Feedback Determines the Structure of Correlated Variability in Primary Visual Cortex

The variable responses of sensory neurons tend to be weakly correlated (spike-count correlation, rsc). This is widely thought to reflect noise in shared afferents, in which case rsc can limit the reliability of sensory coding. However, it could also be due to feedback from higher-order brain regions. Currently, the relative contributions of these sources are unknown. We addressed this by recording from populations of V1 neurons in macaques performing different discrimination tasks involving the same visual input. We found that the structure of rsc (the way rsc varied with neuronal stimulus preference) changed systematically with task instruction. Therefore, even at the earliest stage in the cortical visual hierarchy, rsc structure during task performance primarily reflects feedback dynamics. Consequently, previous proposals for how rsc constrains sensory processing need not apply. Furthermore, we show that correlations between the activity of single neurons and choice depend on feedback engaged by the task.The way spike count variability is correlated between two neurons depends on the neurons' stimulus preferences. Here the authors show that this dependency itself varies systematically with behavioral task, implying a feedback origin of correlations.

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

[2]  H. Helmholtz.  Handbuch der physiologischen Optik , 2015 .

[3]  B. Cumming,et al.  Psychophysically measured task strategy for disparity discrimination is reflected in V2 neurons , 2007, Nature Neuroscience.

[4]  Richard D. Lange,et al.  Inferring the brain’s internal model from sensory responses in a probabilistic inference framework , 2016, bioRxiv.

[5]  P. O. Bishop,et al.  Orientation specificity and response variability of cells in the striate cortex. , 1973, Vision research.

[6]  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.

[7]  Carrie J. McAdams,et al.  Effects of Attention on Orientation-Tuning Functions of Single Neurons in Macaque Cortical Area V4 , 1999, The Journal of Neuroscience.

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

[9]  K O Johnson,et al.  Sensory discrimination: decision process. , 1980, Journal of neurophysiology.

[10]  Bruce G Cumming,et al.  Feedforward and Feedback Sources of Choice Probability in Neural Population Responses This Review Comes from a Themed Issue on Neurobiology of Cognitive Behavior Evidence for Feed-forward Models and Optimal Linear Readout? , 2022 .

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

[12]  A. Sillito,et al.  Always returning: feedback and sensory processing in visual cortex and thalamus , 2006, Trends in Neurosciences.

[13]  M. Posner,et al.  The attention system of the human brain. , 1990, Annual review of neuroscience.

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

[15]  N. Higham.  COMPUTING A NEAREST SYMMETRIC POSITIVE SEMIDEFINITE MATRIX , 1988 .

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

[17]  Edward M. Callaway,et al.  Feedforward, feedback and inhibitory connections in primate visual cortex , 2004, Neural Networks.

[18]  Alexander S. Ecker,et al.  On the Structure of Neuronal Population Activity under Fluctuations in Attentional State , 2015, The Journal of Neuroscience.

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

[20]  Bruce Cumming,et al.  Correlations between the activity of sensory neurons and behavior: how much do they tell us about a neuron's causality? , 2010, Current Opinion in Neurobiology.

[21]  G. J. Tomko,et al.  Neuronal variability: non-stationary responses to identical visual stimuli. , 1974, Brain research.

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

[23]  Bruce G Cumming,et al.  Decision-related activity in sensory neurons: correlations among neurons and with behavior. , 2012, Annual review of neuroscience.

[24]  Jan J. Koenderink,et al.  Information in channel-coded systems: correlated receivers , 1992, Biological Cybernetics.

[25]  A. P. Georgopoulos,et al.  Variability and Correlated Noise in the Discharge of Neurons in Motor and Parietal Areas of the Primate Cortex , 1998, The Journal of Neuroscience.

[26]  Michele A Basso,et al.  Insights into decision making using choice probability. , 2015, Journal of neurophysiology.

[27]  S. E. Kwon,et al.  Sensory and decision-related activity propagate in a cortical feedback loop during touch perception , 2016, Nature Neuroscience.

[28]  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.

[29]  W. Newsome,et al.  Context-Dependent Changes in Functional Circuitry in Visual Area MT , 2008, Neuron.

[30]  A. Parker,et al.  Binocular Neurons in V1 of Awake Monkeys Are Selective for Absolute, Not Relative, Disparity , 1999, The Journal of Neuroscience.

[31]  Stefan Treue,et al.  Feature-based attention influences motion processing gain in macaque visual cortex , 1999, Nature.

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

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

[34]  W. James,et al.  The Principles of Psychology. , 1983 .

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

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

[37]  A E Burgess,et al.  Visual signal detection. IV. Observer inconsistency. , 1988, Journal of the Optical Society of America. A, Optics and image science.

[38]  Bin Tean Teh,et al.  Origins and functional consequences of somatic mitochondrial DNA mutations in human cancer , 2014, eLife.

[39]  Adam Binch,et al.  Perception as Bayesian Inference , 2014 .

[40]  B. Richmond,et al.  Implantation of magnetic search coils for measurement of eye position: An improved method , 1980, Vision Research.

[41]  M. Bethge,et al.  Inferring decoding strategies from choice probabilities in the presence of correlated variability , 2013, Nature Neuroscience.

[42]  József Fiser,et al.  Perceptual Decision-Making as Probabilistic Inference by Neural Sampling , 2014, Neuron.

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

[44]  K. H. Britten,et al.  A relationship between behavioral choice and the visual responses of neurons in macaque MT , 1996, Visual Neuroscience.

[45]  A. Ahumada.  Perceptual Classification Images from Vernier Acuity Masked by Noise , 1996 .

[46]  Hideyuki Suzuki,et al.  Population Code Dynamics in Categorical Perception , 2016, Scientific Reports.

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

[48]  Arnulf B. A. Graf,et al.  Decoding the activity of neuronal populations in macaque primary visual cortex , 2011, Nature Neuroscience.

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

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

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

[52]  Dora E Angelaki,et al.  Reduced choice-related activity and correlated noise accompany perceptual deficits following unilateral vestibular lesion , 2013, Proceedings of the National Academy of Sciences.

[53]  Yong Gu,et al.  Contribution of correlated noise and selective decoding to choice probability measurements in extrastriate visual cortex , 2014, eLife.

[54]  John H R Maunsell,et al.  Potential confounds in estimating trial-to-trial correlations between neuronal response and behavior using choice probabilities. , 2012, Journal of neurophysiology.

[55]  Bruce G. Cumming,et al.  Decision-Related Activity in Sensory Neurons May Depend on the Columnar Architecture of Cerebral Cortex , 2014, The Journal of Neuroscience.