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Effects of biased feedback on learning and deciding in a vernier discrimination task

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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

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

Learning Multi-Modal Dictionaries: Application to Audiovisual Data

Pierre Vandergheynst, Rémi Gribonval, Gianluca Monaci, Philippe Jost

This paper presents a methodology for extracting meaningful synchronous structures from multi-modal signals. Simultaneous processing of multi-modal data can reveal information that is unavailable when handling the sources separately. However, in natural hi ...

2006

A recurrent model for perceptual learning

Michael Herzog

Perceptual learning has received enhanced interest during the last years both from theoreticians and experimentalists. Recent experimental results reveal that mechanisms underlying perceptual learning are more complex than previously expected, thereby ruli ...

Optical Society of America1999

Modeling perceptual learning: difficulties and how they can be overcome

Michael Herzog

We investigated the roles of feedback and attention in training a vernier discrimination task as an example of perceptual learning. Human learning even of simple stimuli, such as verniers, relies on more complex mechanisms than previously expected--ruling ...

Springer Verlag1998

The role of feedback in learning a vernier discrimination task

Michael Herzog

We compare improvement through training in vernier acuity under different feedback conditions in order to clarify the role of feedback during learning of a perceptual task and to test different (neural network) models of perceptual learning. Improvement of ...

Elsevier1997

Optimization of high order perceptrons

Neural networks are widely applied in research and industry. However, their broader application is hampered by various technical details. Among these details are several training parameters and the choice of the topology of the network. The subject of this ...

École Polytechnique Fédérale de Lausanne1997

Optimization of high order perceptrons

EPFL1997

Supervised learning of perceptron and output feedback dynamic networks: a feedback analysis via the small gain theorem

Ali H. Sayed

This paper provides a time-domain feedback analysis of the perceptron learning algorithm and of training schemes for dynamic networks with output feedback. It studies the robustness performance of the algorithms in the presence of uncertainties that might ...

IEEE1997