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Methods
Welcome to the Methods section. The latest articles for this section are displayed below:
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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.
PyMVPA, a novel Python-based framework for multivariate pattern analysis, facilitates the application of statistical learning methods to neural data.
Using the onset time of stimuli, a biologically-inspired system learns to identify the sources of sounds.
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.
BOOK REVIEW
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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