Rank Order Coding: a Retinal Information Decoding Strategy Revealed by Large-Scale Multielectrode Array Retinal Recordings

Visual Abstract How a population of retinal ganglion cells (RGCs) encodes the visual scene remains an open question. Going beyond individual RGC coding strategies, results in salamander suggest that the relative latencies of a RGC pair encode spatial information. Thus, a population code based on this concerted spiking could be a powerful mechanism to transmit visual information rapidly and efficiently. How a population of retinal ganglion cells (RGCs) encodes the visual scene remains an open question. Going beyond individual RGC coding strategies, results in salamander suggest that the relative latencies of a RGC pair encode spatial information. Thus, a population code based on this concerted spiking could be a powerful mechanism to transmit visual information rapidly and efficiently. Here, we tested this hypothesis in mouse by recording simultaneous light-evoked responses from hundreds of RGCs, at pan-retinal level, using a new generation of large-scale, high-density multielectrode array consisting of 4096 electrodes. Interestingly, we did not find any RGCs exhibiting a clear latency tuning to the stimuli, suggesting that in mouse, individual RGC pairs may not provide sufficient information. We show that a significant amount of information is encoded synergistically in the concerted spiking of large RGC populations. Thus, the RGC population response described with relative activities, or ranks, provides more relevant information than classical independent spike count- or latency- based codes. In particular, we report for the first time that when considering the relative activities across the whole population, the wave of first stimulus-evoked spikes is an accurate indicator of stimulus content. We show that this coding strategy coexists with classical neural codes, and that it is more efficient and faster. Overall, these novel observations suggest that already at the level of the retina, concerted spiking provides a reliable and fast strategy to rapidly transmit new visual scenes.

[1]  Tim Gollisch,et al.  Rapid Neural Coding in the Retina with Relative Spike Latencies , 2008, Science.

[2]  R. Johansson,et al.  First spikes in ensembles of human tactile afferents code complex spatial fingertip events , 2004, Nature Neuroscience.

[3]  John A. Barnden,et al.  Temporal winner-take-all networks: a time-based mechanism for fast selection in neural networks , 1993, IEEE Trans. Neural Networks.

[4]  Matthew C Smear,et al.  Perception of sniff phase in mouse olfaction , 2011, Nature.

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

[6]  M. Greschner,et al.  Complex spike-event pattern of transient ON-OFF retinal ganglion cells. , 2006, Journal of neurophysiology.

[7]  R. Masland.  The Neuronal Organization of the Retina , 2012, Neuron.

[8]  Matthew C Smear,et al.  Precise olfactory responses tile the sniff cycle , 2011, Nature Neuroscience.

[9]  J. Sanes,et al.  The most numerous ganglion cell type of the mouse retina is a selective feature detector , 2012, Proceedings of the National Academy of Sciences.

[10]  S. Thorpe,et al.  Spike Timing Dependent Plasticity Finds the Start of Repeating Patterns in Continuous Spike Trains , 2008, PloS one.

[11]  P. Latham,et al.  Synergy, Redundancy, and Independence in Population Codes, Revisited , 2005, The Journal of Neuroscience.

[12]  E J Chichilnisky,et al.  A simple white noise analysis of neuronal light responses , 2001, Network.

[13]  Rufin van Rullen,et al.  Rate Coding Versus Temporal Order Coding: What the Retinal Ganglion Cells Tell the Visual Cortex , 2001, Neural Computation.

[14]  William Bialek,et al.  Spikes: Exploring the Neural Code , 1996 .

[15]  H. Sompolinsky,et al.  The tempotron: a neuron that learns spike timing–based decisions , 2006, Nature Neuroscience.

[16]  H. Sompolinsky,et al.  Computing Complex Visual Features with Retinal Spike Times , 2013, PloS one.

[17]  J Gautrais,et al.  Rate coding versus temporal order coding: a theoretical approach. , 1998, Bio Systems.

[18]  Michael J. Berry,et al.  Low error discrimination using a correlated population code. , 2012, Journal of neurophysiology.

[19]  Timothée Masquelier,et al.  Unsupervised Learning of Visual Features through Spike Timing Dependent Plasticity , 2007, PLoS Comput. Biol..

[20]  K. S. Jayaraman.  Gene-hunters home in on India , 1996, Nature.

[21]  W. R. Taylor,et al.  Local Edge Detectors: A Substrate for Fine Spatial Vision at Low Temporal Frequencies in Rabbit Retina , 2006, The Journal of Neuroscience.

[22]  Rufin van Rullen,et al.  Neurons Tune to the Earliest Spikes Through STDP , 2005, Neural Computation.

[23]  Sheila Nirenberg,et al.  Classification of retinal ganglion cells: a statistical approach. , 2003, Journal of neurophysiology.

[24]  Simon J. Thorpe,et al.  Ultra-rapid object detection with saccadic eye movements: Visual processing speed revisited , 2006, Vision Research.

[25]  Sébastien M. Crouzet,et al.  Fast saccades toward faces: face detection in just 100 ms. , 2010, Journal of vision.

[26]  Luca Berdondini,et al.  Spike Detection for Large Neural Populations Using High Density Multielectrode Arrays , 2015, Front. Neuroinform..

[27]  Luca Berdondini,et al.  Following the ontogeny of retinal waves: pan-retinal recordings of population dynamics in the neonatal mouse , 2013, The Journal of physiology.

[28]  R. Christopher deCharms,et al.  Primary cortical representation of sounds by the coordination of action-potential timing , 1996, Nature.

[29]  Denis G. Pelli,et al.  ECVP '07 Abstracts , 2007, Perception.

[30]  P. E. Hallett,et al.  A schematic eye for the mouse, and comparisons with the rat , 1985, Vision Research.

[31]  D G Pelli,et al.  The VideoToolbox software for visual psychophysics: transforming numbers into movies. , 1997, Spatial vision.

[32]  Tal Makovski,et al.  The visual attractor illusion. , 2010, Journal of vision.

[33]  Randall D. Beer,et al.  Nonnegative Decomposition of Multivariate Information , 2010, ArXiv.

[34]  Luca Berdondini,et al.  Active pixel sensor array for high spatio-temporal resolution electrophysiological recordings from single cell to large scale neuronal networks. , 2009, Lab on a chip.

[35]  Hilla Peretz,et al.  The , 1966 .

[36]  N. Brecha,et al.  The RNA binding protein RBPMS is a selective marker of ganglion cells in the mammalian retina , 2014, The Journal of comparative neurology.

[37]  Benjamin Flecker,et al.  Synergy, redundancy, and multivariate information measures: an experimentalist’s perspective , 2014, Journal of Computational Neuroscience.

[38]  Michael J. Berry,et al.  Synergy from Silence in a Combinatorial Neural Code , 2006, The Journal of Neuroscience.

[39]  P. Latham,et al.  Retinal ganglion cells act largely as independent encoders , 2001, Nature.

[40]  Arnaud Delorme,et al.  Spike-based strategies for rapid processing , 2001, Neural Networks.

[41]  N. Logothetis,et al.  Neurons with Stereotyped and Rapid Responses Provide a Reference Frame for Relative Temporal Coding in Primate Auditory Cortex , 2012, The Journal of Neuroscience.

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

[43]  Eric D Young,et al.  First-spike latency information in single neurons increases when referenced to population onset , 2007, Proceedings of the National Academy of Sciences.

[44]  P. Latham,et al.  Ruling out and ruling in neural codes , 2009, Proceedings of the National Academy of Sciences.

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

[46]  S. Thorpe,et al.  Spike times make sense , 2005, Trends in Neurosciences.