title: Unsupervised feature learning improves prediction of human brain activity in response to natural images.
abstract: Encoding and decoding in functional magnetic resonance imaging has recently emerged as an area of research to noninvasively characterize the relationship between stimulus features and human brain activity. To overcome the challenge of formalizing what stimulus features should modulate single voxel responses, we introduce a general approach for making directly testable predictions of single voxel responses to statistically adapted representations of ecologically valid stimuli. These representations are learned from unlabeled data without supervision. Our approach is validated using a parsimonious computational model of (i) how early visual cortical representations are adapted to statistical regularities in natural images and (ii) how populations of these representations are pooled by single voxels. This computational model is used to predict single voxel responses to natural images and identify natural images from stimulus-evoked multiple voxel responses. We show that statistically adapted low-level sparse and invariant representations of natural images better span the space of early visual cortical representations and can be more effectively exploited in stimulus identification than hand-designed Gabor wavelets. Our results demonstrate the potential of our approach to better probe unknown cortical representations.
authors.affiliation: Radboud University Nijmegen, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, Netherlands.
authors.forename: Umut
authors.initials: U
authors.lastname: Güçlü
authors.affiliation: Radboud University Nijmegen, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, Netherlands.
authors.forename: Marcel A J
authors.initials: MA
authors.lastname: van Gerven
mesh_terms: D001185:Artificial Intelligence; D059907:Functional Neuroimaging / Q000379:methods*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D014793:Visual Cortex / Q000502:physiology*
references.citation: J Neurophysiol. 2013 Jul;110(2):481-94
references.pmid: 23615546
references.citation: Neural Comput. 2010 Dec;22(12):3127-42
references.pmid: 20858128
references.citation: J Neurosci. 1992 Aug;12(8):3139-61
references.pmid: 1322982
references.citation: PLoS Biol. 2012 Jan;10(1):e1001251
references.pmid: 22303281
references.citation: Neuroimage. 2006 Dec;33(4):1104-16
references.pmid: 17029988
references.citation: Proc Natl Acad Sci U S A. 2008 Jul 29;105(30):10607-12
references.pmid: 18641121
references.citation: J Neurosci. 2013 Oct 9;33(41):16275-84
references.pmid: 24107959
references.citation: Neuron. 2009 Sep 24;63(6):902-15
references.pmid: 19778517
references.citation: Nature. 1996 Jun 13;381(6583):607-9
references.pmid: 8637596
references.citation: Top Cogn Sci. 2010 Apr;2(2):251-64
references.pmid: 25163788
references.citation: IEEE Trans Pattern Anal Mach Intell. 2013 Aug;35(8):1798-828
references.pmid: 23787338
references.citation: Vision Res. 1982;22(5):545-59
references.pmid: 7112954
references.citation: Proc R Soc Lond B Biol Sci. 1977 Jul 28;198(1130):1-59
references.pmid: 20635
references.citation: Vision Res. 2001 Aug;41(18):2413-23
references.pmid: 11459597
references.citation: Ann Appl Stat. 2011 Jun;5(2B):1159-1182
references.pmid: 22523529
references.citation: Neural Comput. 2006 Jul;18(7):1527-54
references.pmid: 16764513
references.citation: Nat Neurosci. 2013 Jun;16(6):763-70
references.pmid: 23603707
references.citation: J Opt Soc Am A. 1988 Apr;5(4):598-605
references.pmid: 3404310
references.citation: Neural Comput. 2010 Sep 1;22(9):2308-33
references.pmid: 20569179
references.citation: Nat Neurosci. 2000 Jun;3(6):535-6
references.pmid: 10816307
references.citation: Neuroimage. 2012 Aug 15;62(2):1241-8
references.pmid: 22063093
references.citation: Proc Natl Acad Sci U S A. 2014 Jun 10;111(23):8619-24
references.pmid: 24812127
references.citation: Nature. 2008 Mar 20;452(7185):352-5
references.pmid: 18322462
references.citation: Neuroimage. 2011 May 15;56(2):400-10
references.pmid: 20691790
references.citation: Cereb Cortex. 2001 Dec;11(12):1182-90
references.pmid: 11709489
references.citation: Curr Biol. 2011 Oct 11;21(19):1641-6
references.pmid: 21945275
references.citation: J Opt Soc Am A. 1985 Jul;2(7):1160-9
references.pmid: 4020513
references.citation: Science. 2008 May 30;320(5880):1191-5
references.pmid: 18511683
references.citation: J Physiol. 1968 Mar;195(1):215-43
references.pmid: 4966457
references.citation: Nat Neurosci. 2005 May;8(5):679-85
references.pmid: 15852014
references.citation: Neuroimage. 2008 Jan 15;39(2):647-60
references.pmid: 17977024
references.citation: Science. 1974 Dec 20;186(4169):1133-5
references.pmid: 4219965
references.citation: Nature. 2005 Jun 23;435(7045):1102-7
references.pmid: 15973409
references.citation: Vision Res. 1997 Dec;37(23):3327-38
references.pmid: 9425547
references.citation: J Physiol Paris. 2013 Nov;107(5):369-98
references.pmid: 23369823
references.citation: J Neurosci. 2010 Jan 6;30(1):325-30
references.pmid: 20053913
references.citation: Science. 2001 Sep 28;293(5539):2425-30
references.pmid: 11577229
references.citation: J Neurosci. 1999 May 15;19(10):4046-64
references.pmid: 10234033
references.citation: Nat Neurosci. 2004 May;7(5):456-61
references.pmid: 15114358
references.citation: Neuron. 2008 Dec 10;60(5):915-29
references.pmid: 19081384
references.citation: J Neurophysiol. 1987 Dec;58(6):1233-58
references.pmid: 3437332
references.citation: PLoS Comput Biol. 2013;9(5):e1003079
references.pmid: 23737741
references.citation: J Neurosci. 1988 May;8(5):1610-24
references.pmid: 3367213
references.citation: Neuroimage. 2013 Dec;83:951-61
references.pmid: 23886984