Internal gain modulations, but not changes in stimulus contrast, preserve the neural code

Neurons in primary visual cortex (V1) are strongly modulated both by stimulus contrast and by fluctuations of internal inputs. An important question is whether population codes are preserved under these conditions. Changes in stimulus contrast are thought to leave population codes invariant, whereas the effect of internal gain modulations remains unknown. To address these questions we studied how the direction-of-motion of oriented gratings is encoded in layer 2/3 of mouse V1. Surprisingly, we found that, because contrast gain responses across cells are heterogeneous, a change in contrast alters the information distribution profile across cells leading to the failure of contrast invariance. Remarkably, internal input fluctuations that cause commensurate firing rate modulations at the single-cell level, do respect population code invariance. These observations have important implications for visual information encoding, and argue that the brain strives to maintain the stability of the neural code in the face of fluctuating internal inputs.

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

[2]  J. Allman,et al.  Stimulus specific responses from beyond the classical receptive field: neurophysiological mechanisms for local-global comparisons in visual neurons. , 1985, Annual review of neuroscience.

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

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

[5]  Manuel Guizar-Sicairos,et al.  Efficient subpixel image registration algorithms. , 2008, Optics letters.

[6]  Brenda C. Shields,et al.  Thy1-GCaMP6 Transgenic Mice for Neuronal Population Imaging In Vivo , 2014, PloS one.

[7]  D H Brainard,et al.  The Psychophysics Toolbox. , 1997, Spatial vision.

[8]  Jochen F. Meyer,et al.  Visually Driven Neuropil Activity and Information Encoding in Mouse Primary Visual Cortex , 2017, Front. Neural Circuits.

[9]  Jan Drugowitsch,et al.  Multiplicative and Additive Modulation of Neuronal Tuning with Population Activity Affects Encoded Information , 2016, Neuron.

[10]  I. Ohzawa,et al.  The effects of contrast on visual orientation and spatial frequency discrimination: a comparison of single cells and behavior. , 1987, Journal of neurophysiology.

[11]  Y. Dan,et al.  Neuromodulation of Brain States , 2012, Neuron.

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

[13]  R. Shapley,et al.  Contrast's effect on spatial summation by macaque V1 neurons , 1999, Nature Neuroscience.

[14]  R. Reid,et al.  Broadly Tuned Response Properties of Diverse Inhibitory Neuron Subtypes in Mouse Visual Cortex , 2010, Neuron.

[15]  D. G. Albrecht,et al.  Striate cortex of monkey and cat: contrast response function. , 1982, Journal of neurophysiology.

[16]  D. Hubel,et al.  Receptive fields and functional architecture of monkey striate cortex , 1968, The Journal of physiology.

[17]  Spencer L. Smith,et al.  Parallel processing of visual space by neighboring neurons in mouse visual cortex , 2010, Nature Neuroscience.

[18]  J. Peirce The potential importance of saturating and supersaturating contrast response functions in visual cortex. , 2007, Journal of vision.

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

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

[21]  Nicholas J. Priebe,et al.  The Emergence of Contrast-Invariant Orientation Tuning in Simple Cells of Cat Visual Cortex , 2007, Neuron.

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

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

[24]  David S. Greenberg,et al.  Imaging input and output of neocortical networks in vivo. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

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

[26]  M. Stryker,et al.  A Cortical Circuit for Gain Control by Behavioral State , 2014, Cell.

[27]  Alex R. Wade,et al.  Representation of Concurrent Stimuli by Population Activity in Visual Cortex , 2014, Neuron.

[28]  M. Morrone,et al.  Selective Tuning for Contrast in Macaque Area V4 , 2013, The Journal of Neuroscience.

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

[30]  D. McCormick,et al.  Waking State: Rapid Variations Modulate Neural and Behavioral Responses , 2015, Neuron.

[31]  Eero P. Simoncelli,et al.  Attention stabilizes the shared gain of V4 populations , 2015, eLife.

[32]  I. Ohzawa,et al.  Contrast gain control in the cat's visual system. , 1985, Journal of neurophysiology.

[33]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[34]  I. Ohzawa,et al.  Contrast gain control in the cat visual cortex , 1982, Nature.

[35]  R. Freeman,et al.  Orientation selectivity in the cat's striate cortex is invariant with stimulus contrast , 2004, Experimental Brain Research.

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

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

[38]  D. G. Albrecht,et al.  Cortical neurons: Isolation of contrast gain control , 1992, Vision Research.

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

[40]  C. Baker,et al.  The direction-selective contrast response of area 18 neurons is different for first- and second-order motion , 2005, Visual Neuroscience.

[41]  Stephen P. Boyd,et al.  An Interior-Point Method for Large-Scale $\ell_1$-Regularized Least Squares , 2007, IEEE Journal of Selected Topics in Signal Processing.

[42]  Stefan R. Pulver,et al.  Ultra-sensitive fluorescent proteins for imaging neuronal activity , 2013, Nature.

[43]  P. Golshani,et al.  Cellular mechanisms of brain-state-dependent gain modulation in visual cortex , 2013, Nature Neuroscience.

[44]  M. Weliky,et al.  Small modulation of ongoing cortical dynamics by sensory input during natural vision , 2004, Nature.

[45]  Li Zhaoping,et al.  Exploring the roles of saturating and supersaturating contrast-response functions in conjunction detection and contrast coding. , 2011, Journal of vision.

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

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

[48]  K. Svoboda,et al.  Long-term, high-resolution imaging in the mouse neocortex through a chronic cranial window , 2009, Nature Protocols.

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

[50]  Lawrence Carin,et al.  Sparse multinomial logistic regression: fast algorithms and generalization bounds , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[51]  Radford M. Neal Pattern Recognition and Machine Learning , 2007, Technometrics.

[52]  Martin Vinck,et al.  Arousal and Locomotion Make Distinct Contributions to Cortical Activity Patterns and Visual Encoding , 2014, Neuron.