Recognizing sequences of sequences.

The brain's decoding of fast sensory streams is currently impossible to emulate, even approximately, with artificial agents. For example, robust speech recognition is relatively easy for humans but exceptionally difficult for artificial speech recognition systems. In this paper, we propose that...

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Auteurs principaux:	Stefan J Kiebel, Katharina von Kriegstein, Jean Daunizeau, Karl J Friston
Format:	article
Langue:	EN
Publié:	Public Library of Science (PLoS) 2009
Sujets:	Biology (General) QH301-705.5
Accès en ligne:	https://doaj.org/article/f87b3baec4c943b99d9d3c91dc0c0a8c
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id	oai:doaj.org-article:f87b3baec4c943b99d9d3c91dc0c0a8c
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spelling	oai:doaj.org-article:f87b3baec4c943b99d9d3c91dc0c0a8c2021-11-25T05:42:14ZRecognizing sequences of sequences.1553-734X1553-735810.1371/journal.pcbi.1000464https://doaj.org/article/f87b3baec4c943b99d9d3c91dc0c0a8c2009-08-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/19680429/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358The brain's decoding of fast sensory streams is currently impossible to emulate, even approximately, with artificial agents. For example, robust speech recognition is relatively easy for humans but exceptionally difficult for artificial speech recognition systems. In this paper, we propose that recognition can be simplified with an internal model of how sensory input is generated, when formulated in a Bayesian framework. We show that a plausible candidate for an internal or generative model is a hierarchy of 'stable heteroclinic channels'. This model describes continuous dynamics in the environment as a hierarchy of sequences, where slower sequences cause faster sequences. Under this model, online recognition corresponds to the dynamic decoding of causal sequences, giving a representation of the environment with predictive power on several timescales. We illustrate the ensuing decoding or recognition scheme using synthetic sequences of syllables, where syllables are sequences of phonemes and phonemes are sequences of sound-wave modulations. By presenting anomalous stimuli, we find that the resulting recognition dynamics disclose inference at multiple time scales and are reminiscent of neuronal dynamics seen in the real brain.Stefan J KiebelKatharina von KriegsteinJean DaunizeauKarl J FristonPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 5, Iss 8, p e1000464 (2009)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Stefan J Kiebel Katharina von Kriegstein Jean Daunizeau Karl J Friston Recognizing sequences of sequences.
description	The brain's decoding of fast sensory streams is currently impossible to emulate, even approximately, with artificial agents. For example, robust speech recognition is relatively easy for humans but exceptionally difficult for artificial speech recognition systems. In this paper, we propose that recognition can be simplified with an internal model of how sensory input is generated, when formulated in a Bayesian framework. We show that a plausible candidate for an internal or generative model is a hierarchy of 'stable heteroclinic channels'. This model describes continuous dynamics in the environment as a hierarchy of sequences, where slower sequences cause faster sequences. Under this model, online recognition corresponds to the dynamic decoding of causal sequences, giving a representation of the environment with predictive power on several timescales. We illustrate the ensuing decoding or recognition scheme using synthetic sequences of syllables, where syllables are sequences of phonemes and phonemes are sequences of sound-wave modulations. By presenting anomalous stimuli, we find that the resulting recognition dynamics disclose inference at multiple time scales and are reminiscent of neuronal dynamics seen in the real brain.
format	article
author	Stefan J Kiebel Katharina von Kriegstein Jean Daunizeau Karl J Friston
author_facet	Stefan J Kiebel Katharina von Kriegstein Jean Daunizeau Karl J Friston
author_sort	Stefan J Kiebel
title	Recognizing sequences of sequences.
title_short	Recognizing sequences of sequences.
title_full	Recognizing sequences of sequences.
title_fullStr	Recognizing sequences of sequences.
title_full_unstemmed	Recognizing sequences of sequences.
title_sort	recognizing sequences of sequences.
publisher	Public Library of Science (PLoS)
publishDate	2009
url	https://doaj.org/article/f87b3baec4c943b99d9d3c91dc0c0a8c
work_keys_str_mv	AT stefanjkiebel recognizingsequencesofsequences AT katharinavonkriegstein recognizingsequencesofsequences AT jeandaunizeau recognizingsequencesofsequences AT karljfriston recognizingsequencesofsequences
_version_	1718414555182268416

Recognizing sequences of sequences.

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