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Analyzing spike-timing-dependent plasticity in recurrent neuronal networks »

Matthieu Gilson, Anthony Burkitt, David Grayden, Doreen Thomas, and Leo van Hemmen

A mathematical framework is being used to investigate the learning dynamics induced by a class of biologically realistic synaptic plasticity rules in recurrently connected neuronal networks.
Advancing neuroimaging research with predictive multivariate pattern analysis »

Yaroslav O. Halchenko and Michael Hanke

PyMVPA, a novel Python-based framework for multivariate pattern analysis, facilitates the application of statistical learning methods to neural data.
Audio-visual sensor fusion for object localization »

Vincent Chan

Using the onset time of stimuli, a biologically-inspired system learns to identify the sources of sounds.
Spike-based synaptic plasticity and classification on VLSI »

Srinjoy Mitra and Giacomo Indiveri

A VLSI system implements a bioplausible spike-based learning algorithm and is capable of robust classification of binary patterns, even when they are highly correlated.
Probabilistic reasoning and decision making in sensory-motor systems »

Matthew Cook

A dozen robotics-related PhD projects are brought together to provide a good overview of the state of the art in solving problems by defining joint probability distributions over both sensor readings and actions.


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