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Review of Advances in Neural Networks: Neural Design Technology Stack

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Unsupervised Learning of Phase-Change-Based Neuromorphic Systems

Stanislaw Andrzej Wozniak

Neuromorphic systems provide brain-inspired methods of computing. In a neuromorphic architecture, inputs are processed by a network of neurons receiving operands through synaptic interconnections, tuned in the process of learning. Neurons act simultaneousl ...

EPFL2017

Learning Recruits Neurons Representing Previously Established Associations in the Corvid Endbrain

Galyna Pidpruzhnykova

Crows quickly learn arbitrary associations. As a neuronal correlate of this behavior, single neurons in the corvid endbrain area nidopallium caudolaterale (NCL) change their response properties during association learning. In crows performing a delayed ass ...

Mit Press2017

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

Theory of representation learning in cortical neural networks

Carlos Stein Naves de Brito

Our brain continuously self-organizes to construct and maintain an internal representation of the world based on the information arriving through sensory stimuli. Remarkably, cortical areas related to different sensory modalities appear to share the same f ...

EPFL2016

On the Learning Behavior of Adaptive Networks - Part II: Performance Analysis

Ali H. Sayed

Part I of this paper examined the mean-square stability and convergence of the learning process of distributed strategies over graphs. The results identified conditions on the network topology, utilities, and data in order to ensure stability; the results ...

IEEE2015

Adaptation, Learning, and Optimization over Networks

Ali H. Sayed

This work deals with the topic of information processing over graphs. The presentation is largely self-contained and covers results that relate to the analysis and design of multi-agent networks for the distributed solution of optimization, adaptation, and ...

NOW Publishers2014

On the Influence of Informed Agents on Learning and Adaptation Over Networks

Ali H. Sayed

Adaptive networks consist of a collection of agents with adaptation and learning abilities. The agents interact with each other on a local level and diffuse information across the network through their collaboration. In this work, we consider two types of ...

Institute of Electrical and Electronics Engineers, Inc., 345 E. 47 th St. NY NY 10017-2394 United States2013

Models of Reward-Modulated Spike-Timing-Dependent Plasticity

Nicolas Frémaux

How do animals learn to repeat behaviors that lead to the obtention of food or other “rewarding” objects? As a biologically plausible paradigm for learning in spiking neural networks, spike-timing dependent plasticity (STDP) has been shown to perform well ...

EPFL2013

A New Fast Training Algorithm of the WTM Kohonen Neural Network Implemented for Classification of Biomedical Signals

Rafal Tomasz Dlugosz

A new optimized algorithm for the learning process suitable for hardware implemented Winner Takes Most Kohonen Neural Network (KNN) has been proposed in the paper. In networks of this type a neighborhood mechanism is used to improve the convergence propert ...

2009

Perceptual learning: why mice do not play backgammon

Elisa Tartaglia

Perceptual learning is the ability to modify perception through practice. As a form of brain plasticity, perceptual learning has been studied for more than thirty years in different fields including psychology, neurophysiology and computational neuroscienc ...

EPFL2009