech features, while such improvement was very limited for another recording showing weak contamination. Further analysis and in vitro replication suggest that the contamination is caused by a mechanical action of the sound waves onto the cables and connectors along the recording chain, transforming sound vibrations into an undesired electrical noise that contaminates the biopotential measurements. This study does not question the existence of relevant physiological neural information underlying speech production or sound perception in the high-gamma frequency band, but alerts on the fact that care should be taken to evaluate and eliminate any possible acoustic contamination of neural signals in order to investigate the cortical dynamics of these processes.

International audience; Restoring communication in case of aphasia is a key challenge for neurotechnologies. To this end, brain-computer strategies can be envisioned to allow artificial speech synthesis from the continuous decoding of neural signals underlying speech imagination. Such speech brain-computer interfaces do not exist yet and their design should consider three key choices that need to be made: the choice of appropriate brain regions to record neural activity from, the choice of an appropriate recording technique, and the choice of a neural decoding scheme in association with an appropriate speech synthesis method. These key considerations are discussed here in light of (1) the current understanding of the functional neuroanatomy of cortical areas underlying overt and covert speech production, (2) the available literature making use of a variety of brain recording techniques to better characterize and address the challenge of decoding cor-tical speech signals, and (3) the different speech synthesis approaches that can be considered depending on the level of speech representation (phonetic, acoustic or articulatory) envisioned to be decoded at the core of a speech BCI paradigm.

Brain-computer interfaces and neural prostheses based on the detection of electrocorticography (ECoG) signals are rapidly growing fields of research. Several technologies are currently competing to be the first to reach the market; however, none of them fulfill yet all the requirements of the ideal interface with neurons. Thanks to its biocompatibility, low dimensionality, mechanical flexibility, and electronic properties, graphene is one of the most promising material candidates for neural interfacing. After discussing the operation of graphene solution-gated field-effect transistors (SGFET) and characterizing their performance in saline solution, it is reported here that this technology is suitable for μ-ECoG recordings through studies of spontaneous slow-wave activity, sensory-evoked responses on the visual and auditory cortices, and synchronous activity in a rat model of epilepsy. An in-depth comparison of the signal-to-noise ratio of graphene SGFETs with that of platinum black electrodes confirms that graphene SGFET technology is approaching the performance of state-of-the art neural technologies.

L'hybridation est une technique qui consiste a interconnecter un reseau de neurones biologiques et un reseau de neurones artificiels. Elle est notamment utilisee dans la recherche en neuroscience et a des fins therapeutiques. L'objectif a long-terme est de remplacer les reseaux de neurones endommages par des systemes artificiels. Ceux-ci requierent le developpement de modeles de neurones dont l'activite electrique est similaire a l'activite des reseaux biologiques vivants. Cette correspondance permet de produire une stimulation adequate afin de restaurer la fonction neurale desiree. Dans cet article, un reseau de neurones artificiels numeriques avec une architecture configurable a ete realise. Le reseau de neurones artificiels permet d'emuler l'activite de CPGs (Central Pattern Generator), a l'origine de la locomotion chez les animaux. Cette activite permet de declencher une serie de stimulations sur une moelle epiniere lesee et de recreer ainsi la locomotion precedemment perdue. Ces resultats sont une premiere etape vers des solutions hybrides artificiel / biologique basees sur la micro-stimulation electrique pour la restauration de fonctions du Systeme Nerveux Central (SNC).

Conference of 9th IEEE International Memory Workshop, IMW 2017 ; Conference Date: 14 May 2017 Through 17 May 2017; Conference Code:128262; International audience; In the last decade machine learning algorithms have proven unprecedented performance to solve many real-world detection and classification tasks, for example in image or speech recognition. Despite these advances, there are still some deficits. First, these algorithms require significant memory access thus ruling out an implementation using standard platforms (e.g. GPUs, FPGAS) for embedded applications. Second, most machine leaning algorithms need to be trained with huge data sets (supervised learning). Resistive memories (RRAM) have demonstrated to be a promising candidate to overcome both these constrains. RRAM arrays can act as a dot product accelerator, which is one of the main building blocks in neuromorphic computing systems. This approach could provide improvements in power and speed with respect to the GPU-based networks. Moreover RRAM devices are promising candidates to emulate synaptic plasticity, the capability of synapses to enhance or diminish their connectivity between neurons, which is widely believed to be the basis for learning and memory in the brain. Neural systems exhibit various types and time periods of plasticity, e.g. synaptic modifications can last anywhere from seconds to days or months. In this work we proposed an architecture that implements both Short-And Long-Term Plasticity rules (STP and LTP) using RRAM arrays. We showed the benefits of utilizing both kinds of plasticity with two different applications, visual pattern extraction and decoding of neural signals. LTP allows the neural networks to learn patterns without training data set (unsupervised learning), and STP makes the learning process very robust against environmental noise.

Spike sorting is a crucial step of neural data processing widely used in neuroscience and neuroprosthetics. However, current methods remain not fully automatic and require heavy computations making them not embeddable in implantable devices. To overcome these limitations, we propose a novel method based on an artificial spiking neural network designed to process neural data online and completely automatically. An input layer continuously encodes the data stream into artificial spike trains, which are then processed by two further layers to output artificial trains of spikes reproducing the real spiking activity present in the input signal. The proposed method can be adapted to process several channels simultaneously in the case of tetrode recordings. It outperforms two existing algorithms at low SNR and has the advantage to be compatible with neuromorphic computing and the perspective of being embedded in very low-power analog systems for future implantable devices serving neurorehabilitation applications.

This chapter describes an artificial synapse composed of multiple binary resistive RAM (RRAM) cells connected in parallel, thereby providing synaptic analog behavior. The vertical RRAM technology is presented as a possible solution to gain area by realizing one pillar per synapse. First, the proposed synapse has been used for the implementation of power efficient Convolutional Neural Networks for visual pattern recognition with supervised learning. This architecture is suitable for embedded hardware implementation in portable devices, thereby eliminating the latency to cloud access and avoiding the large energy cost per bit transmitted through the cloud. Second, the RRAM-based synapse coupled with unsupervised learning has been proposed to perform real-time decoding (spike sorting) of complex brain signals using a RRAM-based fully connected neural network. This approach coupled with brain-machine interfaces (BMIs) may enable the design of prosthetic devices that require to extract and decode in situ information from very large numbers of neurons without transmitting and processing this information offline.

Conference of 62nd IEEE International Electron Devices Meeting, IEDM 2016 ; Conference Date: 3 December 2016 Through 7 December 2016; Conference Code:126284; International audience; In this paper, we propose a new circuit architecture and a reading/programming strategy to emulate both Short and Long Term Plasticity (STP, LTP) rules using non-volatile OxRAM cells. For the first time, we show how the intrinsic OxRAM device switching probability at ultra-low power can be exploited to implement STP as well as LTP learning rules. Moreover, we demonstrate the computational power that STP can provide for reliable signal detection in highly noisy input data. A Fully Connected Neural Network incorporating STP and LTP learning rules is used to demonstrate two applications: (i) visual pattern extraction and (ii) decoding of neural signals. A high accuracy is obtained even in presence of significant background noise in the input data.