Forward models demonstrate that repetition suppression is best modelled by local neural scaling

被引:25
作者
Alink, Arjen [1 ]
Abdulrahman, Hunar [2 ]
Henson, Richard N. [2 ]
机构
[1] Univ Med Ctr Hamburg Eppendorf, Dept Syst Neurosci, Martinistr 52, D-20246 Hamburg, Germany
[2] Univ Cambridge, Cognit & Brain Sci Unit, MRC, 15 Chaucer Rd, Cambridge CB2 7EF, England
基金
英国医学研究理事会;
关键词
PRIMARY VISUAL-CORTEX; FUSIFORM FACE AREA; FMRI-ADAPTATION; STIMULUS SELECTIVITY; REVEALS; BOLD; PERCEPTION; PLASTICITY; REPRESENTATIONS; MECHANISMS;
D O I
10.1038/s41467-018-05957-0
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Inferring neural mechanisms from functional magnetic resonance imaging (fMRI) is challenging because the fMRI signal integrates over millions of neurons. One approach is to compare computational models that map neural activity to fMRI responses, to see which best predicts fMRI data. We use this approach to compare four possible neural mechanisms of fMRI adaptation to repeated stimuli (scaling, sharpening, repulsive shifting and attractive shifting), acting across three domains (global, local and remote). Six features of fMRI repetition effects are identified, both univariate and multivariate, from two independent fMRI experiments. After searching over parameter values, only the local scaling model can simultaneously fit all data features from both experiments. Thus fMRI stimulus repetition effects are best captured by down-scaling neuronal tuning curves in proportion to the difference between the stimulus and neuronal preference. These results emphasise the importance of formal modelling for bridging neuronal and fMRI levels of investigation.
引用
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页数:10
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