Publications related to Modeling perceptual learning: difficulties and how they can be overcome

Modeling perceptual learning: why mice do not play backgammon

Michael Herzog, Elisa Tartaglia, Carl Kristoffer Aberg

Perceptual learning is often considered one of the simplest and basic forms of learning in general. Accordingly, it is usually modeled with simple and basic neural networks which show good results in grasping the empirical data. Simple meets simple. Comple ...

2009

Stress, individual differences, and norepinephrine in reinforcement learning-based prediction of mouse behavior in conditioning and spatial learning

Maria del Carmen Sandi Perez, Gediminas Luksys

Acute stress regulates different aspects of behavioral learning through the action of stress hormones and neuromodulators. Stress effects depend on stressor's type, intensity, timing, and the learning paradigm. In addition, genetic background of animals mi ...

EPFL2009

Simulating Learning Dynamics in Project Networks

Juan Andrei Villarroel Fernandez

In this paper we develop a multiagent simulation model to explore the impact of learning dynamics on the productive implementation of innovations in project networks comprised of designers and contractors. Though researchers generally agree that when firms ...

2009

An online framework for learning novel concepts over multiple cues

Barbara Caputo, Jie Luo

We propose an online learning algorithm to tackle the problem of learning under limited computational resources in a teacher-student scenario, over multiple visual cues. For each separate cue, we train an online learning algorithm that sacrifices performan ...

2009

Networks of mixed canonical-dissipative systems and dynamic hebbian learning

Max-Olivier Hongler, Julio Rodriguez

We study the dynamics of a network consisting of N diffusively coupled, stable- limit-cycle oscillators on which individual frequencies are parametrized by

w_k, k = 1, . . . ,N

. We introduce a learning rule which influences the wk by driving the system t ...

2009

New Fast Training Algorithm Suitable For Hardware Kohonen Neural Networks Designed For Analysis 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 ...

Insticc-Inst Syst Technologies Information Control & Communication, Avenida D Manuel L, 27A 2 Esquerdo, Setubal, 2910-595, Portugal2009

Temporal patterning of 'unlearnable' stimuli types does not always enable learning

Michael Herzog, Carl Kristoffer Aberg

Presenting two or more stimulus types randomly interleaved, so-called roving stimuli, disrupts perceptual learning in many paradigms. It was recently reported that learning with disrupting stimuli types is possible when stimuli are presented in an alternat ...

2009

Evolutionary Advantages of Neuromodulated Plasticity in Dynamic, Reward- based Scenarios

Dario Floreano, Claudio Mattiussi, Peter Dürr, Andrea Soltoggio

memory in biological neural networks. Similarly, artificial neural networks could benefit from modulatory dynamics when facing certain types of learning problem. Here we test this hypothesis by introducing modulatory neurons to enhance or dampen neural pla ...

MIT Press2008

Neuroevolution: from architectures to learning

Dario Floreano, Claudio Mattiussi, Peter Dürr

Artificial neural networks are applied to many real-world problems, ranging from pattern classification to robot control. In order to design a neural network for a particular task, the choice of an architecture (including the choice of a neuron model), and ...

2008

Iterative Learning Control based on Stochastic Approximation

Alireza Karimi, Roland Longchamp, Mark Edward John Butcher

In this paper stochastic approximation theory is used to produce Iterative Learning Control algorithms which are less sensitive to stochastic disturbances, a typical problem for the learning process of standard ILC algorithms. Two algorithms are developed, ...

2008

Modeling perceptual learning: difficulties and how they can be overcome

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Modeling perceptual learning: why mice do not play backgammon

Stress, individual differences, and norepinephrine in reinforcement learning-based prediction of mouse behavior in conditioning and spatial learning

Simulating Learning Dynamics in Project Networks

An online framework for learning novel concepts over multiple cues

Networks of mixed canonical-dissipative systems and dynamic hebbian learning

New Fast Training Algorithm Suitable For Hardware Kohonen Neural Networks Designed For Analysis Of Biomedical Signals

Temporal patterning of 'unlearnable' stimuli types does not always enable learning

Evolutionary Advantages of Neuromodulated Plasticity in Dynamic, Reward- based Scenarios

Neuroevolution: from architectures to learning

Iterative Learning Control based on Stochastic Approximation

Simulating Learning Dynamics in Project Networks

Stress, individual differences, and norepinephrine in reinforcement learning-based prediction of mouse behavior in conditioning and spatial learning

Evolutionary Advantages of Neuromodulated Plasticity in Dynamic, Reward- based Scenarios

Neuroevolution: from architectures to learning

Iterative Learning Control based on Stochastic Approximation

Networks of mixed canonical-dissipative systems and dynamic hebbian learning

New Fast Training Algorithm Suitable For Hardware Kohonen Neural Networks Designed For Analysis Of Biomedical Signals

Temporal patterning of 'unlearnable' stimuli types does not always enable learning

Modeling perceptual learning: why mice do not play backgammon

An online framework for learning novel concepts over multiple cues