Artificial Neural Networks and Machine Learning – ICANN 2011

We introduce a new approach to neural encoding and decoding which makes use of sparse regression and Markov random fields. We show that interesting response functions were estimated from neuroimaging data acquired while a subject was watching checkerboard patterns and geometrical figures. Furthermore, we demonstrate that reconstructions of the original stimuli can be generated by loopy belief propagation in a Markov random field.