Attention stabilizes the shared gain of V4 populations

Responses of sensory neurons represent stimulus information, but are also influenced by internal state. For example, when monkeys direct their attention to a visual stimulus, the response gain of specific subsets of neurons in visual cortex changes. Here, we develop a functional model of population activity to investigate the structure of this effect. We fit the model to the spiking activity of bilateral neural populations in area V4, recorded while the animal performed a stimulus discrimination task under spatial attention. The model reveals four separate time-varying shared modulatory signals, the dominant two of which each target task-relevant neurons in one hemisphere. In attention-directed conditions, the associated shared modulatory signal decreases in variance. This finding provides an interpretable and parsimonious explanation for previous observations that attention reduces variability and noise correlations of sensory neurons. Finally, the recovered modulatory signals reflect previous reward, and are predictive of subsequent choice behavior. DOI: http://dx.doi.org/10.7554/eLife.08998.001

[1]  V. L. Senders,et al.  Analysis of response sequences in the setting of a psychophysical experiment. , 1952, The American journal of psychology.

[2]  D. M. Green,et al.  CONSISTENCY OF AUDITORY DETECTION JUDGMENTS. , 1963, Psychology Review.

[3]  A. Levey,et al.  Cholinergic innervation of cortex by the basal forebrain: Cytochemistry and cortical connections of the septal area, diagonal band nuclei, nucleus basalis (Substantia innominata), and hypothalamus in the rhesus monkey , 1983, The Journal of comparative neurology.

[4]  R. Desimone,et al.  Selective attention gates visual processing in the extrastriate cortex. , 1985, Science.

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

[6]  John H. R. Maunsell,et al.  Attentional modulation of visual motion processing in cortical areas MT and MST , 1996, Nature.

[7]  J. Movshon,et al.  A computational analysis of the relationship between neuronal and behavioral responses to visual motion , 1996, The Journal of neuroscience : the official journal of the Society for Neuroscience.

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

[9]  W. Newsome,et al.  The Variable Discharge of Cortical Neurons: Implications for Connectivity, Computation, and Information Coding , 1998, The Journal of Neuroscience.

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

[11]  Carrie J. McAdams,et al.  Effects of Attention on the Reliability of Individual Neurons in Monkey Visual Cortex , 1999, Neuron.

[12]  Carlos D. Brody,et al.  Correlations Without Synchrony , 1999, Neural Computation.

[13]  J. Maunsell,et al.  Effects of Attention on the Processing of Motion in Macaque Middle Temporal and Medial Superior Temporal Visual Cortical Areas , 1999, The Journal of Neuroscience.

[14]  R. Desimone,et al.  Modulation of Oscillatory Neuronal Synchronization by Selective Visual Attention , 2001, Science.

[15]  Felix Wichmann,et al.  The psychometric function: I , 2001 .

[16]  Sanjoy Dasgupta,et al.  A Generalization of Principal Components Analysis to the Exponential Family , 2001, NIPS.

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

[18]  F A Wichmann,et al.  Ning for Helpful Comments and Suggestions. This Paper Benefited Con- Siderably from Conscientious Peer Review, and We Thank Our Reviewers the Psychometric Function: I. Fitting, Sampling, and Goodness of Fit , 2001 .

[19]  S. Treue,et al.  Attentional Modulation Strength in Cortical Area MT Depends on Stimulus Contrast , 2002, Neuron.

[20]  Christian W. Eurich,et al.  Representational Accuracy of Stochastic Neural Populations , 2002, Neural Computation.

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

[22]  G. Laurent,et al.  Transient Dynamics versus Fixed Points in Odor Representations by Locust Antennal Lobe Projection Neurons , 2005, Neuron.

[23]  Uri T Eden,et al.  A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects. , 2005, Journal of neurophysiology.

[24]  P. Glimcher,et al.  JOURNAL OF THE EXPERIMENTAL ANALYSIS OF BEHAVIOR 2005, 84, 555–579 NUMBER 3(NOVEMBER) DYNAMIC RESPONSE-BY-RESPONSE MODELS OF MATCHING BEHAVIOR IN RHESUS MONKEYS , 2022 .

[25]  J. Maunsell,et al.  Effects of spatial attention on contrast response functions in macaque area V4. , 2006, Journal of neurophysiology.

[26]  V. Jayaraman,et al.  Encoding and Decoding of Overlapping Odor Sequences , 2006, Neuron.

[27]  L. Paninski,et al.  Common-input models for multiple neural spike-train data , 2007, Network.

[28]  Michael N. Shadlen,et al.  Probabilistic reasoning by neurons , 2007, Nature.

[29]  Jaime de la Rocha,et al.  Supplementary Information for the article ‘ Correlation between neural spike trains increases with firing rate ’ , 2007 .

[30]  M. Hawken,et al.  Gain Modulation by Nicotine in Macaque V1 , 2007, Neuron.

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

[32]  Louise S. Delicato,et al.  Acetylcholine contributes through muscarinic receptors to attentional modulation in V1 , 2008, Nature.

[33]  J. Poulet,et al.  Internal brain state regulates membrane potential synchrony in barrel cortex of behaving mice , 2008, Nature.

[34]  A. Engel,et al.  Neuronal Synchronization along the Dorsal Visual Pathway Reflects the Focus of Spatial Attention , 2008, Neuron.

[35]  D. Heeger,et al.  The Normalization Model of Attention , 2009, Neuron.

[36]  B. Cumming,et al.  Decision-related activity in sensory neurons reflects more than a neuron’s causal effect , 2009, Nature.

[37]  Joonyeol Lee,et al.  A Normalization Model of Attentional Modulation of Single Unit Responses , 2009, PloS one.

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

[39]  Jude F. Mitchell,et al.  Spatial Attention Decorrelates Intrinsic Activity Fluctuations in Macaque Area V4 , 2009, Neuron.

[40]  Wei Wu,et al.  A new look at state-space models for neural data , 2010, Journal of Computational Neuroscience.

[41]  Andrew M. Clark,et al.  Stimulus onset quenches neural variability: a widespread cortical phenomenon , 2010, Nature Neuroscience.

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

[43]  Konrad P. Kording,et al.  Sensory Adaptation and Short Term Plasticity as Bayesian Correction for a Changing Brain , 2010, PloS one.

[44]  J. Maunsell,et al.  A Neuronal Population Measure of Attention Predicts Behavioral Performance on Individual Trials , 2010, The Journal of Neuroscience.

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

[46]  D. Whitney,et al.  Serial dependence in visual perception , 2011 .

[47]  Mijung Park,et al.  Receptive Field Inference with Localized Priors , 2011, PLoS Comput. Biol..

[48]  John P. Cunningham,et al.  Empirical models of spiking in neural populations , 2011, NIPS.

[49]  Andrew D. Zaharia,et al.  The Detection of Visual Contrast in the Behaving Mouse , 2011, The Journal of Neuroscience.

[50]  K. Harris,et al.  Cortical state and attention , 2011, Nature Reviews Neuroscience.

[51]  Eero P. Simoncelli,et al.  Modeling the Impact of Common Noise Inputs on the Network Activity of Retinal Ganglion Cells Action Editor: Brent Doiron , 2022 .

[52]  The correlation structure induced by fluctuations in attention , 2012 .

[53]  A. Litwin-Kumar,et al.  Slow dynamics and high variability in balanced cortical networks with clustered connections , 2012, Nature Neuroscience.

[54]  Mijung Park,et al.  Bayesian inference for low rank spatiotemporal neural receptive fields , 2013, NIPS.

[55]  N. Sigala,et al.  Dynamic Coding for Cognitive Control in Prefrontal Cortex , 2013, Neuron.

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

[57]  David Pfau,et al.  Robust learning of low-dimensional dynamics from large neural ensembles , 2013, NIPS.

[58]  Alexander Thiele,et al.  Attention-Induced Variance and Noise Correlation Reduction in Macaque V1 Is Mediated by NMDA Receptors , 2013, Neuron.

[59]  Timothy Q. Gentner,et al.  Associative Learning Enhances Population Coding by Inverting Interneuronal Correlation Patterns , 2013, Neuron.

[60]  Victor Solo,et al.  Point-Process Principal Components Analysis via Geometric Optimization , 2013, Neural Computation.

[61]  Jakob H. Macke,et al.  Low-dimensional models of neural population activity in sensory cortical circuits , 2014, NIPS.

[62]  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.

[63]  Douglas A Ruff,et al.  Global Cognitive Factors Modulate Correlated Response Variability between V4 Neurons , 2014, The Journal of Neuroscience.

[64]  Eero P. Simoncelli,et al.  Partitioning neuronal variability , 2014, Nature Neuroscience.

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

[66]  George H. Denfield,et al.  Pupil Fluctuations Track Fast Switching of Cortical States during Quiet Wakefulness , 2014, Neuron.

[67]  Alexander S. Ecker,et al.  State Dependence of Noise Correlations in Macaque Primary Visual Cortex , 2014, Neuron.

[68]  Byron M. Yu,et al.  Dimensionality reduction for large-scale neural recordings , 2014, Nature Neuroscience.

[69]  Matthew T. Kaufman,et al.  Supplementary materials for : Cortical activity in the null space : permitting preparation without movement , 2014 .

[70]  M. Carandini,et al.  The Nature of Shared Cortical Variability , 2015, Neuron.

[71]  Mamiko Niwa,et al.  Task Engagement Selectively Modulates Neural Correlations in Primary Auditory Cortex , 2015, The Journal of Neuroscience.

[72]  Stephen V. David,et al.  Cortical Membrane Potential Signature of Optimal States for Sensory Signal Detection , 2015, Neuron.

[73]  Eero P. Simoncelli,et al.  A model of sensory neural responses in the presence of unknown modulatory inputs , 2015, 1507.01497.

[74]  Alexander S. Ecker,et al.  Improved Estimation and Interpretation of Correlations in Neural Circuits , 2015, PLoS Comput. Biol..

[75]  Nicholas A. Steinmetz,et al.  Diverse coupling of neurons to populations in sensory cortex , 2015, Nature.

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

[77]  M. Carandini,et al.  Cortical State Determines Global Variability and Correlations in Visual Cortex , 2015, The Journal of Neuroscience.

[78]  Perceptual Decision-Making as Probabilistic Inference by Neural Sampling , 2014, Neuron.