Concept

Neurone formel

Publications associées (288)

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Scalable Neuron Circuit Using Conductive-Bridge RAM for Pattern Reconstructions

Behnoush Attarimashalkoubeh

— A novel neuron circuit using a Cu/Ti/Al2O3-based conductive-bridge random access memory (CBRAM) device for hardware neural networks that utilize nonvolatile memories as synaptic weights is introduced. The neuronal operations are designed and proved using ...

Institute of Electrical and Electronics Engineers2016

Prospective Coding by Spiking Neurons

Johanni Michael Brea, Walter Senn

Animals learn to make predictions, such as associating the sound of a bell with upcoming feeding or predicting a movement that a motor command is eliciting. How predictions are realized on the neuronal level and what plasticity rule underlies their learnin ...

Public Library of Science2016

Brief wide-field photostimuli evoke and modulate oscillatory reverberating activity in cortical networks

Michele Giugliano

Cell assemblies manipulation by optogenetics is pivotal to advance neuroscience and neuroengineering. In in vivo applications, photostimulation often broadly addresses a population of cells simultaneously, leading to feed-forward and to reverberating respo ...

Nature Publishing Group2016

Review of Advances in Neural Networks: Neural Design Technology Stack

Yusuf Leblebici, Stanislaw Andrzej Wozniak

This review provides a high-level synthesis of significant recent advances in artificial neural network research, as well as multi-disciplinary concepts connected to the far-reaching goal of obtaining intelligent systems. We assume that a global outlook of ...

Elsevier2016

Deep(ly) Learning the Depth

Xun Victor Suo

We report on the use of deep learning algorithms to perform depth recovery in multiview imaging. We show that if enough training data are provided, a neural network such as multilayer perceptron can be trained to recover the depth in multiview imaging as a ...

2016

Nonlinear Hebbian Learning as a Unifying Principle in Receptive Field Formation

Wulfram Gerstner

The development of sensory receptive fields has been modeled in the past by a variety of models including normative models such as sparse coding or independent component analysis and bottom-up models such as spike-timing dependent plasticity or the Bienen- ...

Public Library of Science2016

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

Wulfram Gerstner, Skander Mensi, Christian Antonio Pozzorini, Olivier Richard Hagens

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 ...

Public Library of Science2016

When Neurons Fail - Technical Report

Rachid Guerraoui, El Mahdi El Mhamdi

Neural networks have been traditionally considered robust in the sense that their precision degrades gracefully with the failure of neurons and can be compensated by additional learning phases. Nevertheless, critical applications for which neural networks ...

EPFL2016

Towards End-to-End Speech Recognition

Dimitri Palaz

Standard automatic speech recognition (ASR) systems follow a divide and conquer approach to convert speech into text. Alternately, the end goal is achieved by a combination of sub-tasks, namely, feature extraction, acoustic modeling and sequence decoding, ...

EPFL2016

Expectation Propagation for Rectified Linear Poisson Regression

Matthias Seeger, Young Jun Ko

The Poisson likelihood with rectified linear function as non-linearity is a physically plausible model to discribe the stochastic arrival process of photons or other particles at a detector. At low emission rates the discrete nature of this process leads t ...

2015