A modeling framework to understand and optimize ultrasound neuromodulation

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Axons are ultrathin membrane cables that are specialized for the conduction of action potentials. Although their diameter is variable along their length, how their morphology is determined is unclear. Here, we demonstrate that unmyelinated axons of the mou ...

Pacinian corpuscles are among the most sensitive mechanoreceptors found in vertebrates, and they are tuned to vibrations in the highest perceptible frequency range (100 to 2000 Hz). One of their anatomical hallmarks is the onion-like cell layers surroundin ...

Proprioception tells the brain the state of the body based on distributed sensors in the body. However, the principles that govern proprioceptive processing from those distributed sensors are poorly understood. Here, we employ a task-driven neural network ...

Subcortical brain structures such as the basal ganglia or the thalamus are involved in regulating motor and cognitive behavior. However, their contribution to perceptual consciousness is still unclear, due to the inherent difficulties of recording subcorti ...

Cortical dynamics underlie many cognitive processes and emerge from complex multi-scale interactions, which are challenging to study in vivo. Large-scale, biophysically detailed models offer a tool which can complement laboratory approaches. We present a m ...

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

Despite the widespread empirical success of ResNet, the generalization properties of deep ResNet are rarely explored beyond the lazy training regime. In this work, we investigate scaled ResNet in the limit of infinitely deep and wide neural networks, of wh ...

Here we provide the neural data, activation and predictions for the best models and result dataframes of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the behavioral and neural experimental data (cu ...

Topographical disorientation refers to the selective inability to orient oneself in familiar surroundings. However, to date its neural correlates remain poorly understood. Here we use quantitative lesion analysis and a lesion network mapping approach in or ...

In humans and animals, surprise is a physiological reaction to an unexpected event, but how surprise can be linked to plausible models of neuronal activity is an open problem. We propose a self-supervised spiking neural network model where a surprise signa ...

Recently, cutting-edge brain-machine interfaces (BMIs) have revealed the potential of decoders such as recurrent neural networks (RNNs) in predicting attempted handwriting [1] or speech [2], enabling rapid communication recovery after paralysis. However, c ...

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

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-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens ...

The capabilities of deep learning systems have advanced much faster than our ability to understand them. Whilst the gains from deep neural networks (DNNs) are significant, they are accompanied by a growing risk and gravity of a bad outcome. This is troubli ...

This thesis presents an extensive exploration of neuroelectronic interfaces, focusing on microfabrication, in silico modeling, and their applications in designing and fabricating devices for neural interfacing. The research encompasses both peripheral nerv ...

Electrical stimulation of the visual nervous system could improve the quality of life of patients affected by acquired blindness by restoring some visual sensations, but requires careful optimization of stimulation parameters to produce useful perceptions. ...

In inverse problems, the task is to reconstruct an unknown signal from its possibly noise-corrupted measurements. Penalized-likelihood-based estimation and Bayesian estimation are two powerful statistical paradigms for the resolution of such problems. They ...

The rise of robotic body augmentation brings forth new developments that will transform robotics, human-machine interaction, and wearable electronics. Extra robotic limbs, although building upon restorative technologies, bring their own set of challenges i ...

The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

Information is transmitted between brain regions through the release of neurotransmitters from long-range projecting axons. Understanding how the activity of such long-range connections contributes to behavior requires efficient methods for reversibly mani ...

A modeling framework to understand and optimize ultrasound neuromodulation

Chattez avec Graph Search

Membrane mechanics dictate axonal pearls-on-a-string morphology and function

Lamellar Schwann Cells in the Pacinian Corpuscle Potentiate Vibration Perception

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Subcortical correlates of consciousness with human single neuron recordings

Modeling and simulation of neocortical micro- and mesocircuitry. Part II: Physiology and experimentation

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

The neural correlates of topographical disorientation-a lesion analysis study

Fast adaptation to rule switching using neuronal surprise

33.3 MiBMI: A 192/512-Channel 2.46mm² Miniaturized Brain-Machine Interface Chipset Enabling 31-Class Brain-to-Text Conversion Through Distinctive Neural Codes

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Safe Deep Neural Networks

Modeling, fabrication and validation of 3D neural interfaces for peripheral nerves and brain organoids

Decoding electroencephalographic responses to visual stimuli compatible with electrical stimulation

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Investigating neural resource allocation in the sensorimotor control of extra limbs

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

A bistable inhibitory optoGPCR for multiplexed optogenetic control of neural circuits