A Sparse Probabilistic Code Underlies the Limits of Behavioral Discrimination

Abstract The cortical code that underlies perception must enable subjects to perceive the world at time scales relevant for behavior. We find that mice can integrate visual stimuli very quickly (<100 ms) to reach plateau performance in an orientation discrimination task. To define features of cortical activity that underlie performance at these time scales, we measured single-unit responses in the mouse visual cortex at time scales relevant to this task. In contrast to high-contrast stimuli of longer duration, which elicit reliable activity in individual neurons, stimuli at the threshold of perception elicit extremely sparse and unreliable responses in the primary visual cortex such that the activity of individual neurons does not reliably report orientation. Integrating information across neurons, however, quickly improves performance. Using a linear decoding model, we estimate that integrating information over 50–100 neurons is sufficient to account for behavioral performance. Thus, at the limits of visual perception, the visual system integrates information encoded in the probabilistic firing of unreliable single units to generate reliable behavior.

[1]  J. Simon Wiegert,et al.  Multiple dynamic representations in the motor cortex during sensorimotor learning , 2012, Nature.

[2]  Maik C. Stüttgen,et al.  Psychophysical and neurometric detection performance under stimulus uncertainty , 2008, Nature Neuroscience.

[3]  G. DeAngelis,et al.  Parallel Input Channels to Mouse Primary Visual Cortex , 2010, The Journal of Neuroscience.

[4]  Pamela Reinagel,et al.  Evidence That Primary Visual Cortex Is Required for Image, Orientation, and Motion Discrimination by Rats , 2013, PloS one.

[5]  William B. Levy,et al.  Energy Efficient Neural Codes , 1996, Neural Computation.

[6]  M. Carandini,et al.  Inhibition dominates sensory responses in awake cortex , 2012, Nature.

[7]  Denis Fize,et al.  Speed of processing in the human visual system , 1996, Nature.

[8]  Terrence J. Sejnowski,et al.  The “independent components” of natural scenes are edge filters , 1997, Vision Research.

[9]  Alan Agresti,et al.  Simple and Effective Confidence Intervals for Proportions and Differences of Proportions Result from Adding Two Successes and Two Failures , 2000 .

[10]  Haishan Yao,et al.  Contrast-dependent orientation discrimination in the mouse , 2015, Scientific Reports.

[11]  A. Pouget,et al.  The Cost of Accumulating Evidence in Perceptual Decision Making , 2012, The Journal of Neuroscience.

[12]  Lindsey L. Glickfeld,et al.  Mouse Primary Visual Cortex Is Used to Detect Both Orientation and Contrast Changes , 2013, The Journal of Neuroscience.

[13]  A. Marcel Conscious and unconscious perception: An approach to the relations between phenomenal experience and perceptual processes , 1983, Cognitive Psychology.

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

[15]  Robert A. Marino,et al.  Free viewing of dynamic stimuli by humans and monkeys. , 2009, Journal of vision.

[16]  Kenneth D Harris,et al.  Spike sorting for large, dense electrode arrays , 2015, Nature Neuroscience.

[17]  Ingo Fründ,et al.  Inference for psychometric functions in the presence of nonstationary behavior. , 2011, Journal of vision.

[18]  K. H. Britten,et al.  Neuronal correlates of a perceptual decision , 1989, Nature.

[19]  M. Scanziani,et al.  Distinct recurrent versus afferent dynamics in cortical visual processing , 2015, Nature Neuroscience.

[20]  R. Douglas,et al.  Characterization of mouse cortical spatial vision , 2004, Vision Research.

[21]  David J. Field,et al.  What Is the Goal of Sensory Coding? , 1994, Neural Computation.

[22]  Philip Meier,et al.  Collinear features impair visual detection by rats. , 2011, Journal of vision.

[23]  G. Orban,et al.  How well do response changes of striate neurons signal differences in orientation: a study in the discriminating monkey , 1990, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[24]  Kevin M. Cury,et al.  DeepLabCut: markerless pose estimation of user-defined body parts with deep learning , 2018, Nature Neuroscience.

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

[26]  Bingni W. Brunton,et al.  Rats and Humans Can Optimally Accumulate Evidence for Decision-Making , 2013, Science.

[27]  Lindsey L. Glickfeld,et al.  Cortico-cortical projections in mouse visual cortex are functionally target specific , 2013, Nature Neuroscience.

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

[29]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.

[30]  R. Reid,et al.  Frontiers in Cellular Neuroscience Cellular Neuroscience Methods Article , 2022 .

[31]  Georg B. Keller,et al.  Learning Enhances Sensory and Multiple Non-sensory Representations in Primary Visual Cortex , 2015, Neuron.

[32]  A. Grinvald,et al.  Relationship between intrinsic connections and functional architecture revealed by optical imaging and in vivo targeted biocytin injections in primate striate cortex. , 1993, Proceedings of the National Academy of Sciences of the United States of America.

[33]  M. Stryker,et al.  Modulation of Visual Responses by Behavioral State in Mouse Visual Cortex , 2010, Neuron.

[34]  T. Wiesel,et al.  Columnar specificity of intrinsic horizontal and corticocortical connections in cat visual cortex , 1989, The Journal of neuroscience : the official journal of the Society for Neuroscience.

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

[36]  M. Meister,et al.  Rapid Innate Defensive Responses of Mice to Looming Visual Stimuli , 2013, Current Biology.

[37]  Alexander S. Ecker,et al.  Population code in mouse V1 facilitates read-out of natural scenes through increased sparseness , 2014, Nature Neuroscience.

[38]  M. Scanziani,et al.  Tuned Thalamic Excitation is Amplified by Visual Cortical Circuits , 2013, Nature Neuroscience.

[39]  M. Bradley,et al.  The pupil as a measure of emotional arousal and autonomic activation. , 2008, Psychophysiology.

[40]  M. Young,et al.  Longer fixation duration while viewing face images , 2006, Experimental Brain Research.

[41]  Jakob Voigts,et al.  Neural ensemble communities: open-source approaches to hardware for large-scale electrophysiology , 2015, Current Opinion in Neurobiology.

[42]  Rob R. de Ruyter van Steveninck,et al.  The metabolic cost of neural information , 1998, Nature Neuroscience.

[43]  Geraint Rees,et al.  Neural correlates of consciousness in humans , 2002, Nature Reviews Neuroscience.

[44]  Alexandre Pouget,et al.  Insights from a Simple Expression for Linear Fisher Information in a Recurrently Connected Population of Spiking Neurons , 2011, Neural Computation.

[45]  R. Naik Ramesh,et al.  Intermingled Ensembles in Visual Association Cortex Encode Stimulus Identity or Predicted Outcome , 2018, Neuron.

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

[47]  Mark Mazurek,et al.  Robust quantification of orientation selectivity and direction selectivity , 2014, Front. Neural Circuits.

[48]  R. L. de Valois,et al.  Psychophysical studies of monkey vision. 3. Spatial luminance contrast sensitivity tests of macaque and human observers. , 1974, Vision research.

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

[50]  Karl Deisseroth,et al.  Activation of Specific Interneurons Improves V1 Feature Selectivity and Visual Perception , 2012, Nature.

[51]  Timothy D. Hanks,et al.  Elapsed Decision Time Affects the Weighting of Prior Probability in a Perceptual Decision Task , 2011, The Journal of Neuroscience.

[52]  J. Movshon,et al.  The analysis of visual motion: a comparison of neuronal and psychophysical performance , 1992, The Journal of neuroscience : the official journal of the Society for Neuroscience.

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

[54]  Ad Aertsen,et al.  Push-Pull Receptive Field Organization and Synaptic Depression: Mechanisms for Reliably Encoding Naturalistic Stimuli in V1 , 2016, Front. Neural Circuits.

[55]  A. Cowey,et al.  Blindsight in monkeys , 1995, Nature.

[56]  David A Leopold,et al.  Primary visual cortex: awareness and blindsight. , 2012, Annual review of neuroscience.

[57]  G. Holmes DISTURBANCES OF VISION BY CEREBRAL LESIONS , 1918, The British journal of ophthalmology.

[58]  C. Niell,et al.  Vision Drives Accurate Approach Behavior during Prey Capture in Laboratory Mice , 2016, Current Biology.

[59]  Hans Strasburger,et al.  Assessing spatial vision — automated measurement of the contrast-sensitivity function in the hooded rat , 2000, Journal of Neuroscience Methods.

[60]  K. Svoboda,et al.  Sparse optical microstimulation in barrel cortex drives learned behaviour in freely moving mice , 2008, Nature.

[61]  Daniel N Hill,et al.  Quality Metrics to Accompany Spike Sorting of Extracellular Signals , 2011, The Journal of Neuroscience.

[62]  G. Leuba,et al.  Comparison of neuronal and glial numerical density in primary and secondary visual cortex of man , 2004, Experimental Brain Research.

[63]  D. Tank,et al.  Imaging Large-Scale Neural Activity with Cellular Resolution in Awake, Mobile Mice , 2007, Neuron.

[64]  Pamela Reinagel,et al.  Temporal and spatial tuning of dorsal lateral geniculate nucleus neurons in unanesthetized rats. , 2016, Journal of neurophysiology.

[65]  J L Gallant,et al.  Sparse coding and decorrelation in primary visual cortex during natural vision. , 2000, Science.

[66]  Pamela Reinagel Speed and accuracy of visual image discrimination by rats , 2013, Front. Neural Circuits.

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

[68]  Wei Ji Ma,et al.  A Fast and Simple Population Code for Orientation in Primate V1 , 2012, The Journal of Neuroscience.

[69]  Shawn R. Olsen,et al.  First spikes in visual cortex enable perceptual discrimination , 2018, bioRxiv.

[70]  D. Hubel,et al.  Receptive fields of single neurones in the cat's striate cortex , 1959, The Journal of physiology.

[71]  E. S. Pearson,et al.  THE USE OF CONFIDENCE OR FIDUCIAL LIMITS ILLUSTRATED IN THE CASE OF THE BINOMIAL , 1934 .

[72]  W. M. Keck,et al.  Highly Selective Receptive Fields in Mouse Visual Cortex , 2008, The Journal of Neuroscience.

[73]  G. Palm,et al.  Density of neurons and synapses in the cerebral cortex of the mouse , 1989, The Journal of comparative neurology.

[74]  D. B. Leitch,et al.  Neuron densities vary across and within cortical areas in primates , 2010, Proceedings of the National Academy of Sciences.

[75]  P. Fldik,et al.  The Speed of Sight , 2001, Journal of Cognitive Neuroscience.