Publications related to Perceptual learning: why mice do not play backgammon

Stimulus sampling as an exploration mechanism for fast reinforcement learning

Reinforcement learning in neural networks requires a mechanism for exploring new network states in response to a single, nonspecific reward signal. Existing models have introduced synaptic or neuronal noise to drive this exploration. However, those types o ...

Springer Verlag2009

A combined neural network/gradient-based approach for the identification of constitutive model parameters using self-boring pressuremeter tests

Laurent Vulliet, Rafal Filip Obrzud

This paper presents a numerical procedure of material parameter identification for the coupled hydromechanical boundary value problem (BVP) of the self-boring pressuremeter test (SBPT) in clay. First, the neural network (NN) technique is applied to obtain ...

2009

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

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

Anesthesia prevents auditory perceptual learning

Michael Herzog, Alain Farron, Elisa Tartaglia, Carl Kristoffer Aberg

BACKGROUND: An auditory perceptual learning paradigm was used to investigate whether implicit memories are formed during general anesthesia. METHODS: Eighty-seven patients who had an American Society of Anesthesiologists physical status of I-III and were s ...

Lippincott, Williams & Wilkins2009

Human perceptual learning by mental imagery

Michael Herzog, Fred Mast, Elisa Tartaglia, Laura Bamert

Perceptual learning is learning to perceive. For example, a radiologist is able to easily identify anomalies in medical images only after extended training. Theoretical and psychophysical studies [1-12] suggest that such improvements of performance are acc ...

Elsevier2009

Neural modeling of the cortical processes underlying imitative behaviors

Eric Sauser

Imitation is the ability to recognize, learn and reproduce the actions of others. In addition to facilitating the transmission of knowledge and skills, it has been suggested that this fundamental cognitive capacity is at the origin of other human faculties ...

EPFL2008

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

Perceptual learning: why mice do not play backgammon

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Stimulus sampling as an exploration mechanism for fast reinforcement learning

A combined neural network/gradient-based approach for the identification of constitutive model parameters using self-boring pressuremeter tests

Simulating Learning Dynamics in Project Networks

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

Anesthesia prevents auditory perceptual learning

Human perceptual learning by mental imagery

Neural modeling of the cortical processes underlying imitative behaviors

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

Stimulus sampling as an exploration mechanism for fast reinforcement learning

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

Simulating Learning Dynamics in Project Networks

Anesthesia prevents auditory perceptual learning

A combined neural network/gradient-based approach for the identification of constitutive model parameters using self-boring pressuremeter tests

Neural modeling of the cortical processes underlying imitative behaviors

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

Human perceptual learning by mental imagery

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