Amplification of Trial-to-Trial Response Variability by Neurons in Visual Cortex

The visual cortex responds to repeated presentations of the same stimulus with high variability. Because the firing mechanism is remarkably noiseless, the source of this variability is thought to lie in the membrane potential fluctuations that result from summated synaptic input. Here this hypothesis is tested through measurements of membrane potential during visual stimulation. Surprisingly, trial-to-trial variability of membrane potential is found to be low. The ratio of variance to mean is much lower for membrane potential than for firing rate. The high variability of firing rate is explained by the threshold present in the function that converts inputs into firing rates. Given an input with small, constant noise, this function produces a firing rate with a large variance that grows with the mean. This model is validated on responses recorded both intracellularly and extracellularly. In neurons of visual cortex, thus, a simple deterministic mechanism amplifies the low variability of summated synaptic inputs into the large variability of firing rate. The computational advantages provided by this amplification are not known.

[1]  S. Rice.  Mathematical analysis of random noise , 1944 .

[2]  D. Kernell,et al.  Quantitative aspects of repetitive firing of mammalian motoneurones, caused by injected currents , 1963, The Journal of physiology.

[3]  C. Stevens,et al.  Synaptic noise and other sources of randomness in motoneuron interspike intervals. , 1968, Journal of neurophysiology.

[4]  M. Abeles.  Role of the cortical neuron: integrator or coincidence detector? , 1982, Israel journal of medical sciences.

[5]  I. Ohzawa,et al.  Visual orientation and spatial frequency discrimination: a comparison of single neurons and behavior. , 1987, Journal of neurophysiology.

[6]  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.

[7]  Henry C. Tuckwell,et al.  Introduction to theoretical neurobiology , 1988 .

[8]  J. Molenaar,et al.  The spike generating mechanism of cat retinal ganglion cells , 1989, Vision Research.

[9]  Moshe Abeles,et al.  Corticonics: Neural Circuits of Cerebral Cortex , 1991 .

[10]  D. Heeger.  Half-squaring in responses of cat striate cells , 1992, Visual Neuroscience.

[11]  D. Ferster,et al.  Linearity of summation of synaptic potentials underlying direction selectivity in simple cells of the cat visual cortex. , 1993, Science.

[12]  William R. Softky,et al.  The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs , 1993, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[13]  T. Sejnowski,et al.  Reliability of spike timing in neocortical neurons. , 1995, Science.

[14]  J. Movshon,et al.  Spike train encoding by regular-spiking cells of the visual cortex. , 1996, Journal of neurophysiology.

[15]  Christof Koch,et al.  Temporal Precision of Spike Trains in Extrastriate Cortex of the Behaving Macaque Monkey , 1999, Neural Computation.

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

[17]  Kenneth D. Miller,et al.  Physiological Gain Leads to High ISI Variability in a Simple Model of a Cortical Regular Spiking Cell , 1997, Neural Computation.

[18]  B. Knight,et al.  Response variability and timing precision of neuronal spike trains in vivo. , 1997, Journal of neurophysiology.

[19]  D. Snodderly,et al.  Response Variability of Neurons in Primary Visual Cortex (V1) of Alert Monkeys , 1997, The Journal of Neuroscience.

[20]  D. G. Albrecht,et al.  Visual cortex neurons in monkeys and cats: Detection, discrimination, and identification , 1997, Visual Neuroscience.

[21]  G D Lewen,et al.  Reproducibility and Variability in Neural Spike Trains , 1997, Science.

[22]  X J Wang,et al.  Calcium coding and adaptive temporal computation in cortical pyramidal neurons. , 1998, Journal of neurophysiology.

[23]  C. Stevens,et al.  Input synchrony and the irregular firing of cortical neurons , 1998, Nature Neuroscience.

[24]  T. Albright,et al.  Efficient Discrimination of Temporal Patterns by Motion-Sensitive Neurons in Primate Visual Cortex , 1998, Neuron.

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

[26]  A. Grinvald,et al.  Linking spontaneous activity of single cortical neurons and the underlying functional architecture. , 1999, Science.

[27]  C. Gray,et al.  Cellular Mechanisms Contributing to Response Variability of Cortical Neurons In Vivo , 1999, The Journal of Neuroscience.

[28]  J Rinzel,et al.  Influence of temporal correlation of synaptic input on the rate and variability of firing in neurons. , 2000, Biophysical journal.

[29]  D. Ferster,et al.  The contribution of noise to contrast invariance of orientation tuning in cat visual cortex. , 2000, Science.

[30]  M. Carandini,et al.  Orientation tuning of input conductance, excitation, and inhibition in cat primary visual cortex. , 2000, Journal of neurophysiology.

[31]  R. Reid,et al.  Low Response Variability in Simultaneously Recorded Retinal, Thalamic, and Cortical Neurons , 2000, Neuron.

[32]  M. Carandini,et al.  Membrane Potential and Firing Rate in Cat Primary Visual Cortex , 2000, The Journal of Neuroscience.

[33]  M. Volgushev,et al.  Comparison of the selectivity of postsynaptic potentials and spike responses in cat visual cortex , 2000, The European journal of neuroscience.

[34]  Shin Ishii,et al.  Gaussian Process Approach to Spiking Neurons for Inhomogeneous Poisson Inputs , 2001, Neural Computation.

[35]  D. Hansel,et al.  How Noise Contributes to Contrast Invariance of Orientation Tuning in Cat Visual Cortex , 2002, The Journal of Neuroscience.

[36]  K. Miller,et al.  Neural noise can explain expansive, power-law nonlinearities in neural response functions. , 2002, Journal of neurophysiology.

[37]  M. Carandini,et al.  Suppression without Inhibition in Visual Cortex , 2002, Neuron.

[38]  M. Volgushev,et al.  A novel mechanism of response selectivity of neurons in cat visual cortex , 2002, The Journal of physiology.

[39]  D. Ringach,et al.  On the classification of simple and complex cells , 2002, Vision Research.

[40]  Matteo Carandini,et al.  Predicting signal and noise in LGN and V1 , 2003 .

[41]  Michael Rudolph,et al.  Note on “ Characterization of subthreshold voltage fluctuations in neuronal membranes ” , 2008 .

[42]  A. Grinvald,et al.  Spontaneously emerging cortical representations of visual attributes , 2003, Nature.

[43]  David J. Heeger,et al.  Neuronal correlates of perception in early visual cortex , 2003, Nature Neuroscience.

[44]  P. Heggelund,et al.  Response variability and orientation discrimination of single cells in striate cortex of cat , 1978, Experimental Brain Research.

[45]  J. A. Movshon,et al.  The dependence of response amplitude and variance of cat visual cortical neurones on stimulus contrast , 1981, Experimental Brain Research.

[46]  G. Orban,et al.  The response variability of striate cortical neurons in the behaving monkey , 2004, Experimental Brain Research.

[47]  Nicholas J. Priebe,et al.  The contribution of spike threshold to the dichotomy of cortical simple and complex cells , 2004, Nature Neuroscience.