Information-Limiting Correlations in Large Neural Populations

Understanding the neural code requires understanding how populations of neurons code information. Theoretical models predict that information may be limited by correlated noise in large neural populations. Nevertheless, analyses based on tens of neurons have failed to find evidence of saturation. Moreover, some studies have shown that noise correlations can be very small, and therefore may not affect information coding. Understanding the neural code requires understanding how populations of neurons code information. Theoretical models predict that information may be limited by correlated noise in large neural populations. Nevertheless, analyses based on tens of neurons have failed to find evidence of saturation. Moreover, some studies have shown that noise correlations can be very small, and therefore may not affect information coding. To determine whether information-limiting correlations exist, we implanted eight Utah arrays in prefrontal cortex (PFC; area 46) of two male macaque monkeys, recording >500 neurons simultaneously. We estimated information in PFC about saccades as a function of ensemble size. Noise correlations were, on average, small (∼10−3). However, information scaled strongly sublinearly with ensemble size. After shuffling trials, destroying noise correlations, information was a linear function of ensemble size. Thus, we provide evidence for the existence of information-limiting noise correlations in large populations of PFC neurons. SIGNIFICANCE STATEMENT Recent theoretical work has shown that even small correlations can limit information if they are “differential correlations,” which are difficult to measure directly. However, they can be detected through decoding analyses on recordings from a large number of neurons over a large number of trials. We have achieved both by collecting neural activity in dorsal-lateral prefrontal cortex of macaques using eight microelectrode arrays (768 electrodes), from which we were able to compute accurate information estimates. We show, for the first time, strong evidence for information-limiting correlations. Despite pairwise correlations being small (on the order of 10−3), they affect information coding in populations on the order of 100 s of neurons.

[1]  Haim Sompolinsky,et al.  Nonlinear Population Codes , 2004, Neural Computation.

[2]  Adam J. Sachs,et al.  Correlated variability modifies working memory fidelity in primate prefrontal neuronal ensembles , 2017, Proceedings of the National Academy of Sciences.

[3]  D. Sparks,et al.  Population coding of saccadic eye movements by neurons in the superior colliculus , 1988, Nature.

[4]  Xiao-Jing Wang,et al.  The importance of mixed selectivity in complex cognitive tasks , 2013, Nature.

[5]  M. Cohen,et al.  Measuring and interpreting neuronal correlations , 2011, Nature Neuroscience.

[6]  Apostolos P. Georgopoulos,et al.  Neural activity in prefrontal cortex during copying geometrical shapes , 2003, Experimental Brain Research.

[7]  K. Miller,et al.  One-Dimensional Dynamics of Attention and Decision Making in LIP , 2008, Neuron.

[8]  T. Papaioannou.  Information, Measures of , 2006 .

[9]  S. Panzeri,et al.  An exact method to quantify the information transmitted by different mechanisms of correlational coding. , 2003, Network.

[10]  Douglas A Ruff,et al.  Attention can increase or decrease spike count correlations between pairs of neurons depending on their role in a task , 2014, Nature Neuroscience.

[11]  William Bialek,et al.  Spikes: Exploring the Neural Code , 1996 .

[12]  Bruno B. Averbeck,et al.  Noise Correlations and Information Encoding and Decoding , 2009 .

[13]  S. Kay.  Fundamentals of statistical signal processing: estimation theory , 1993 .

[14]  Hsin-Hao Yu,et al.  Correlated Variability in the Neurons With the Strongest Tuning Improves Direction Coding , 2019, Cerebral cortex.

[15]  B. Averbeck,et al.  Action Selection and Action Value in Frontal-Striatal Circuits , 2012, Neuron.

[16]  A. P. Georgopoulos,et al.  Neuronal population coding of movement direction. , 1986, Science.

[17]  Rava Azeredo da Silveira,et al.  Structures of Neural Correlation and How They Favor Coding , 2016, Neuron.

[18]  Y. Dan,et al.  Coding of visual information by precisely correlated spikes in the lateral geniculate nucleus , 1998, Nature Neuroscience.

[19]  Maxwell H. Turner,et al.  Direction-Selective Circuits Shape Noise to Ensure a Precise Population Code , 2016, Neuron.

[20]  M. A. Smith,et al.  Spatial and Temporal Scales of Neuronal Correlation in Primary Visual Cortex , 2008, The Journal of Neuroscience.

[21]  A. Grinvald,et al.  Linking spontaneous activity of single cortical neurons and the underlying functional architecture. , 1999, Science.

[22]  R. Romo,et al.  Correlated Neuronal Discharges that Increase Coding Efficiency during Perceptual Discrimination , 2003, Neuron.

[23]  Julio C. Martinez-Trujillo,et al.  Structure of Spike Count Correlations Reveals Functional Interactions between Neurons in Dorsolateral Prefrontal Cortex Area 8a of Behaving Primates , 2013, PloS one.

[24]  Yueh-Peng Chen,et al.  Network Anisotropy Trumps Noise for Efficient Object Coding in Macaque Inferior Temporal Cortex. , 2015, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[25]  Maureen A. Hagan,et al.  Neuronal Correlations in MT and MST Impair Population Decoding of Opposite Directions of Random Dot Motion , 2018, eNeuro.

[26]  E T Rolls,et al.  Correlations and the encoding of information in the nervous system , 1999, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[27]  Timothy D. Hanks,et al.  Probabilistic Population Codes for Bayesian Decision Making , 2008, Neuron.

[28]  M. A. Smith,et al.  Stimulus Dependence of Neuronal Correlation in Primary Visual Cortex of the Macaque , 2005, The Journal of Neuroscience.

[29]  Peter Dayan,et al.  The Effect of Correlated Variability on the Accuracy of a Population Code , 1999, Neural Computation.

[30]  D. Sparks,et al.  Size and distribution of movement fields in the monkey superior colliculus , 1976, Brain Research.

[31]  J. Maunsell,et al.  Using Neuronal Populations to Study the Mechanisms Underlying Spatial and Feature Attention , 2011, Neuron.

[32]  A. Pouget,et al.  Neural correlations, population coding and computation , 2006, Nature Reviews Neuroscience.

[33]  T. Poggio.  A theory of how the brain might work. , 1990, Cold Spring Harbor symposia on quantitative biology.

[34]  Brent Doiron,et al.  Scaling Properties of Dimensionality Reduction for Neural Populations and Network Models , 2016, PLoS Comput. Biol..

[35]  TJ Gawne,et al.  How independent are the messages carried by adjacent inferior temporal cortical neurons? , 1993, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[36]  Arnulf B. A. Graf,et al.  Predicting oculomotor behaviour from correlated populations of posterior parietal neurons , 2014, Nature Communications.

[37]  W. Bair,et al.  Correlated Firing in Macaque Visual Area MT: Time Scales and Relationship to Behavior , 2001, The Journal of Neuroscience.

[38]  Naoshige Uchida,et al.  Demixed principal component analysis of neural population data , 2014, eLife.

[39]  J. Barry Richmond,et al.  Neural Coding , 2014, Encyclopedia of Computational Neuroscience.

[40]  Alexandre Pouget,et al.  Origin of information-limiting noise correlations , 2015, Proceedings of the National Academy of Sciences.

[41]  John P. Cunningham,et al.  Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity , 2008, NIPS.

[42]  P. Latham,et al.  Retinal ganglion cells act largely as independent encoders , 2001, Nature.

[43]  W. Singer,et al.  Stimulus-specific neuronal oscillations in orientation columns of cat visual cortex. , 1989, Proceedings of the National Academy of Sciences of the United States of America.

[44]  H. Sompolinsky,et al.  Population coding in neuronal systems with correlated noise. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[45]  T. Bullock.  Neuron doctrine and electrophysiology. , 1959, Science.

[46]  W. Newsome,et al.  Context-dependent computation by recurrent dynamics in prefrontal cortex , 2013, Nature.

[47]  Cyriel M A Pennartz,et al.  Population-Level Neural Codes Are Robust to Single-Neuron Variability from a Multidimensional Coding Perspective. , 2016, Cell reports.

[48]  P. Dayan,et al.  Supporting Online Material Materials and Methods Som Text Figs. S1 to S9 References the Asynchronous State in Cortical Circuits , 2022 .

[49]  M. Mishkin,et al.  Spontaneous High-Gamma Band Activity Reflects Functional Organization of Auditory Cortex in the Awake Macaque , 2012, Neuron.

[50]  Kathryn B. Laskey,et al.  Neural Coding: Higher-Order Temporal Patterns in the Neurostatistics of Cell Assemblies , 2000, Neural Computation.

[51]  Daeyeol Lee,et al.  Neural Noise and Movement-Related Codes in the Macaque Supplementary Motor Area , 2003, The Journal of Neuroscience.

[52]  Matthew T. Kaufman,et al.  Neural population dynamics during reaching , 2012, Nature.

[53]  Carlos Garcia,et al.  A simple procedure for the comparison of covariance matrices , 2012, BMC Evolutionary Biology.

[54]  Ha Hong,et al.  Performance-optimized hierarchical models predict neural responses in higher visual cortex , 2014, Proceedings of the National Academy of Sciences.

[55]  Haim Sompolinsky,et al.  Implications of Neuronal Diversity on Population Coding , 2006, Neural Computation.

[56]  A. Pouget,et al.  Information-limiting correlations , 2014, Nature Neuroscience.

[57]  Daeyeol Lee,et al.  Coding and transmission of information by neural ensembles , 2004, Trends in Neurosciences.

[58]  Daeyeol Lee,et al.  Effects of noise correlations on information encoding and decoding. , 2006, Journal of neurophysiology.

[59]  A. Pouget,et al.  Correlations and Neuronal Population Information. , 2016, Annual review of neuroscience.

[60]  Amy M. Ni,et al.  Learning and attention reveal a general relationship between population activity and behavior , 2018, Science.

[61]  Sébastien Tremblay,et al.  Attentional Filtering of Visual Information by Neuronal Ensembles in the Primate Lateral Prefrontal Cortex , 2015, Neuron.

[62]  Alexandre Pouget,et al.  Measuring Fisher Information Accurately in Correlated Neural Populations , 2015, PLoS Comput. Biol..

[63]  A. Grinvald,et al.  Spontaneously emerging cortical representations of visual attributes , 2003, Nature.

[64]  J. Maunsell,et al.  Attention improves performance primarily by reducing interneuronal correlations , 2009, Nature Neuroscience.

[65]  Ehud Zohary,et al.  Correlated neuronal discharge rate and its implications for psychophysical performance , 1994, Nature.

[66]  M. Paradiso,et al.  A theory for the use of visual orientation information which exploits the columnar structure of striate cortex , 2004, Biological Cybernetics.

[67]  A. Pouget,et al.  Efficient computation and cue integration with noisy population codes , 2001, Nature Neuroscience.

[68]  Daeyeol Lee,et al.  Activity in prefrontal cortex during dynamic selection of action sequences , 2006, Nature Neuroscience.

[69]  Andrew R. Mitz,et al.  High channel count single-unit recordings from nonhuman primate frontal cortex , 2017, Journal of Neuroscience Methods.

[70]  M. A. Smith,et al.  The spatial structure of correlated neuronal variability , 2016, Nature Neuroscience.

[71]  A. Pouget,et al.  Tuning curve sharpening for orientation selectivity: coding efficiency and the impact of correlations , 2004, Nature Neuroscience.

[72]  Alireza Soltani,et al.  Selective Changes in Noise Correlations Contribute to an Enhanced Representation of Saccadic Targets in Prefrontal Neuronal Ensembles , 2018, Cerebral cortex.

[73]  Alexander S. Ecker,et al.  Decorrelated Neuronal Firing in Cortical Microcircuits , 2010, Science.

[74]  E. D. Adrian,et al.  The Basis of Sensation , 1928, The Indian Medical Gazette.

[75]  Surya Ganguli,et al.  On simplicity and complexity in the brave new world of large-scale neuroscience , 2015, Current Opinion in Neurobiology.

[76]  W. Graf,et al.  Oculomotor Areas of the Primate Frontal Lobes: A Transneuronal Transfer of Rabies Virus and [14C]-2-Deoxyglucose Functional Imaging Study , 2004, The Journal of Neuroscience.

[77]  Yong Gu,et al.  Perceptual Learning Reduces Interneuronal Correlations in Macaque Visual Cortex , 2011, Neuron.