Local field potentials indicate network state and account for neuronal response variability

Multineuronal recordings have revealed that neurons in primary visual cortex (V1) exhibit coordinated fluctuations of spiking activity in the absence and in the presence of visual stimulation. From the perspective of understanding a single cell’s spiking activity relative to a behavior or stimulus, these network fluctuations are typically considered to be noise. We show that these events are highly correlated with another commonly recorded signal, the local field potential (LFP), and are also likely related to global network state phenomena which have been observed in a number of neural systems. Moreover, we show that attributing a component of cell firing to these network fluctuations via explicit modeling of the LFP improves the recovery of cell properties. This suggests that the impact of network fluctuations may be estimated using the LFP, and that a portion of this network activity is unrelated to the stimulus and instead reflects ongoing cortical activity. Thus, the LFP acts as an easily accessible bridge between the network state and the spiking activity.

[1]  Roman Bauer,et al.  Fast oscillations display sharper orientation tuning than slower components of the same recordings in striate cortex of the awake monkey , 2000, The European journal of neuroscience.

[2]  Jonathon Shlens,et al.  The Structure of Large-Scale Synchronized Firing in Primate Retina , 2009, The Journal of Neuroscience.

[3]  Xin Huang,et al.  Noise correlations in cortical area MT and their potential impact on trial-by-trial variation in the direction and speed of smooth-pursuit eye movements. , 2009, Journal of neurophysiology.

[4]  Biyu J. He,et al.  Electrophysiological correlates of the brain's intrinsic large-scale functional architecture , 2008, Proceedings of the National Academy of Sciences.

[5]  Roger Newson,et al.  Review of Generalized Linear Models and Extensions by Hardin and Hilbe , 2001 .

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

[7]  U. Mitzdorf Properties of the evoked potential generators: current source-density analysis of visually evoked potentials in the cat cortex. , 1987, The International journal of neuroscience.

[8]  Alexander S. Ecker,et al.  Feature Selectivity of the Gamma-Band of the Local Field Potential in Primate Primary Visual Cortex , 2008, Front. Neurosci..

[9]  R. Shapley,et al.  LFP power spectra in V1 cortex: the graded effect of stimulus contrast. , 2005, Journal of neurophysiology.

[10]  M. Carandini,et al.  Local Origin of Field Potentials in Visual Cortex , 2009, Neuron.

[11]  J J Eggermont,et al.  Synchrony between single-unit activity and local field potentials in relation to periodicity coding in primary auditory cortex. , 1995, Journal of neurophysiology.

[12]  I. Fried,et al.  Interhemispheric correlations of slow spontaneous neuronal fluctuations revealed in human sensory cortex , 2008, Nature Neuroscience.

[13]  Konrad Paul Kording,et al.  How are complex cell properties adapted to the statistics of natural stimuli? , 2004, Journal of neurophysiology.

[14]  Dean V Buonomano,et al.  Development and Plasticity of Spontaneous Activity and Up States in Cortical Organotypic Slices , 2007, The Journal of Neuroscience.

[15]  T. Poggio,et al.  Object Selectivity of Local Field Potentials and Spikes in the Macaque Inferior Temporal Cortex , 2006, Neuron.

[16]  D. Ferster,et al.  Synchronous Membrane Potential Fluctuations in Neurons of the Cat Visual Cortex , 1999, Neuron.

[17]  J. Anthony Movshon,et al.  Comparison of Recordings from Microelectrode Arrays and Single Electrodes in the Visual Cortex , 2007, The Journal of Neuroscience.

[18]  E. Brown,et al.  Analysis of LFP phase predicts sensory response of barrel cortex. , 2006, Journal of neurophysiology.

[19]  Alain Destexhe,et al.  Neuronal Computations with Stochastic Network States , 2006, Science.

[20]  W. Newsome,et al.  The Variable Discharge of Cortical Neurons: Implications for Connectivity, Computation, and Information Coding , 1998, 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. Shapley,et al.  Spatial Spread of the Local Field Potential and its Laminar Variation in Visual Cortex , 2009, The Journal of Neuroscience.

[23]  L. Paninski Maximum likelihood estimation of cascade point-process neural encoding models , 2004, Network.

[24]  J. Hardin,et al.  Generalized Linear Models and Extensions , 2001 .

[25]  R. Normann,et al.  A method for pneumatically inserting an array of penetrating electrodes into cortical tissue , 2006, Annals of Biomedical Engineering.

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

[27]  Stephen A. Engel,et al.  FMRI measurements of changes in color and orientation tuning in V1 , 2002 .

[28]  Liam Paninski,et al.  Statistical models for neural encoding, decoding, and optimal stimulus design. , 2007, Progress in brain research.

[29]  Eero P. Simoncelli,et al.  Spatio-temporal correlations and visual signalling in a complete neuronal population , 2008, Nature.

[30]  Arthur Gretton,et al.  Inferring spike trains from local field potentials. , 2008, Journal of neurophysiology.

[31]  A. B. Bonds,et al.  Gamma oscillation maintains stimulus structure-dependent synchronization in cat visual cortex. , 2005, Journal of neurophysiology.

[32]  B. McNaughton,et al.  Tetrodes markedly improve the reliability and yield of multiple single-unit isolation from multi-unit recordings in cat striate cortex , 1995, Journal of Neuroscience Methods.

[33]  D. G. Albrecht,et al.  Spatial frequency selectivity of cells in macaque visual cortex , 1982, Vision Research.

[34]  Anthony J. Movshon,et al.  Signals in Macaque Striate Cortical Neurons that Support the Perception of Glass Patterns , 2002, The Journal of Neuroscience.

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

[36]  Emery N. Brown,et al.  Statistical models of spike trains , 2008 .

[37]  A. Grinvald,et al.  Spatiotemporal Dynamics of Sensory Responses in Layer 2/3 of Rat Barrel Cortex Measured In Vivo by Voltage-Sensitive Dye Imaging Combined with Whole-Cell Voltage Recordings and Neuron Reconstructions , 2003, The Journal of Neuroscience.

[38]  A. Grinvald,et al.  Dynamics of Ongoing Activity: Explanation of the Large Variability in Evoked Cortical Responses , 1996, Science.

[39]  Robert E. Kass,et al.  A Spike-Train Probability Model , 2001, Neural Computation.

[40]  M. Carandini,et al.  Stimulus contrast modulates functional connectivity in visual cortex , 2009, Nature Neuroscience.

[41]  M. A. Smith,et al.  Correlations and brain states: from electrophysiology to functional imaging , 2009, Current Opinion in Neurobiology.

[42]  J. Movshon,et al.  Nature and interaction of signals from the receptive field center and surround in macaque V1 neurons. , 2002, Journal of neurophysiology.

[43]  Robert Shapley,et al.  Receptive field structure of neurons in monkey primary visual cortex revealed by stimulation with natural image sequences. , 2002, Journal of vision.

[44]  W. Newsome,et al.  Local Field Potential in Cortical Area MT: Stimulus Tuning and Behavioral Correlations , 2006, The Journal of Neuroscience.

[45]  Jonathan W. Pillow,et al.  Likelihood-based approaches to modeling the neural code , 2007 .

[46]  D. Pollen,et al.  Spatial and temporal frequency selectivity of neurones in visual cortical areas V1 and V2 of the macaque monkey. , 1985, The Journal of physiology.

[47]  N. Logothetis,et al.  Very slow activity fluctuations in monkey visual cortex: implications for functional brain imaging. , 2003, Cerebral cortex.

[48]  R Eckhorn,et al.  Inhibition of sustained gamma oscillations (35-80 Hz) by fast transient responses in cat visual cortex. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[49]  Shy Shoham,et al.  Robust, automatic spike sorting using mixtures of multivariate t-distributions , 2003, Journal of Neuroscience Methods.

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

[51]  J. Gallant,et al.  Natural Stimulus Statistics Alter the Receptive Field Structure of V1 Neurons , 2004, The Journal of Neuroscience.

[52]  Peter E. Latham,et al.  Neural characterization in partially observed populations of spiking neurons , 2007, NIPS.

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

[54]  H. Vaughan,et al.  Averaged multiple unit activity as an estimate of phasic changes in local neuronal activity: effects of volume-conducted potentials , 1980, Journal of Neuroscience Methods.

[55]  P. König,et al.  A Functional Gamma-Band Defined by Stimulus-Dependent Synchronization in Area 18 of Awake Behaving Cats , 2003, The Journal of Neuroscience.

[56]  G. Buzsáki Large-scale recording of neuronal ensembles , 2004, Nature Neuroscience.

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

[58]  G. Buzsáki,et al.  Sequential structure of neocortical spontaneous activity in vivo , 2007, Proceedings of the National Academy of Sciences.