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

Related publications (21)

Uncovering neural architectures enabling robust and flexible motor control in Drosophila

Uncovering the neural architectures that enable robust and flexible motor control is a fundamental challenge in neuroscience. Understanding the neural circuitry organisation at a cellular level is crucial for advancing our knowledge and developing interven ...

EPFL2025

Does surface completion fail to support uncrowding?

Michael Herzog, Lisa Felicia Schwetlick

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 ...

Association for Research in Vision and Ophthalmology (ARVO)2025

Computational Imaging: a Multi-Facet Perspective

Sepand Kashani

Computational Imaging (CI) is the field concerned with reconstructing images of some quantity of interest from a finite number of measurements. It is pervasive in science and engineering problems by enabling the reconstruction of images from a variety of m ...

EPFL2025

Archimedean Oath: A reflection tool for responsible engineers

Ingrid Le Duc, Vittorio Rossi

This workshop, inspired by the Archimedean Oath, seeks to engage participants in critically reflecting on the responsibilities of engineers in society. Following the update of the content of the Oath to address contemporary challenges, the workshop aims at ...

European Society for Engineering Education (SEFI)2024

Isotropic Magnetic Field Probe with ICNIRP 2020 Frequency Shaping in the Band up to 400 MHz

German Augusto Ramirez Arroyave

We present the design and validation of an isotropic field probe with geometry and frequency response shaped to correctly weight the aggregate contribution of the various frequency components of a broadband magnetic field in the band 100 kHz - 400 MHz acco ...

IEEE2023

On the accuracy of the Conventional gait Model: Distinction between marker misplacement and soft tissue artefact errors

Kamiar Aminian, Arnaud Barré

There is a lack of knowledge about the accuracy of the Conventional Gait Model (CGM), compared to the true bone motion. Accuracy is hindered by both marker misplacement and soft-tissue artefact (STA). The effect of the lateral knee marker (KNE) misplacemen ...

ELSEVIER SCI LTD2023

A new age in protein design empowered by deep learning

Bruno Emanuel Ferreira De Sousa Correia, Michael Bronstein, Casper Alexander Goverde, Arne Schneuing, Hamed Khakzad, Ilia Igashov

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 ...

Cell Press2023

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

Andrea Di Russo

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, ...

EPFL2023

Using Animal Motion Capture to Learn Neural Representations

Semih Günel

Understanding behavior from neural activity is a fundamental goal in neuroscience. It has practical applications in building robust brain-machine interfaces, human-computer interaction, and assisting patients with neurological disabilities. Despite the eve ...

EPFL2022

Understanding Deep Neural Networks using Adversarial Attacks

Krishna Kanth Nakka

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 ...

EPFL2022

Multi-layer State Evolution Under Random Convolutional Design

Lenka Zdeborová, Florent Gérard Krzakala

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 ...

2022

A Structured Dictionary Perspective on Implicit Neural Representations

Gizem Yüce, Pascal Frossard, Guillermo Ortiz Jimenez, Beril Besbinar

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 ...

IEEE COMPUTER SOC2022

Hierarchical graph representations in digital pathology

Jean-Philippe Thiran, Guillaume Jaume

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 ...

ELSEVIER2022

Improving the Accuracy of Fracture Toughness Measurement in Burst Experiments

Guanyi Lu

Experimental studies suggest that the fracture toughness of rocks increases with the confining pressure. Among many methods to quantify this dependency, a so-called burst experiment (Abou-Sayed, 1978) may be the most widely applied in practice. Its thick w ...

SPRINGER WIEN2022

Selective attention involves a feature-specific sequential release from inhibitory gating

David Pascucci, Gijs Plomp

Selective attention is a fundamental cognitive mechanism that allows our brain to preferentially process relevant sensory information, while filtering out distracting information. Attention is thought to flexibly gate the communication of irrelevant inform ...

2022

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

Alexander Mathis, Mackenzie Mathis

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 ...

CELL PRESS2022

System-level, input-output and new parameterizations of stabilizing controllers, and their numerical computation

Maryam Kamgarpour, Luca Furieri

It is known that the set of internally stabilizing controller Cstab is non-convex, but it admits convex characterizations using certain closed-loop maps: a classical result is the Youla parameterization, and two recent notions are the system-level paramete ...

PERGAMON-ELSEVIER SCIENCE LTD2022

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

Marcel Salathé, Harris Héritier, Djilani James Kebaili, Victor Guy Boulanger

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 ...

FRONTIERS MEDIA SA2022

Beyond potentials: Integrated machine learning models for materials

Michele Ceriotti

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 ...

SPRINGER HEIDELBERG2022

An Accuracy-Driven Compression Methodology to Derive Efficient Codebook-Based CNNs

David Atienza Alonso, Giovanni Ansaloni, Miguel Peon Quiros, Flavio Ponzina

Codebook-based optimizations are a class of algorithmic-level transformations able to effectively reduce the computing and memory requirements of Convolutional Neural Networks (CNNs). This approach tightly limits the number of unique weights in each layer, ...

2022