Uncovering neural architectures enabling robust and flexible motor control in Drosophila

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In crowding, perception of a target deteriorates in the presence of nearby elements. As the entire stimulus configuration across large parts of the visual field influences crowding and not just nearby elements, low-level explanations, such as local pooling ...

Implantable neural interfaces with the central and peripheral nervous systems are currently used to restore sensory, motor, and cognitive functions in disabled people with very promising results. They have also been used to modulate autonomic activities to ...

Locomotion is based on a sophisticated interaction among the environment, the musculoskeletal system, the spinal cord, and the brain locomotor areas. Quality of life is strongly related to the proper capability of this movement. However, many pathologies, ...

The rapid progress in the field of deep learning has had a significant impact on protein design. Deep learning methods have recently produced a breakthrough in protein structure prediction, leading to the availability of high-quality models for millions of ...

In this work, we introduce and study a class of Deep Neural Networks (DNNs) in continuous-time. The proposed architecture stems from the combination of Neural Ordinary Differential Equations (Neural ODEs) with the model structure of recently introduced Rec ...

Deep Neural Networks (DNNs) have achieved great success in a wide range of applications, such as image recognition, object detection, and semantic segmentation. Even though the discriminative power of DNNs is nowadays unquestionable, serious concerns have ...

Animals both explore and avoid novel objects in the environment, but the neural mechanisms that underlie these behaviors and their dynamics remain uncharacterized. Here, we used multi-point tracking (DeepLabCut) and behavioral segmentation (MoSeq) to chara ...

Signal recovery under generative neural network priors has emerged as a promising direction in statistical inference and computational imaging. Theoretical analysis of reconstruction algorithms under generative priors is, however, challenging. For generati ...

Implicit neural representations (INRs) have recently emerged as a promising alternative to classical discretized representations of signals. Nevertheless, despite their practical success, we still do not understand how INRs represent signals. We propose a ...

The automatic recognition of food on images has numerous interesting applications, including nutritional tracking in medical cohorts. The problem has received significant research attention, but an ongoing public benchmark on non-biased (i.e., not scraped ...

Motivated by the need for a better understanding of post-stroke recovery and new biomarkers to improve stroke patient stratification and outcomes, this thesis investigated structure-function coupling and its role in post-stroke recovery. Furthermore, in or ...

Cancer diagnosis, prognosis, and therapy response predictions from tissue specimens highly depend on the phenotype and topological distribution of constituting histological entities. Thus, adequate tissue representations for encoding histological entities ...

Over the past decade, interatomic potentials based on machine learning (ML) techniques have become an indispensable tool in the atomic-scale modeling of materials. Trained on energies and forces obtained from electronic-structure calculations, they inherit ...

In macaque monkeys, dorsal intraparietal areas are involved in several daily visuomotor actions. However, their border and sources of cortical afferents remain loosely defined. Combining retrograde histologic tracing and MRI diffusion-based tractography, w ...

In past years, we have witnessed the fields of geosciences and remote sensing and artificial intelligence (AI) become closer. Thanks to the massive availability of observational data, improved simulations, and algorithmic advances, these disciplines have f ...

In order to perform network analysis tasks, representations that capture the most relevant information in the graph structure are needed. However, existing methods learn representations that cannot be interpreted in a straightforward way and that are relat ...

Conventional sampling focuses on encoding and decoding bandlimited signals by recording signal amplitudes at known time points. Alternately, sampling can be approached using biologically-inspired schemes. Among these are integrate-and-fire time encoding ma ...

Uncovering neural architectures enabling robust and flexible motor control in Drosophila

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Does surface completion fail to support uncrowding?

Organ Neuroprosthetics: Connecting Transplanted and Artificial Organs with the Nervous System

Investigating the neuromechanical control of healthy gait modulation and pathological gaits observed in cerebral palsy using neuromuscular simulations

A new age in protein design empowered by deep learning

Unconstrained Parametrization of Dissipative and Contracting Neural Ordinary Differential Equations

Understanding Deep Neural Networks using Adversarial Attacks

Striatal dopamine explains novelty-induced behavioral dynamics and individual variability in threat prediction

Multi-layer State Evolution Under Random Convolutional Design

A Structured Dictionary Perspective on Implicit Neural Representations

The Food Recognition Benchmark: Using Deep Learning to Recognize Food in Images

Determining patterns of post-stroke motor recovery through longitudinal multimodal MRI: A step towards patient stratification

Hierarchical graph representations in digital pathology

Beyond potentials: Integrated machine learning models for materials

The Complex Hodological Architecture of the Macaque Dorsal Intraparietal Areas as Emerging from Neural Tracers and DW-MRI Tractography

Toward a Collective Agenda on AI for Earth Science Data Analysis

node2coords: Graph Representation Learning with Wasserstein Barycenters

Encoding And Decoding Mixed Bandlimited Signals Using Spiking Integrate-And-Fire Neurons