Carbon nanotubes might improve neuronal performance by favouring electrical shortcuts

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Living photovoltaics are microbial electrochemical devices that use whole cell–electrode interactions to convert solar energy to electricity. The bottleneck in these technologies is the limited electron transfer between the microbe and the electrode surfac ...

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 ...

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 ...

Transformer models such as GPT generate human-like language and are predictive of human brain responses to language. Here, using functional-MRI-measured brain responses to 1,000 diverse sentences, we first show that a GPT-based encoding model can predict t ...

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 ...

The present invention concerns a bodily implantable or probe device and microelectrode fabrication method comprising providing at least one silicon substrate including an electronic device or unit; providing, on a first side of the silicon substrate, at le ...

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 ...

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 ...

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 ...

In the realm of point cloud scene understanding, particularly in indoor scenes, objects are arranged following human habits, resulting in objects of certain semantics being closely positioned and displaying notable inter-object correlations. This can creat ...

The invention of 3D atomic force microscopy (3D-AFM) has enabled visualizing subnanoscale 3D hydration structures. Meanwhile, its applications to imaging flexible molecular chains have started to be experimentally explored. However, the validity and princi ...

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 ...

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

2

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

Neural decoding of the visual system is a subject of research interest, both to understand how the visual system works and to be able to use this knowledge in areas, such as computer vision or brain-computer interfaces. Spike-based decoding is often used, ...

The remarkable adaptability observed in marine ecosystems has often inspired researchers when developing new soft materials. The research undertaken in this thesis explores how ion chelator pair interactions influence the mechanical properties of bioinspir ...

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 ...

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 ...

Cyclic GMP-AMP synthase (cGAS) senses aberrant DNA during infection, cancer and inflammatory disease, and initiates potent innate immune responses through the synthesis of 2 ' 3 '-cyclic GMP-AMP (cGAMP)1-7. The indiscriminate activity of cGAS towards DNA d ...

Herein, machine learning (ML) models using multiple linear regression (MLR), support vector regression (SVR), random forest (RF) and artificial neural network (ANN) are developed and compared to predict the output features viz. specific capacitance (Csp), ...

In this paper, we study sampling from a posterior derived from a neural network. We propose a new probabilistic model consisting of adding noise at every pre- and post-activation in the network, arguing that the resulting posterior can be sampled using an ...

Carbon nanotubes might improve neuronal performance by favouring electrical shortcuts

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Polydopamine-coated photoautotrophic bacteria for improving extracellular electron transfer in living photovoltaics

Subcortical correlates of consciousness with human single neuron recordings

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

Driving and suppressing the human language network using large language models

Fast adaptation to rule switching using neuronal surprise

Bodily implant microelectrode and bodily implant microelectrode fabrication method

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

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

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

Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange

Revealing the Mechanism Underlying 3D-AFM Imaging of Suspended Structures by Experiments and Simulations

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

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Predicting Visual Stimuli From Cortical Response Recorded With Wide-Field Imaging in a Mouse

From Surfactants to 3D Printing: How Ion-Chelator Pair Interactions Affect the Mechanical Properties of Bioinspired Materials

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

The CRL5-SPSB3 ubiquitin ligase targets nuclear cGAS for degradation

Machine learning models for prediction of electrochemical properties in supercapacitor electrodes using MXene and graphene nanoplatelets

Gibbs sampling the posterior of neural networks