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Sensitivity to perturbations in vivo implies high noise and suggests rate coding in cortex



It is well known that neural activity exhibits variability, in the sense that identical sensory stimuli produce different responses1,2,3, but it has been difficult to determine what this variability means. Is it noise, or does it carry important information—about, for example, the internal state of the organism? Here we address this issue from the bottom up, by asking whether small perturbations to activity in cortical networks are amplified. Based on in vivo whole-cell patch-clamp recordings in rat barrel cortex, we find that a perturbation consisting of a single extra spike in one neuron produces approximately 28 additional spikes in its postsynaptic targets. We also show, using simultaneous intra- and extracellular recordings, that a single spike in a neuron produces a detectable increase in firing rate in the local network. Theoretical analysis indicates that this amplification leads to intrinsic, stimulus-independent variations in membrane potential of the order of ±2.2–4.5 mV—variations that are pure noise, and so carry no information at all. Therefore, for the brain to perform reliable computations, it must either use a rate code, or generate very large, fast depolarizing events, such as those proposed by the theory of synfire chains4,5. However, in our in vivo recordings, we found that such events were very rare. Our findings are thus consistent with the idea that cortex is likely to use primarily a rate code.

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Figure 1: The effect of an extra spike on a neuronal network.
Figure 2: Small perturbations affect spiking probability in vivo.
Figure 3: Determining the sensitivity of neurons to small perturbations in vivo.
Figure 4: The effect of one extra spike on network activity in vivo.
Figure 5: Precisely timed events are rare.


  1. 1

    Richmond, B. J., Optican, L. M., Podell, M. & Spitzer, H. Temporal encoding of two-dimensional patterns by single units in primate inferior temporal cortex. I. Response characteristics. J. Neurophysiol. 57, 132–146 (1987)

    CAS  Article  Google Scholar 

  2. 2

    Victor, J. D. & Purpura, K. P. Nature and precision of temporal coding in visual cortex: a metric-space analysis. J. Neurophysiol. 76, 1310–1326 (1996)

    CAS  Article  Google Scholar 

  3. 3

    Tolhurst, D. J., Movshon, J. A. & Dean, A. F. The statistical reliability of signals in single neurons in cat and monkey visual cortex. Vision Res. 23, 775–785 (1983)

    CAS  Article  Google Scholar 

  4. 4

    Abeles, M. Corticonics: Neural Circuits of the Cerebral Cortex (Cambridge Univ. Press, 1991)

    Book  Google Scholar 

  5. 5

    Izhikevich, E. Polychronization: computation with spikes. Neural Comput. 18, 245–282 (2006)

    MathSciNet  Article  Google Scholar 

  6. 6

    Hecht, S., Shlaer, S. & Pirenne, M. Energy, quanta, and vision. J. Gen. Physiol. 25, 819–840 (1942)

    CAS  Article  Google Scholar 

  7. 7

    Binzegger, T., Douglas, R. J. & Martin, K. A. A quantitative map of the circuit of cat primary visual cortex. J. Neurosci. 24, 8441–8453 (2004)

    CAS  Article  Google Scholar 

  8. 8

    Braitenberg, V. & Schüz, A. Anatomy of the Cortex (Springer, 1991)

    Book  Google Scholar 

  9. 9

    Markram, H., Lübke, J., Frotscher, M., Roth, A. & Sakmann, B. Physiology and anatomy of synaptic connections between thick tufted pyramidal neurones in the developing rat neocortex. J. Physiol. (Lond.) 500, 409–440 (1997)

    CAS  Article  Google Scholar 

  10. 10

    Herrmann, A. & Gerstner, W. Noise and the PSTH response to current transients: I. General theory and application to the integrate-and-fire neuron. J. Comput. Neurosci. 11, 135–151 (2001)

    CAS  Article  Google Scholar 

  11. 11

    Richardson, M. Firing-rate response of linear and nonlinear integrate-and-fire neurons to modulated current-based and conductance-based synaptic drive. Phys. Rev. E 76, 021919 (2007)

    ADS  Article  Google Scholar 

  12. 12

    Song, S., Sjöström, P. J., Reigl, M., Nelson, S. & Chklovskii, D. B. Highly nonrandom features of synaptic connectivity in local cortical circuits. PLoS Biol. 3, e68 (2005)

    Article  Google Scholar 

  13. 13

    Barbour, B., Brunel, N., Hakim, V. & Nadal, J. P. What can we learn from synaptic weight distributions? Trends Neurosci. 30, 622–629 (2007)

    CAS  Article  Google Scholar 

  14. 14

    Thomson, A. M. & Lamy, C. Functional maps of neocortical local circuitry. Front. Neurosci 1, 19–42 (2007)

    CAS  Article  Google Scholar 

  15. 15

    Holmgren, C., Harkany, T., Svennenfors, B. & Zilberter, Y. Pyramidal cell communication within local networks in layer 2/3 of rat neocortex. J. Physiol. (Lond.) 551, 139–153 (2003)

    CAS  Article  Google Scholar 

  16. 16

    Crochet, S. & Petersen, C. C. Correlating whisker behavior with membrane potential in barrel cortex of awake mice. Nature Neurosci. 9, 608–610 (2006)

    CAS  Article  Google Scholar 

  17. 17

    Ferezou, I., Bolea, S. & Petersen, C. C. Visualizing the cortical representation of whisker touch: voltage-sensitive dye imaging in freely moving mice. Neuron 50, 617–629 (2006)

    CAS  Article  Google Scholar 

  18. 18

    Larkum, M. E., Zhu, J. J. & Sakmann, B. A new cellular mechanism for coupling inputs arriving at different cortical layers. Nature 398, 338–341 (1999)

    ADS  CAS  Article  Google Scholar 

  19. 19

    Ariav, G., Polsky, A. & Schiller, J. Submillisecond precision of the input-output transformation function mediated by fast sodium dendritic spikes in basal dendrites of CA1 pyramidal neurons. J. Neurosci. 23, 7750–7758 (2003)

    CAS  Article  Google Scholar 

  20. 20

    Häusser, M. & Mel, B. Dendrites: bug or feature? Curr. Opin. Neurobiol. 13, 372–383 (2003)

    Article  Google Scholar 

  21. 21

    London, M. & Häusser, M. Dendritic computation. Annu. Rev. Neurosci. 28, 503–532 (2005)

    CAS  Article  Google Scholar 

  22. 22

    Murayama, M. et al. Dendritic encoding of sensory stimuli controlled by deep cortical interneurons. Nature 457, 1137–1141 (2009)

    ADS  CAS  Article  Google Scholar 

  23. 23

    van Vreeswijk, C. & Sompolinsky, H. Chaos in neuronal networks with balanced excitatory and inhibitory activity. Science 274, 1724–1726 (1996)

    ADS  CAS  Article  Google Scholar 

  24. 24

    van Vreeswijk, C. & Sompolinsky, H. Chaotic balanced state in a model of cortical circuits. Neural Comput. 10, 1321–1371 (1998)

    CAS  Article  Google Scholar 

  25. 25

    Banerjee, A., Seriès, P. & Pouget, A. Dynamical constraints on using precise spike timing to compute in recurrent cortical networks. Neural Comput. 20, 974–993 (2008)

    Article  Google Scholar 

  26. 26

    Izhikevich, E. M. & Edelman, G. M. Large-scale model of mammalian thalamocortical systems. Proc. Natl Acad. Sci. USA 105, 3593–3598 (2008)

    ADS  CAS  Article  Google Scholar 

  27. 27

    Smale, S. Differentiable dynamical systems. Bull. Am. Math. Soc. 73, 747–817 (1967)

    MathSciNet  Article  Google Scholar 

  28. 28

    Arabzadeh, E., Panzeri, S. & Diamond, M. Deciphering the spike train of a sensory neuron: counts and temporal patterns in the rat whisker pathway. J. Neurosci. 26, 9216–9226 (2006)

    CAS  Article  Google Scholar 

  29. 29

    Bair, W. & Koch, C. Temporal precision of spike trains in extrastriate cortex of the behaving macaque monkey. Neural Comput. 8, 1185–1202 (1996)

    CAS  Article  Google Scholar 

  30. 30

    de Kock, C. P. & Sakmann, B. Spiking in primary somatosensory cortex during natural whisking in awake head-restrained rats is cell-type specific. Proc. Natl Acad. Sci. USA 106, 16446–16450 (2009)

    ADS  CAS  Article  Google Scholar 

  31. 31

    Margrie, T. W., Brecht, M. & Sakmann, B. In vivo, low-resistance, whole-cell recordings from neurons in the anaesthetized and awake mammalian brain. Pflügers Arch. 444, 491–498 (2002)

    CAS  Article  Google Scholar 

  32. 32

    Häusser, M. & Roth, A. Estimating the time course of the excitatory synaptic conductance in neocortical pyramidal cells using a novel voltage jump method. J. Neurosci. 17, 7606–7625 (1997)

    Article  Google Scholar 

  33. 33

    Mainen, Z. F., Joerges, J., Huguenard, J. R. & Sejnowski, T. J. A model of spike initiation in neocortical pyramidal neurons. Neuron 15, 1427–1439 (1995)

    CAS  Article  Google Scholar 

  34. 34

    Hines, M. L. & Carnevale, N. T. The NEURON simulation environment. Neural Comput. 9, 1179–1209 (1997)

    CAS  Article  Google Scholar 

  35. 35

    Latham, P. E., Richmond, B. J., Nelson, P. G. & Nirenberg, S. N. Intrinsic dynamics in neuronal networks: I. Theory. J. Neurophysiol. 83, 808–827 (2000)

    CAS  Article  Google Scholar 

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We thank P. Dayan for discussions, I. van Welie and P. Dayan for comments on the manuscript, and H. Cuntz for comments on the spike detection algorithm. P.E.L. was supported by the Gatsby Charitable Foundation and US National Institute of Mental Health grant R01 MH62447. M.L., A.R., L.B. and M.H. were supported by the Wellcome Trust, the Gatsby Charitable Foundation, the Engineering and Physical Sciences Research Council and the Medical Research Council.

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Correspondence to Peter E. Latham.

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London, M., Roth, A., Beeren, L. et al. Sensitivity to perturbations in vivo implies high noise and suggests rate coding in cortex. Nature 466, 123–127 (2010).

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