Publication

Probabilistic Depth Image Registration incorporating Nonvisual Information

Publications associées (29)

Bayesian modeling of brain function and behavior

The application of Bayesian modeling techniques is increasingly common in neuroscience due to the coherent and principled way in which the paradigm deals with uncertainty. The Bayesian framework is particularly valuable in the context of complex, ill-posed ...

EPFL2020

Development of a new visual prosthesis for preclinical studies on artificial vision

Marta Jole Ildelfonsa Airaghi Leccardi

The technological evolution in materials science and microengineering favored the production of advanced visual prostheses useful for fighting blindness and to improve patients quality of life. Visual prostheses aim at replacing lost visual functions by ar ...

EPFL2020

Somatostatin enhances visual processing and perception by suppressing excitatory inputs to parvalbumin-positive interneurons in V1

Carl Petersen, Graham Knott, Catherine Maclachlan, Seung-Hee Lee

Somatostatin (SST) is a neuropeptide expressed in a major subtype of GABAergic interneurons in the cortex. Despite abundant expression of SST and its receptors, their modulatory function in cortical processing remains unclear. Here, we found that SST appli ...

2020

A Neuro-Inspired Computational Model for a Visually Guided Robotic Lamprey Using Frame and Event Based Cameras

Auke Ijspeert, Alessandro Crespi, Alexandre Bernardino, Mehmet Hasan Mutlu, Simon Lukas Hauser, Jorg Conradt, Ibrahim Youssef Youssef

The computational load associated with computer vision is often prohibitive, and limits the capacity for on-board image analysis in compact mobile robots. Replicating the kind of feature detection and neural processing that animals excel at remains a chall ...

2020

A t-distribution based operator for enhancing out of distribution robustness of neural network classifiers

Philip Neil Garner

Neural Network (NN) classifiers can assign extreme probabilities to samples that have not appeared during training (out-of-distribution samples) resulting in erroneous and unreliable predictions. One of the causes for this unwanted behaviour lies in the us ...

IEEE Institute of Electrical and Electronics Engineers2020

In vivo evaluation of gene editing and prosthetic strategies to restore vision in small and large animal models of retinal degeneration

Paola Vagni

Inherited retinal diseases (IRDs) form a group of diverse disorders that lead to the degeneration of the light-sensing cells of the retina: the photoreceptors. IRDs are among the leading causes of blindness in working-age adults living in industrialized co ...

EPFL2020

Learning and leveraging shared domain semantics to counteract visual domain shifts

Róger Bermúdez Chacón

One of the main limitations of artificial intelligence today is its inability to adapt to unforeseen circumstances. Machine Learning (ML), due to its data-driven nature, is particularly susceptible to this. ML relies on observations in order to learn impli ...

EPFL2020

Inference on the Angular Distribution of Extremes

Claudio Andri Semadeni

The spectral distribution plays a key role in the statistical modelling of multivariate extremes, as it defines the dependence structure of multivariate extreme-value distributions and characterizes the limiting distribution of the relative sizes of the co ...

EPFL2020

Bayesian regression explains how human participants handle parameter uncertainty

Jean-Pascal Théodor Pfister, Michael Herzog, Maya Anna Jastrzebowska, Mattew Pachai

Author summary How do humans make prediction when the critical factor that influences the quality of the prediction is hidden? Here, we address this question by conducting a simple psychophysical experiment in which participants had to extrapolate a parabo ...

2020