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

Perceptual learning: why mice do not play backgammon

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

Stimulus sampling as an exploration mechanism for fast reinforcement learning

Eleni Vasilaki, Walter Senn

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

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

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

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

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

Evolutionary and Computational Advantages of Neuromodulated Plasticity

Andrea Soltoggio

The integration of modulatory neurons into evolutionary artificial neural networks is proposed here. A model of modulatory neurons was devised to describe a plasticity mechanism at the low level of synapses and neurons. No initial assumptions were made on ...

University of Birmingham, UK2008

Mill-cut: a neural network system for the prediction of thermo-mechanical loads induced in end-milling operations

Paul Xirouchakis, Jitender Kumar Rai, Louis Villedieu

This paper presents the design and implementation issues of a generalized system called mill-cut, developed for the prediction of cutting forces and temperature in end-milling operations. Based on an ANN approach, mill-cut predicts all the three components ...

Springer-Verlag2008

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

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Perceptual learning: why mice do not play backgammon

Stimulus sampling as an exploration mechanism for fast reinforcement learning

Modeling perceptual learning: why mice do not play backgammon

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

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

Neuroevolution: from architectures to learning

Evolutionary and Computational Advantages of Neuromodulated Plasticity

Mill-cut: a neural network system for the prediction of thermo-mechanical loads induced in end-milling operations

Perceptual learning: why mice do not play backgammon

Stimulus sampling as an exploration mechanism for fast reinforcement learning

Modeling perceptual learning: why mice do not play backgammon

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

Neuroevolution: from architectures to learning

Evolutionary and Computational Advantages of Neuromodulated Plasticity

Simulating Learning Dynamics in Project Networks

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

Mill-cut: a neural network system for the prediction of thermo-mechanical loads induced in end-milling operations

Networks of mixed canonical-dissipative systems and dynamic hebbian learning