|Welcome to The Neuromorphic Engineer|
You are in:
Welcome to the Methods section. The latest articles for this section are displayed below:
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.
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.
Tell us what to cover!
If you'd like to write an article or know of someone else who is doing relevant and interesting stuff, let us know. E-mail the editor and suggest the subject for the article and, if you're suggesting someone else's work, tell us their name, affiliation, and e-mail.