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In search of the neural circuits of intrinsic motivation

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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

Perceptual learning, roving and the unsupervised bias

Michael Herzog, Wulfram Gerstner, Carl Kristoffer Aberg, Nicolas Frémaux

Perceptual learning improves perception through training. Perceptual learning improves with most stimulus types but fails when certain stimulus types are mixed during training (roving). This result is surprising because classical supervised and unsupervise ...

Elsevier2012

Diffusion Adaptation Strategies for Distributed Optimization and Learning Over Networks

Ali H. Sayed

We propose an adaptive diffusion mechanism to optimize global cost functions in a distributed manner over a network of nodes. The cost function is assumed to consist of a collection of individual components. Diffusion adaptation allows the nodes to coopera ...

IEEE2012

Bayesian multitask inverse reinforcement learning

Christos Dimitrakakis

We generalise the problem of inverse reinforcement learning to multiple tasks, from multiple demonstrations. Each one may represent one expert trying to solve a different task, or as different experts trying to solve the same task. Our main contribution is ...

2011

Functional Requirements for Reward-Modulated Spike-Timing-Dependent Plasticity

Wulfram Gerstner, Nicolas Frémaux

Recent experiments have shown that spike-timing-dependent plasticity is influenced by neuromodulation. We derive theoretical conditions for successful learning of reward-related behavior for a large class of learning rules where Hebbian synaptic plasticity ...

Society for Neuroscience2010

Stress, genotype and norepinephrine in the prediction of mouse behavior using reinforcement learning

Maria del Carmen Sandi Perez, Wulfram Gerstner, Gediminas Luksys

Individual behavioral performance during learning is known to be affected by modulatory factors, such as stress and motivation, and by genetic predispositions that influence sensitivity to these factors. Despite numerous studies, no integrative framework i ...

Nature Publishing Group2009

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

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

Intrinsically motivated machines

Frédéric Kaplan

Children seem intrinsically motivated to manipulate, to explore, to test, to learn and they look for activities and situations that provide such learning opportunities. Inspired by research in developmental psychology and neuroscience, some researchers hav ...

Springer Verlag2007

Adding prediction risk to the theory of reward learning

This article analyzes the simple Rescorla-Wagner learning rule from the vantage point of least squares learning theory. In particular, it suggests how measures of risk, such as prediction risk, can be used to adjust the learning constant in reinforcement l ...

2007