Real-time biomimetic Central Pattern Generators in an FPGA for hybrid experiments

This investigation of the leech heartbeat neural network system led to the development of a low resources, real-time, biomimetic digital hardware for use in hybrid experiments. The leech heartbeat neural network is one of the simplest central pattern generators (CPG). In biology, CPG provide the rhythmic bursts of spikes that form the basis for all muscle contraction orders (heartbeat) and locomotion (walking, running, etc.). The leech neural network system was previously investigated and this CPG formalized in the Hodgkin–Huxley neural model (HH), the most complex devised to date. However, the resources required for a neural model are proportional to its complexity. In response to this issue, this article describes a biomimetic implementation of a network of 240 CPGs in an FPGA (Field Programmable Gate Array), using a simple model (Izhikevich) and proposes a new synapse model: activity-dependent depression synapse. The network implementation architecture operates on a single computation core. This digital system works in real-time, requires few resources, and has the same bursting activity behavior as the complex model. The implementation of this CPG was initially validated by comparing it with a simulation of the complex model. Its activity was then matched with pharmacological data from the rat spinal cord activity. This digital system opens the way for future hybrid experiments and represents an important step toward hybridization of biological tissue and artificial neural networks. This CPG network is also likely to be useful for mimicking the locomotion activity of various animals and developing hybrid experiments for neuroprosthesis development

SIMONE: a realistic neural network simulator to reproduce MEA-based recordings

International audienceContemporary multielectrode arrays (MEAs) used to record extracellular activity from neural tissues can deliver data at rates on the order of 100 Mbps. Such rates require efficient data compression and/or preprocessing algorithms implemented on an application specific integrated circuit (ASIC) close to the MEA. We present SIMONE (Statistical sIMulation Of Neuronal networks Engine), a versatile simulation tool whose parameters can be either fixed or defined by a probability distribution. We validated our tool by simulating data recorded from the first olfactory relay of an insect. Different key aspects make this tool suitable for testing the robustness and accuracy of neural signal processing algorithms (such as the detection, alignment, and classification of spikes). For instance, most of the parameters can be defined by a probabilistic distribution, then tens of simulations may be obtained from the same scenario. This is especially useful when validating the robustness of the processing algorithm. Moreover, the number of active cells and the exact firing activity of each one of them is perfectly known, which provides an easy way to test accuracy

Overt speech decoding from cortical activity: a comparison of different linear methods

IntroductionSpeech BCIs aim at reconstructing speech in real time from ongoing cortical activity. Ideal BCIs would need to reconstruct speech audio signal frame by frame on a millisecond-timescale. Such approaches require fast computation. In this respect, linear decoder are good candidates and have been widely used in motor BCIs. Yet, they have been very seldomly studied for speech reconstruction, and never for reconstruction of articulatory movements from intracranial activity. Here, we compared vanilla linear regression, ridge-regularized linear regressions, and partial least squares regressions for offline decoding of overt speech from cortical activity.MethodsTwo decoding paradigms were investigated: (1) direct decoding of acoustic vocoder features of speech, and (2) indirect decoding of vocoder features through an intermediate articulatory representation chained with a real-time-compatible DNN-based articulatory-to-acoustic synthesizer. Participant's articulatory trajectories were estimated from an electromagnetic-articulography dataset using dynamic time warping. The accuracy of the decoders was evaluated by computing correlations between original and reconstructed features.ResultsWe found that similar performance was achieved by all linear methods well above chance levels, albeit without reaching intelligibility. Direct and indirect methods achieved comparable performance, with an advantage for direct decoding.DiscussionFuture work will address the development of an improved neural speech decoder compatible with fast frame-by-frame speech reconstruction from ongoing activity at a millisecond timescale

Flexible Graphene Solution-Gated Field-Effect Transistors : Efficient Transducers for Micro-Electrocorticography

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

Etude expérimentale et modélisation de la stimulation électrique extracellulaire des réseaux de neurones avec des matrices de microélectrodes (MEA) (analyse des mécanismes sous-jacents et amélioration de la focalité spatiale des stimulations)

La stimulation électrique extracellulaire du système nerveux central avec des matrices de microélectrodes (MEAs) constitue un enjeu majeur en Neurosciences, tant en recherche fondamentale que clinique. Cette thèse a pour but de comprendre les mécanismes sous-tendant la stimulation extracellulaire des neurones. Une étude expérimentale montre que les stimulations monopolaires sont peu focales. Une étude computationnelle a consisté à développer un modèle éléments finis pour calculer de manière réaliste le potentiel extracellulaire généré par une stimulation. Grâce à ce modèle, une configuration d électrodes permettant de focaliser les étendues stimulées a été élaborée (brevet déposé). Enfin, un modèle, constitué d'un neurone compartimenté placé dans un champ de potentiel, a permis de comprendre plusieurs effets de la stimulation extracellulaire sur la réponse neuronale. Ce travail apporte une meilleure maîtrise des stimulations extracellulaires, en vue de leur utilisation pour l étude des phénomènes activité-dépendants impliqués dans la plasticité des réseaux de neurones et pour la mise au point de neuroprothèses efficaces.Extracellular electrical stimulation of the central nervous system using microelectrode arrays (MEAs) is currently a challenging stake in neuroscience, in the fields of both fundamental and clinical research. This thesis aims at understanding the mechanisms underlying the extracellular stimulation of neurons. An experimental study shows that monopolar stimulations are not focal. Through a computational study, a finite element model was developed and used for realistic computation of the extracellular potential created by a stimulation. Based on this model, a new electrode configuration is proposed to achieve focal spatial stimulations (patent pending). Finally, a model made of a compartmentalized neuron placed in an extracellular potential field was built and used to explain several effects of extracellular stimulation on the neural response. This work allows a better control of extracellular stimulation for their use to study activity-dependent phenomena underlying neural network plasticity and for the development of efficient neural prostheses.BORDEAUX1-BU Sciences-Talence (335222101) / SudocSudocFranceF

An Attention-Based Spiking Neural Network for Unsupervised Spike-Sorting

International audienceBio-inspired computing using artificial spiking neural networks promises performances outperforming currently available computational approaches. Yet, the number of applications of such networks remains limited due to the absence of generic training procedures for complex pattern recognition, which require the design of dedicated architectures for each situation. We developed a spike-timing-dependent plasticity (STDP) spiking neural network (SSN) to address spike-sorting, a central pattern recognition problem in neuroscience. This network is designed to process an extracellular neural signal in an online and unsupervised fashion. The signal stream is continuously fed to the network and processed through several layers to output spike trains matching the truth after a short learning period requiring only few data. The network features an attention mechanism to handle the scarcity of action potential occurrences in the signal, and a threshold adaptation mechanism to handle patterns with different sizes. This method outperforms two existing spike-sorting algorithms at low signal-to-noise ratio (SNR) and can be adapted to process several channels simultaneously in the case of tetrode recordings. Such attention-based STDP network applied to spike-sorting opens perspectives to embed neuromorphic processing of neural data in future brain implants

Influence of a neural tissue on the modeled (A) and experimental (B) potential fields.

<p>Current-controlled monopolar stimulations were delivered to the tissue (1 µA). A, Top: Top view of the finite element model including a neural tissue surrounded by the Ringer solution. Bottom: The potential field was computed for different values of the electrical conductivity <i>σ_tissue</i> of the neural tissue, while the conductivity of the Ringer solution remained unchanged (<i>σ_Ringer</i> = 1.65 S/m). B, Top: Inverted-microscope photograph of a hindbrain-spinal cord preparation on the MEA. Bottom: Experimental potential field distribution in the presence of a hindbrain-spinal cord preparation (square symbols), and modeled potential field (red curve) obtained with the finite element model equipped with Robin BCs. In this case, the surface conductances of the stimulation and ground electrodes (<i>g_stim</i> and <i>g_ground</i>) and the tissue conductivity <i>σ_tissue</i> were estimated to optimize the fit between modeled and experimental fields (R² = 0.989, p<0.0001). The estimated value of <i>σ_tissue</i> was 0.057 S/m.</p

Improvement of the focality of the potential field with a ground surface configuration.

<p>The normalized potential field (<i>V_norm</i>) is plotted along a line passing 50 µm over the electrodes (A), and mapped over the <i>z</i> = 50 µm horizontal plane (B). The offset values were: 0 mV for the monopolar configuration and the concentric bipolar configuration, and 0.027, 0.00044, 0.0000026, and 0 mV for the ground surface configuration, when using Robin boundary conditions with <i>g_GS</i> = 400, 4000, 40000 S/m², and infinite. The maps cover a distance of 1000 (respectively 250) µm on both sides of the stimulation electrode, in the <i>x</i> (respectively <i>y</i>) direction. The three electrode configurations presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0004828#pone-0004828-g003" target="_blank">Figure 3</a> are considered: Monopolar (M), Concentric Bipolar (CB) and Ground Surface (GS). For this latter configuration, the surface conductance <i>g_GS</i> was assigned four different values: 400, 4000, 40000 S/m², and infinite. We also characterized the focality obtained using non homogeneous Neumann BC (Equation 13), which corresponds to a very low conductance of the ground surface. For a nominal current of 1 µA, the maximum potential field was 1.99 mV 50 µm above the planar disk electrode in the case of a monopolar configuration. Panel C shows the equivalent current required to reach the same potential amplitude with each configuration.</p

Schematic representation of the electrode/medium interface.

<p>During an electrical stimulation, a potential drop occurs between the metal and the medium sides of the interface. This drop can be modeled by a Robin boundary condition, taking into account the surface conductance of the interface (<i>g</i>) and the electrical conductivity of the medium (<i>σ</i>). While the voltage on the metal side <i>V_metal</i> is uniform, the electrical potential in the medium side (<i>V</i>) is allowed to vary over the electrode surface. Please, note here that even in the absence of stimulation, a junction potential exists at the interface. However, in this paper, this electrochemical equilibrium potential is assumed to be constant during the short time of the stimulation, and only the variations of the potential difference at the interface during the stimulation are considered.</p