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The "Liquid Computer": A Novel Strategy for Real-Time Computing on Time Series

Publications associées (46)

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

Human babies have a natural desire to interact with new toys and objects, through which they learn how the world around them works, e.g., that glass shatters when dropped, but a rubber ball does not. When their predictions are proven incorrect, such as whe ...

EPFL2024

Butterfly effects in perceptual development: A review of the 'adaptive initial degradation' hypothesis

Lukas Vogelsang, Marin Vogelsang

Human perceptual development evolves in a stereotyped fashion, with initially limited perceptual capabilities maturing over the months or years following the commencement of sensory experience into robust proficiencies. This review focuses on the functiona ...

San Diego2024

Deep Learning Meets Sparse Regularization

Rahul Parhi

Deep learning (DL) has been wildly successful in practice, and most of the state-of-the-art machine learning methods are based on neural networks (NNs). Lacking, however, is a rigorous mathematical theory that adequately explains the amazing performance of ...

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Informing Neural Networks with Simplified Physics for Better Flow Prediction

Fedor Sergeev

Surrogate deep neural networks (DNNs) can significantly speed up the engineering design process by providing a quick prediction that emulates simulated data. Many previous works have considered improving the accuracy of such models by introducing additiona ...

2023

Origami robots are characterized by their compact design, quasi-two-dimensional manufacturing process, and folding joint-based transmission kinematics. The physical requirements in terms of payload, range of motion, and embedding core robotic components ha ...

MARY ANN LIEBERT, INC2022

Computational tool for stock-constrained design of structures

Corentin Jean Dominique Fivet, Jonas Warmuth, Jan Friedrich Georg Brütting

Designing structures from reused elements is becoming an increasingly important design task for structural engineers as it has potential to significantly reduce adverse environmental impacts of building structures. To allow for a broad application of this ...

2021

Tuning the π‐Accepting Properties of Mesoionic Carbenes: A Combined Computational and Experimental Study

Rosario Scopelliti, Kay Severin, Farzaneh Fadaei Tirani, Andrzej Sienkiewicz, Zhaowen Dong

Mesoionic imidazolylidenes are recognized as excellent electron-donating ligands in organometallic and main group chemistry. However, these carbene ligands typically show poor π-accepting properties. We have performed a computational analysis of 71 mesoion ...

2021

An efficient analytical reduction of detailed nonlinear neuron models

Felix Schürmann, Pramod Shivaji Kumbhar, Michael Emiel Gevaert, Oren Amsalem, Idan Segev, Guy Eyal

Detailed conductance-based nonlinear neuron models consisting of thousands of synapses are key for understanding of the computational properties of single neurons and large neuronal networks, and for interpreting experimental results. Simulations of these ...

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Biophysically detailed mathematical models of multiscale cardiac active mechanics

Alfio Quarteroni, Francesco Regazzoni

Author summary Computer-based numerical simulations of the heart are increasingly assuming a recognized role in the context of computational medicine and cardiology. They are based on mathematical models describing the different physical phenomena occurrin ...

2020

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

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

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

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