Correlations and Neuronal Population Information

被引:225
作者
Kohn, Adam [1 ,2 ]
Coen-Cagli, Ruben [3 ]
Kanitscheider, Ingmar [3 ,4 ,5 ]
Pouget, Alexandre [3 ,6 ,7 ]
机构
[1] Albert Einstein Coll Med, Dominick Purpura Dept Neurosci, Bronx, NY 10461 USA
[2] Albert Einstein Coll Med, Dept Ophthalmol & Visual Sci, Bronx, NY 10461 USA
[3] Univ Geneva, Dept Basic Neurosci, CH-1211 Geneva, Switzerland
[4] Univ Texas Austin, Ctr Learning & Memory, Austin, TX 78712 USA
[5] Univ Texas Austin, Dept Neurosci, Austin, TX 78712 USA
[6] Univ Rochester, Dept Brain & Cognit Sci, 601 Elmwood Ave, Rochester, NY 14627 USA
[7] UCL, Gatsby Computat Neurosci Unit, London W1T 4JG, England
来源
ANNUAL REVIEW OF NEUROSCIENCE, VOL 39 | 2016年 / 39卷
关键词
neural coding; theoretical neuroscience; perception; Fisher information; decoding; neural variability; PRIMARY VISUAL-CORTEX; NOISE CORRELATIONS; BAYESIAN-INFERENCE; AREA MT; FUNCTIONAL-ORGANIZATION; ORIENTATION SELECTIVITY; CHOICE-PROBABILITIES; INPUT CORRELATIONS; CODING EFFICIENCY; CORTICAL-NEURONS;
D O I
10.1146/annurev-neuro-070815-013851
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Brain function involves the activity of neuronal populations. Much recent effort has been devoted to measuring the activity of neuronal populations in different parts of the brain under various experimental conditions. Population activity patterns contain rich structure, yet many studies have focused on measuring pairwise relationships between members of a larger population-termed noise correlations. Here we review recent progress in understanding how these correlations affect population information, how information should be quantified, and what mechanisms may give rise to correlations. As population coding theory has improved, it has made clear that some forms of correlation are more important for information than others. We argue that this is a critical lesson for those interested in neuronal population responses more generally: Descriptions of population responses should be motivated by and linked to well-specified function. Within this context, we offer suggestions of where current theoretical frameworks fall short.
引用
收藏
页码:237 / 256
页数:20
相关论文
共 133 条
[11]   Redundancy reduction revisited [J].
Barlow, H .
NETWORK-COMPUTATION IN NEURAL SYSTEMS, 2001, 12 (03) :241-253
[12]  
Beck J., 2012, Advances in Neural Information Processing Systems, V25, P3059
[13]   Insights from a Simple Expression for Linear Fisher Information in a Recurrently Connected Population of Spiking Neurons [J].
Beck, Jeffrey ;
Bejjanki, Vikranth R. ;
Pouget, Alexandre .
NEURAL COMPUTATION, 2011, 23 (06) :1484-1502
[14]   Not Noisy, Just Wrong: The Role of Suboptimal Inference in Behavioral Variability [J].
Beck, Jeffrey M. ;
Ma, Wei Ji ;
Pitkow, Xaq ;
Latham, Peter E. ;
Pouget, Alexandre .
NEURON, 2012, 74 (01) :30-39
[15]   Perceptual learning as improved probabilistic inference in early sensory areas [J].
Bejjanki, Vikranth R. ;
Beck, Jeffrey M. ;
Lu, Zhong-Lin ;
Pouget, Alexandre .
NATURE NEUROSCIENCE, 2011, 14 (05) :642-U139
[16]   A Fast and Simple Population Code for Orientation in Primate V1 [J].
Berens, Philipp ;
Ecker, Alexander S. ;
Cotton, R. James ;
Ma, Wei Ji ;
Bethge, Matthias ;
Tolias, Andreas S. .
JOURNAL OF NEUROSCIENCE, 2012, 32 (31) :10618-10626
[17]   Spontaneous Cortical Activity Reveals Hallmarks of an Optimal Internal Model of the Environment [J].
Berkes, Pietro ;
Orban, Gergo ;
Lengyel, Mate ;
Fiser, Jozsef .
SCIENCE, 2011, 331 (6013) :83-87
[18]  
Bosking WH, 1997, J NEUROSCI, V17, P2112
[19]   Role of Input Correlations in Shaping the Variability and Noise Correlations of Evoked Activity in the Neocortex [J].
Bujan, Alejandro F. ;
Aertsen, Ad ;
Kumar, Arvind .
JOURNAL OF NEUROSCIENCE, 2015, 35 (22) :8611-8625
[20]   Tuning curves, neuronal variability, and sensory coding [J].
Butts, DA ;
Goldman, MS .
PLOS BIOLOGY, 2006, 4 (04) :639-646