Neural assemblies as core elements for modeling neural networks in the brain

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Current progress in artificial intelligence is centered around so-called large language models that consist of neural networks processing long sequences of high-dimensional vectors called tokens. Statistical physics provides powerful tools to study the fun ...

Since the 1960s, the prevalence of pollen allergy has increased worldwide in developed countries and this trend is continuing, exacerbated by climate change. In the 20th century, operational pollen monitoring networks have been set-up to respond to the nee ...

This thesis is concerned with improving the interpretability of network summaries in both a theoretical and an applied framework, with an application to clinically relevant problems in neuroscience. The theoretical part of this thesis is concerned with gra ...

Background and Objective: Cardiovascular diseases, a leading cause of noncommunicable disease-related deaths, require early and accurate detection to improve patient outcomes. Taking advantage of advances in machine learning and deep learning, multiple app ...

Few organisms offer a window as precise and tractable into the exploration of biological form as Drosophila melanogaster. Yet the extraordinary natural variation in its brain morphology has remained largely uncharted, restrained by limitations in imaging, ...

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 ...

Recent studies show that a reproducing kernel Hilbert space (RKHS) is not a suitable space to model functions by neural networks as the curse of dimensionality (CoD) cannot be evaded when trying to approximate even a single ReLU neuron (Bach, 2017). In thi ...

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 ...

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 ...

Reasoning about distance is indispensable for establishing or avoiding contact in manipulation tasks. To this end, we present an online approach for learning implicit representations of signed distance using piecewise polynomial basis functions. Starting f ...

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 ...

The reported rate of the occurrence of unilateral spatial neglect (USN) is highly variable likely due to the lack of validity and low sensitivity of classical tools used to assess it. Virtual reality (VR) assessments try to overcome these limitations by pr ...

In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

This article summarizes the recent advancements in the design, fabrication, and control of microrobotic devices for the diagnosis and treatment of brain disorders. With a focus on diverse actuation methods, we discuss how advancements in materials science ...

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 ...

Reinforcement feedback can improve motor learning, but the underlying brain mechanisms remain underexplored. In particular, the causal contribution of specific patterns of oscillatory activity within the human striatum is unknown. To address this question, ...

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 ...

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 ...

Neural assemblies as core elements for modeling neural networks in the brain

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Bilinear Sequence Regression: A Model for Learning from Long Sequences of High-Dimensional Tokens

Automatic identification of atmospheric bioaerosol using real-time imaging and fluorescence

Nonparametric network summaries

FADE: Forecasting for anomaly detection on ECG

Mapping Brain Morphology with Micro-CT and Topological Data Analysis: From Drosophila to General Applications

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

Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks

Subcortical correlates of consciousness with human single neuron recordings

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

Online Learning of Continuous Signed Distance Fields Using Piecewise Polynomials

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

An immersive virtual reality tool for assessing left and right unilateral spatial neglect

Random matrix methods for high-dimensional machine learning models

Small-scale robotic devices for medical interventions in the brain

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Non-invasive stimulation of the human striatum disrupts reinforcement learning of motor skills

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

Generalization of Scaled Deep ResNets in the Mean-Field Regime