Abstract
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.
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van Gerven, M.A.J., Maris, E., Heskes, T. (2011). A Markov Random Field Approach to Neural Encoding and Decoding. In: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2011. ICANN 2011. Lecture Notes in Computer Science, vol 6792. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21738-8_1
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DOI: https://doi.org/10.1007/978-3-642-21738-8_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21737-1
Online ISBN: 978-3-642-21738-8
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