Publications related to Unsupervised Learning of Phase-Change-Based Neuromorphic Systems

Path planning versus cue responding: a bioinspired model of switching between navigation strategies

In this paper we describe a new computational model of switching between path-planning and cue-guided navigation strategies. It is based on three main assumptions: (i) the strategies are mediated by separate memory systems that learn independently and in p ...

Springer Verlag2010

Development and Validation of a Spike Detection and Classification Algorithm Aimed at Implementation on Hardware Devices

Diego Ghezzi

Neurons cultured in vitro on MicroElectrode Array (MEA) devices connect to each other, forming a network. To study electrophysiological activity and long term plasticity effects, long period recording and spike sorter methods are needed. Therefore, on-line ...

2010

Code-specific policy gradient rules for spiking neurons

Wulfram Gerstner, Guillaume Hennequin

Although it is widely believed that reinforcement learning is a suitable tool for describing behavioral learning, the mechanisms by which it can be implemented in networks of spiking neurons are not fully understood. Here, we show that different learning r ...

Massachusetts Institute of Technology Press2009

Spike-Based Reinforcement Learning in Continuous State and Action Space: When Policy Gradient Methods Fail

Wulfram Gerstner, Eleni Vasilaki, Nicolas Frémaux, Walter Senn

Changes of synaptic connections between neurons are thought to be the physiological basis of learning. These changes can be gated by neuromodulators that encode the presence of reward. We study a family of reward-modulated synaptic learning rules for spiki ...

Public Library of Science2009

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

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

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

Learning and Predicting Multimodal Daily Life Patterns from Cell Phones

Daniel Gatica-Perez, Katayoun Farrahi

In this paper, we investigate the multimodal nature of cell phone data in terms of discovering recurrent and rich patterns in people's lives. We present a method that can discover routines from multiple modalities (location and proximity) jointly modeled, ...

2009

Establishing a Novel Modeling Tool: A Python-based Interface for a Neuromorphic Hardware System

Neuromorphic hardware systems provide new possibilities for the neuroscience modeling community. Due to the intrinsic parallelism of the micro-electronic emulation of neural computation, such models are highly scalable without a loss of speed. However, the ...

Frontiers Research Foundation2009

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

Unsupervised Learning of Phase-Change-Based Neuromorphic Systems

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Path planning versus cue responding: a bioinspired model of switching between navigation strategies

Development and Validation of a Spike Detection and Classification Algorithm Aimed at Implementation on Hardware Devices

Code-specific policy gradient rules for spiking neurons

Spike-Based Reinforcement Learning in Continuous State and Action Space: When Policy Gradient Methods Fail

Stimulus sampling as an exploration mechanism for fast reinforcement learning

Perceptual learning: why mice do not play backgammon

Networks of mixed canonical-dissipative systems and dynamic hebbian learning

Learning and Predicting Multimodal Daily Life Patterns from Cell Phones

Establishing a Novel Modeling Tool: A Python-based Interface for a Neuromorphic Hardware System

Neuroevolution: from architectures to learning

Development and Validation of a Spike Detection and Classification Algorithm Aimed at Implementation on Hardware Devices

Code-specific policy gradient rules for spiking neurons

Path planning versus cue responding: a bioinspired model of switching between navigation strategies

Stimulus sampling as an exploration mechanism for fast reinforcement learning

Learning and Predicting Multimodal Daily Life Patterns from Cell Phones

Perceptual learning: why mice do not play backgammon

Neuroevolution: from architectures to learning

Establishing a Novel Modeling Tool: A Python-based Interface for a Neuromorphic Hardware System

Networks of mixed canonical-dissipative systems and dynamic hebbian learning

Spike-Based Reinforcement Learning in Continuous State and Action Space: When Policy Gradient Methods Fail