Extracting information from neuronal populations: information theory and decoding approaches

被引:560
作者
Quiroga, Rodrigo Quian [1 ]
Panzeri, Stefano [2 ,3 ]
机构
[1] Univ Leicester, Dept Engn, Leicester LE1 7RH, Leics, England
[2] Italian Inst Technol, Dept Robot Brain & Cognit Sci, I-16163 Genoa, Italy
[3] Univ Manchester, Fac Life Sci, Manchester M13 9PL, Lancs, England
基金
英国医学研究理事会; 英国工程与自然科学研究理事会;
关键词
POSTERIOR PARIETAL CORTEX; LOCAL-FIELD POTENTIALS; PRIMARY VISUAL-CORTEX; HIPPOCAMPAL PYRAMIDAL CELLS; INFERIOR TEMPORAL CORTEX; CORTICAL-NEURONS; SPIKE TRAIN; NEURAL PROSTHETICS; ASSOCIATION CORTEX; STIMULUS LOCATION;
D O I
10.1038/nrn2578
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
To a large extent, progress in neuroscience has been driven by the study of single-cell responses averaged over several repetitions of stimuli or behaviours. However, the brain typically makes decisions based on single events by evaluating the activity of large neuronal populations. Therefore, to further understand how the brain processes information, it is important to shift from a single-neuron, multiple-trial framework to multiple-neuron, single-trial methodologies. Two related approaches-decoding and information theory can be used to extract single-trial information from the activity of neuronal populations. Such population analysis can give us more information about how neurons encode stimulus features than traditional single-cell studies.
引用
收藏
页码:173 / 185
页数:13
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