Bottlenecks, Modularity, and the Neural Control of Behavior

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In most complex nervous systems there is a clear anatomical separation between the nerve cord, which contains most of the final motor outputs necessary for behaviour, and the brain. In insects, the neck connective is both a physical and an information bott ...

Our understanding of the generalization capabilities of neural networks (NNs) is still incomplete. Prevailing explanations are based on implicit biases of gradient descent (GD) but they cannot account for the capabilities of models from gradient-free metho ...

We study the random binary symmetric perceptron problem, focusing on the behavior of rare high-margin solutions. While most solutions are isolated, we demonstrate that these rare solutions are part of clusters of extensive entropy, heuristically correspond ...

Deep neural networks trained on physical losses are emerging as promising surrogates for nonlinear numerical solvers. These tools can predict solutions to Maxwell's equations and compute gradients of output fields with respect to the material and geometric ...

Visuo-motor integration shapes our daily experience and underpins the sense of feeling in control over our actions. The last decade has seen a surge in robotically and virtually mediated interactions, whereby bodily actions ultimately result in an artifici ...

In medical image segmentation, supervised deep networks' success comes at the cost of requiring abundant labeled data. While asking domain experts to annotate only one or a few of the cohort's images is feasible, annotating all available images is impracti ...

Superagers are defined as older adults who have youthful memory performance comparable to that of middle-aged adults. Classifying superagers based on the brain connectome using machine learning modeling can provide important insights on the physiology unde ...

This thesis aims to explore and exploit trade-offs in integrated circuits and systems to overcome the fundamental bottlenecks faced by future data acquisition and communication systems. Specifically, we target the emerging implantable neurotechnology and t ...

This article presents two algorithms developed based on two different techniques, from clusterization theory, namely k-means clustering technique and Fuzzy C-means technique, respectively. In this context, the study offers a sustained comparison of the two ...

Removal of noise from fluorescence microscopy images is an important first step in many biological analysis pipelines. Current state-of-the-art supervised methods employ convolutional neural networks that are trained with clean (ground-truth) images. Recen ...

For an autonomous car, getting surprised is the worst thing that can happen. To prevent that, plenty of studies are trying to forecast traffic participants’ actions especially in an urban scene using recurrent networks. Recurrent networks are used for temp ...

Hardware accelerators based on two-terminal non-volatile memories (NVMs) can potentially provide competitive speed and accuracy for the training of fully connected deep neural networks (FC-DNNs), with respect to GPUs and other digital accelerators. We rece ...

Purpose of review ...

We propose Deep Feature Factorization (DFF), a method capable of localizing similar semantic concepts within an image or a set of images. We use DFF to gain insight into a deep convolutional neural network's learned features, where we detect hierarchical c ...

The study of brain dynamics enables us to characterize the time-varying functional connectivity among distinct neural groups. However, current methods suffer from the absence of structural connectivity information. We propose to integrate infra-slow neural ...

Most recent approaches to monocular 3D pose estimation rely on Deep Learning. They either train a Convolutional Neural Network to directly regress from image to 3D pose, which ignores the dependencies between human joints, or model these dependencies via a ...

The way in which single neurons transform input into output spike trains has fundamental consequences for network coding. Theories and modeling studies based on standard Integrate-and-Fire models implicitly assume that, in response to increasingly strong i ...

The impact of Phase Change Memory (PCM) as well as other Non-Volatile Memory (NVM) device characteristics on the quantitative classification performance of artificial neural networks is studied. Our results show that any NVM-based neural network — not just ...

Bottlenecks, Modularity, and the Neural Control of Behavior

Chattez avec Graph Search

Comparative connectomics of Drosophila descending and ascending neurons

Neural Redshift: Random Networks are not Random Functions

On the atypical solutions of the symmetric binary perceptron

Predicting nonlinear optical scattering with physics-driven neural networks

Neural signatures of visuo-motor integration during human-robot interactions

Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentation

Predicting Superagers by Machine Learning Classification Based on the Functional Brain Connectome Using Resting-State Functional Magnetic Resonance Imaging

Resource Trade-Offs in Circuits and Systems: from Neurotechnology to Communications

Image Clustering Algorithms to Identify Complicated Cerebral Diseases. Description and Comparison

Removing Structured Noise With Self-Supervised Blind-Spot Networks

Feed-forwards meet recurrent networks in vehicle trajectory prediction

Training fully connected networks with resistive memories: impact of device failures

A nation-wide initiative for brain imaging and clinical phenotype data federation in Swiss university memory centres

Deep Feature Factorization for Concept Discovery

Transient networks of spatio-temporal connectivity map communication pathways in brain functional systems

Structured Prediction of 3D Human Pose with Deep Neural Networks

Enhanced Sensitivity to Rapid Input Fluctuations by Nonlinear Threshold Dynamics in Neocortical Pyramidal Neurons

PCM for Neuromorphic Applications: Impact of Device Characteristics on Neural Network Performance