Integrating Neural Interfaces with Machine Learning: Enabling Self-Sufficient and Ubiquitous Prosthetic and Diagnostic Devices

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Transforaminal lumbar interbody fusion (TLIF) is a commonly used technique for treating lumbar degenerative diseases. In this study, we developed a fully computer-supported pipeline to predict both the cage height and the degree of lumbar lordosis subtract ...

Complex interactions between brain regions and the spinal cord (SC) govern body motion, which is ultimately driven by muscle activation. Motor planning or learning are mainly conducted at higher brain regions, whilst the SC acts as a brain-muscle gateway a ...

Cortical populations of neurons develop sparse representations adapted to the statistics of the environment. To learn efficient population codes, synaptic plasticity mechanisms must differentiate relevant latent features from spurious input correlations, w ...

Detailed population maps play an important role in diverse fields ranging from humanitarian action to urban planning. Generating such maps in a timely and scalable manner presents a challenge, especially in datascarce regions. To address it we have develop ...

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimen ...

Species distribution modeling is a highly versatile tool for understanding the intricate relationship between environmental conditions and species occurrences. However, the available data often lacks information on confirmed species absence and is limited ...

"Ensure healthy lives and promote well-being for all at all ages" is the third sustainable development goal for the United Nations Agenda of 2030. This doctoral thesis fully embodied this objective by targeting stroke, a leading cause of death and disabili ...

Random Fourier features (RFFs) provide a promising way for kernel learning in a spectral case. Current RFFs-based kernel learning methods usually work in a two-stage way. In the first-stage process, learn-ing an optimal feature map is often formulated as a ...

Electrochemical batteries are ubiquitous devices in our society. When employed in mission-critical applications, the ability to precisely predict their end-of-discharge under highly variable operating conditions is of paramount importance in order to suppo ...

Self-supervised learning (SSL) models use only the intrinsic structure of a given signal, independent of its acoustic domain, to extract essential information from the input to an embedding space. This implies that the utility of such representations is no ...

An automatic seizure detection method from high-resolution intracranial-EEG (iEEG) signals is presented to minimize the computational complexity and realize real-time accurate seizure detection for biomedical implants. Complex signal processing on a large ...

The synthesis of metal-organic frameworks (MOFs) is often complex and the desired structure is not always obtained. In this work, we report a methodology that uses a joint machine learning and experimental approach to optimize the synthesis conditions of A ...

In this dissertation, I develop theory and evidence to argue that new technologies are central to how firms organize to create and capture value. I use computational methods such as reinforcement learning and probabilistic topic modeling to investigate thr ...

In studies of the visual system as well as in computer vision, the focus is often on contrast edges. However, the primate visual system contains a large number of cells that are insensitive to spatial contrast and, instead, respond to uniform homogeneous i ...

It has been experimentally observed that the efficiency of distributed training with stochastic gradient (SGD) depends decisively on the batch size and—in asynchronous implementations—on the gradient staleness. Especially, it has been observed that the spe ...

Background: The increasingly common applications of machine-learning schemes to atomic-scale simulations have triggered efforts to better understand the mathematical properties of the mapping between the Cartesian coordinates of the atoms and the variety o ...

In recent years, the importance of electric mobility has increased in response to climate change. The fast-growing deployment of electric vehicles (EVs) worldwide is expected to decrease transportation-related emissions, facilitate the integration of renew ...

Objective.Mobile Brain/Body Imaging (MoBI) frameworks allowed the research community to find evidence of cortical involvement at walking initiation and during locomotion. However, the decoding of gait patterns from brain signals remains an open challenge. ...

Whether it occurs in artificial or biological substrates, {\it learning} is a {distributed} phenomenon in at least two aspects. First, meaningful data and experiences are rarely found in one location, hence {\it learners} have a strong incentive to work t ...

Organisms use rewards to navigate and adapt to (uncertain) environments. Error-based learning about rewards issupported by the dopaminergic system, which is thought to signal reward prediction errors to make adjustmentsto past predictions. More recently, t ...

Integrating Neural Interfaces with Machine Learning: Enabling Self-Sufficient and Ubiquitous Prosthetic and Diagnostic Devices

Chattez avec Graph Search

Development of End-to-End Artificial Intelligence Models for Surgical Planning in Transforaminal Lumbar Interbody Fusion

The spinal cord facilitates cerebellar upper limb motor learning and control; inputs from neuromusculoskeletal simulation

Learning what matters: Synaptic plasticity with invariance to second-order input correlations

High-resolution population maps derived from Sentinel-1 and Sentinel-2

The multimodality cell segmentation challenge: toward universal solutions

On the selection and effectiveness of pseudo-absences for species distribution modeling with deep learning

Combination of Neurotechnologies Towards Personalized Neurorehabilitation for Stroke Patients

End-to-end kernel learning via generative random Fourier features

Ageing-aware battery discharge prediction with deep learning

Can Self-Supervised Neural Representations Pre-Trained on Human Speech distinguish Animal Callers?

Computation Complexity Reduction Technique for Accurate Seizure Detection Implants

Using genetic algorithms to systematically improve the synthesis conditions of Al-PMOF

Technology Strategy in Dynamic Environments: A Computational Analysis of the Automation of Routines, the Organization of AI, and the Evaluation of Technology Risk

Feed-forward and noise-tolerant detection of feature homogeneity in spiking networks with a latency code

Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates

Local invertibility and sensitivity of atomic structure-feature mappings

Deep reinforcement learning control of electric vehicle charging in the presence of photovoltaic generation

Deep learning-based BCI for gait decoding from EEG with LSTM recurrent neural network

Robust Distributed Learning

Risk prediction error signaling: A two-component response?