The touch and zap method for in vivo whole-cell patch recording of intrinsic and visual responses of cortical neurons and Glial cells

Whole-cell patch recording is an essential tool for quantitatively establishing the biophysics of brain function, particularly in vivo. This method is of particular interest for studying the functional roles of cortical glial cells in the intact brain, which cannot be assessed with extracellular recordings. Nevertheless, a reasonable success rate remains a challenge because of stability, recording duration and electrical quality constraints, particularly for voltage clamp, dynamic clamp or conductance measurements. To address this, we describe "Touch and Zap", an alternative method for whole-cell patch clamp recordings, with the goal of being simpler, quicker and more gentle to brain tissue than previous approaches. Under current clamp mode with a continuous train of hyperpolarizing current pulses, seal formation is initiated immediately upon cell contact, thus the "Touch". By maintaining the current injection, whole-cell access is spontaneously achieved within seconds from the cell-attached configuration by a self-limited membrane electroporation, or "Zap", as seal resistance increases. We present examples of intrinsic and visual responses of neurons and putative glial cells obtained with the revised method from cat and rat cortices in vivo. Recording parameters and biophysical properties obtained with the Touch and Zap method compare favourably with those obtained with the traditional blind patch approach, demonstrating that the revised approach does not compromise the recorded cell. We find that the method is particularly well-suited for whole-cell patch recordings of cortical glial cells in vivo, targeting a wider population of this cell type than the standard method, with better access resistance. Overall, the gentler Touch and Zap method is promising for studying quantitative functional properties in the intact brain with minimal perturbation of the cell's intrinsic properties and local network. Because the Touch and Zap method is performed semi-automatically, this approach is more reproducible and less dependent on experimenter technique

Impact de conductances synaptiques et intrinsèques sur les propriétés de décharge de neurones corticaux in vivo en conditions artificielles et fonctionnelles

Dans le système nerveux central, le neurone s'inscrit dans un réseau de neurones interconnectés. Chaque neurone va prendre en compte la multitude d'entrées synaptiques qu'il reçoit du réseau et décider ou non de renvoyer à son tour un message en fonction de ses propriétés propres. Dans cette thèse je me suis attaché à évaluer l'impact de deux conductances sur la décharge des neurones. La première est synaptique, il s'agit de l'inhibition shunante portée par les canaux GABAA et la deuxième est intrinsèque, elle est responsable du courant porté par les canaux BK. Pour répondre à cette question nous avons caractérisé, in vivo, la fonction de transfert Entrée/Sortie de neurones du cortex visuel et somato sensoriel de chats et de rats en réponse à des stimuli artificiels (saut de courant ou de conductance) et fonctionnels (stimulation visuelle). Le dynamic-clamp imposé en patch-clamp à l'aveugle en cellule entière nous a permit de simuler les conductances étudiées et de quantifier leur impact sur les fonctions de transferts neuronales. Nous avons pu montrer l'action à la fois linéaire et non linéaire de l'inhibition shuntante sans remplir les conditions présumées nécessaires dans les précédents travaux. De même, nous avons confirmé un rôle non intuitif de la conductance BK qui, bien que provoquant un courant hyperpolarisant, va agir en amplifiant la réponse neuronale. Pour répondre aux difficultés techniques de tels enregistrements, nous avons développé de nouvelles techniques, en particulier pour l'accès au milieu intracellulaire en patch clampPARIS-BIUP (751062107) / SudocPARIS-BIUSJ-Physique recherche (751052113) / SudocSudocFranceF

Strategies for mapping synaptic inputs on dendrites in vivo by combining two-photon microscopy, sharp intracellular recording and pharmacology

Uncovering the functional properties of individual synaptic inputs on single neurons is critical for understanding the computational role of synapses and dendrites. Previous studies combined whole-cell patch recording to load neurons with a fluorescent calcium indicator and two-photon imaging to map subcellular changes in fluorescence upon sensory stimulation. By hyperpolarizing the neuron below spike threshold, the patch electrode ensured that changes in fluorescence associated with synaptic events were isolated from those caused by back-propagating action potentials. This technique holds promise for determining whether the existence of unique cortical feature maps across different species may be associated with distinct wiring diagrams. However, the use of whole-cell patch for mapping inputs on dendrites is challenging in large mammals, due to brain pulsations and the accumulation of fluorescent dye in the extracellular milieu. Alternatively, sharp intracellular electrodes have been used to label neurons with fluorescent dyes, but the current passing capabilities of these high impedance electrodes may be insufficient to prevent spiking. In this study, we tested whether sharp electrode recording is suitable for mapping functional inputs on dendrites in the cat visual cortex. We compared three different strategies for suppressing visually evoked spikes: (1) hyperpolarization by intracellular current injection, (2) pharmacological blockade of voltage-gated sodium channels by intracellular QX-314, and (3) GABA iontophoresis from a perisomatic electrode glued to the intracellular electrode. We found that functional inputs on dendrites could be successfully imaged using all three strategies. However, the best method for preventing spikes was GABA iontophoresis with low currents (5 to 10 nA), which minimally affected the local circuit. Our methods advance the possibility of determining functional connectivity in preparations where whole-cell patch may be impractical

QPLayer: efficient differentiation of convex quadratic optimization

Optimization layers within neural network architectures have become increasingly popular for their ability to solve a wide range of machine learning tasks and to model domain-specific knowledge. However, designing optimization layers requires careful consideration as the underlying optimization problems might be infeasible during training. Motivated by applications in learning, control, and robotics, this work focuses on convex quadratic programming (QP) layers. The specific structure of this type of optimization layer can be efficiently exploited for faster computations while still allowing rich modeling capabilities. We leverage primal-dual augmented Lagrangian techniques for computing derivatives of both feasible and infeasible QPs. Not requiring feasibility allows, as a byproduct, for more flexibility in the QP to be learned. The effectiveness of our approach is demonstrated in a few standard learning experiments, obtaining three to ten times faster computations than alternative state-of-the-art methods while being more accurate and numerically robust. Along with these contributions, we provide an open-source C++ software package called QPLayer for efficiently differentiating convex QPs and which can be interfaced with modern learning frameworks

QPLayer: efficient differentiation of convex quadratic optimization

Optimization layers within neural network architectures have become increasingly popular for their ability to solve a wide range of machine learning tasks and to model domain-specific knowledge. However, designing optimization layers requires careful consideration as the underlying optimization problems might be infeasible during training. Motivated by applications in learning, control, and robotics, this work focuses on convex quadratic programming (QP) layers. The specific structure of this type of optimization layer can be efficiently exploited for faster computations while still allowing rich modeling capabilities. We leverage primal-dual augmented Lagrangian techniques for computing derivatives of both feasible and infeasible QPs. Not requiring feasibility allows, as a byproduct, for more flexibility in the QP to be learned. The effectiveness of our approach is demonstrated in a few standard learning experiments, obtaining three to ten times faster computations than alternative state-of-the-art methods while being more accurate and numerically robust. Along with these contributions, we provide an open-source C++ software package called QPLayer for efficiently differentiating convex QPs and which can be interfaced with modern learning frameworks

PROXQP: an Efficient and Versatile Quadratic Programming Solver for Real-Time Robotics Applications and Beyond

Convex Quadratic programming (QP) has become a core component in the modern engineering toolkit, particularly in robotics, where QP problems are legions, ranging from real-time whole-body controllers to planning and estimation algorithms. Many of those QPs need to be solved at high frequency. Meeting timing requirements requires taking advantage of as many structural properties as possible for the problem at hand. For instance, it is generally crucial to resort to warm-starting to exploit the resemblance of consecutive control iterations. While a large range of off-the-shelf QP solvers is available, only a few are suited to exploit problem structure and warm-starting capacities adequately. In this work, we propose the PROXQP algorithm, a new and efficient QP solver that exploits QP structures by leveraging primal-dual augmented Lagrangian techniques. For convex QPs, PROXQP features a global convergence guarantee to the closest feasible QP, an essential property for safe closedloop control. We illustrate its practical performance on various standard robotic and control experiments, including a real-world closed-loop model predictive control application. While originally tailored for robotics applications, we show that PROXQP also performs at the level of state of the art on generic QP problems, making PROXQP suitable for use as an off-the-shelf solver for regular applications beyond robotics