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Link to original content: https://doi.org/10.1038/nn.3431
Temporal whitening by power-law adaptation in neocortical neurons | Nature Neuroscience
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Temporal whitening by power-law adaptation in neocortical neurons

Nature Neuroscience volume 16, pages 942–948 (2013)Cite this article

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Abstract

Spike-frequency adaptation (SFA) is widespread in the CNS, but its function remains unclear. In neocortical pyramidal neurons, adaptation manifests itself by an increase in the firing threshold and by adaptation currents triggered after each spike. Combining electrophysiological recordings in mice with modeling, we found that these adaptation processes lasted for more than 20 s and decayed over multiple timescales according to a power law. The power-law decay associated with adaptation mirrored and canceled the temporal correlations of input current received in vivo at the somata of layer 2/3 somatosensory pyramidal neurons. These findings suggest that, in the cortex, SFA causes temporal decorrelation of output spikes (temporal whitening), an energy-efficient coding procedure that, at high signal-to-noise ratio, improves the information transfer.

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Figure 1: Spiking neuron model GLIF-ξ and experimental protocol.
Figure 2: Power-law adaptation filters extracted from in vitro recordings.
Figure 3: The GLIF-ξL model predicts the occurrence of single spikes with millisecond precision.
Figure 4: The GLIF-ξL model accurately predicts the firing rate response on multiple timescales.
Figure 5: Power-law adaptation is near-optimally tuned to perform temporal whitening.

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References

  1. Attwell, D. & Laughlin, S.B. An energy budget for signaling in the grey matter of the brain. J. Cereb. Blood Flow Metab. 21, 1133–1145 (2001).

    Article  CAS  PubMed  Google Scholar 

  2. Laughlin, S.B. Energy as a constraint on the coding and processing of sensory information. Curr. Opin. Neurobiol. 11, 475–480 (2001).

    Article  CAS  PubMed  Google Scholar 

  3. Barlow, H. Possible principles underlying the transformation of sensory messages. in Sensory Communication (ed. Rosenblith, W.A.) 217–234 (MIT Press, Cambridge, Massachusetts, 1961).

  4. Srinivasan, M.V., Laughlin, S.B. & Dubs, A. Predictive coding: a fresh view of inhibition in the retina. Proc. R. Soc. Lond. B Biol. Sci. 216, 427–459 (1982).

    Article  CAS  PubMed  Google Scholar 

  5. Dong, D. & Atick, J. Temporal decorrelation: a theory of lagged and nonlagged responses in the lateral geniculate nucleus. Network 6, 159–178 (1995).

    Article  Google Scholar 

  6. Dan, Y., Atick, J. & Reid, R.C. Efficient coding of natural scenes in the lateral geniculate nucleus: experimental test of a computational theory. J. Neurosci. 16, 3351–3362 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Pitkow, X. & Meister, M. Decorrelation and efficient coding by retinal ganglion cells. Nat. Neurosci. 15, 628–635 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Wark, B., Lundstrom, B.N. & Fairhall, A.L. Sensory adaptation. Curr. Opin. Neurobiol. 17, 423–429 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Wainwright, M.J. Visual adaptation as optimal information transmission. Vision Res. 39, 3960–3974 (1999).

    Article  CAS  PubMed  Google Scholar 

  10. Brenner, N., Bialek, W. & de Ruyter van Steveninck, R. Adaptive rescaling maximizes information transmission. Neuron 26, 695–702 (2000).

    Article  CAS  PubMed  Google Scholar 

  11. Fairhall, A.L., Lewen, G.D., Bialek, W. & de Ruyter Van Steveninck, R.R. Efficiency and ambiguity in an adaptive neural code. Nature 412, 787–792 (2001).

    Article  CAS  PubMed  Google Scholar 

  12. Maravall, M., Petersen, R.S., Fairhall, A.L., Arabzadeh, E. & Diamond, M.E. Shifts in coding properties and maintenance of information transmission during adaptation in barrel cortex. PLoS Biol. 5, e19 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Baccus, S.A. & Meister, M. Fast and slow contrast adaptation in retinal circuitry. Neuron 36, 909–919 (2002).

    Article  CAS  PubMed  Google Scholar 

  14. Ulanovsky, N., Las, L., Farkas, D. & Nelken, I. Multiple time scales of adaptation in auditory cortex neurons. J. Neurosci. 24, 10440–10453 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Simoncelli, E.P. & Olshausen, B. Natural image statistics and neural representation. Annu. Rev. Neurosci. 24, 1193–1216 (2001).

    Article  CAS  PubMed  Google Scholar 

  16. Izhikevich, E.M. Simple model of spiking neurons. IEEE Trans. Neural Netw. 14, 1569–1572 (2003).

    Article  CAS  PubMed  Google Scholar 

  17. Brette, R. & Gerstner, W. Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. J. Neurophysiol. 94, 3637–3642 (2005).

    Article  PubMed  Google Scholar 

  18. Spain, W.J. & Schwindt, P. Two transient potassium currents in layer V pyramidal neurones from cat sensorimotor cortex. J. Physiol. (Lond.) 434, 591–607 (1991).

    Article  CAS  Google Scholar 

  19. Gilboa, G., Chen, R. & Brenner, N. History-dependent multiple-timescale dynamics in a single-neuron model. J. Neurosci. 25, 6479–6489 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. La Camera, G. et al. Multiple time scales of temporal response in pyramidal and fast spiking cortical neurons. J. Neurophysiol. 96, 3448–3464 (2006).

    Article  PubMed  Google Scholar 

  21. Lundstrom, B.N., Higgs, M.H., Spain, W.J. & Fairhall, A.L. Fractional differentiation by neocortical pyramidal neurons. Nat. Neurosci. 11, 1335–1342 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Drew, P.J. & Abbott, L.F. Models and properties of power-law adaptation in neural systems. J. Neurophysiol. 96, 826–833 (2006).

    Article  PubMed  Google Scholar 

  23. Fleidervish, I.A., Friedman, A. & Gutnick, M.J. Slow inactivation of Na+ current and slow cumulative spike adaptation in mouse and guinea-pig neocortical neurones in slices. J. Physiol. (Lond.) 493, 83–97 (1996).

    Article  CAS  Google Scholar 

  24. Mickus, T., Jung, H.y. & Spruston, N. Properties of slow, cumulative sodium channel inactivation in rat hippocampal CA1 pyramidal neurons. Biophys. J. 76, 846–860 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Melnick, I.V., Santos, S.F.A. & Safronov, B.V. Mechanism of spike frequency adaptation in substantia gelatinosa neurones of rat. J. Physiol. (Lond.) 559, 383–395 (2004).

    Article  CAS  Google Scholar 

  26. Madison, D.V. & Nicoll, R.A. Control of the repetitive discharge of rat CA 1 pyramidal neurones in vitro. J. Physiol. (Lond.) 354, 319–331 (1984).

    Article  CAS  Google Scholar 

  27. Schwindt, P.C., Spain, W.J. & Crill, W.E. Long-lasting reduction of excitability by a sodium-dependent potassium current in cat neocortical neurons. J. Neurophysiol. 61, 233–244 (1989).

    Article  CAS  PubMed  Google Scholar 

  28. Sanchez-Vives, M.V., Nowak, L.G. & McCormick, D.A. Cellular mechanisms of long-lasting adaptation in visual cortical neurons in vitro. J. Neurosci. 20, 4286–4299 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Wang, X.J., Liu, Y., Sanchez-Vives, M.V. & McCormick, D.A. Adaptation and temporal decorrelation by single neurons in the primary visual cortex. J. Neurophysiol. 89, 3279–3293 (2003).

    Article  PubMed  Google Scholar 

  30. Rieke, F., Warland, D., de Ruyter van Steveninck, R. & Bialek, W. Spikes: Exploring the neural code (MIT Press, Cambridge, Massachusetts, 1999).

  31. Crochet, S., Poulet, J.F.A., Kremer, Y. & Petersen, C.C.H. Synaptic mechanisms underlying sparse coding of active touch. Neuron 69, 1160–1175 (2011).

    Article  CAS  PubMed  Google Scholar 

  32. Gerstner, W. & Kistler, W. Spiking Neuron Models: Single Neurons, Populations, Plasticity (Cambridge University Press, New York, 2002).

  33. Mensi, S. et al. Parameter extraction and classification of three cortical neuron types reveals two distinct adaptation mechanisms. J. Neurophysiol. 107, 1756–1775 (2012).

    Article  PubMed  Google Scholar 

  34. Jolivet, R. et al. The quantitative single-neuron modeling competition. Biol. Cybern. 99, 417–426 (2008).

    Article  PubMed  Google Scholar 

  35. Naud, R., Gerhard, F., Mensi, S. & Gerstner, W. Improved similarity measures for small sets of spike trains. Neural Comput. 23, 3016–3069 (2011).

    Article  PubMed  Google Scholar 

  36. Atick, J. Could information theory provide an ecological theory of sensory processing? Network 22, 4–44 (2011).

    Article  PubMed  Google Scholar 

  37. Benda, J. & Herz, A.V.M. A universal model for spike-frequency adaptation. Neural Comput. 15, 2523–2564 (2003).

    Article  PubMed  Google Scholar 

  38. Köndgen, H. et al. The dynamical response of neocortical neurons to temporally modulated noisy inputs in vitro. Cereb. Cortex 18, 2086–2097 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Toib, A., Lyakhov, V. & Marom, S. Interaction between duration of activity and time course of recovery from slow inactivation in mammalian brain Na+ channels. J. Neurosci. 18, 1893–1903 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Lowen, S.B., Liebovitch, L.S. & White, J.A. Fractal ion-channel behavior generates fractal firing patterns in neuronal models. Phys. Rev. E Stat. Phys. Plasmas Fluids Relat. Interdiscip. Topics 59, 5970–5980 (1999).

    CAS  PubMed  Google Scholar 

  41. Bohte, S.M. & Rombouts, J.O. Fractionally predictive spiking neurons. Neural Inf. Process. Syst. 23, 253–261 (2010).

    Google Scholar 

  42. Mar, D.J., Chow, C.C., Gerstner, W., Adams, R.W. & Collins, J.J. Noise shaping in populations of coupled model neurons. Proc. Natl. Acad. Sci. USA 96, 10450–10455 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Shin, J. Adaptation in spiking neurons based on the noise shaping neural coding hypothesis. Neural Netw. 14, 907–919 (2001).

    Article  CAS  PubMed  Google Scholar 

  44. Chacron, M.J., Lindner, B. & Longtin, A. Noise shaping by interval correlations increases information transfer. Phys. Rev. Lett. 92, 080601 (2004).

    Article  CAS  PubMed  Google Scholar 

  45. Avila-Akerberg, O. & Chacron, M.J. Nonrenewal spike train statistics: causes and functional consequences on neural coding. Exp. Brain Res. 210, 353–371 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Lefort, S., Tomm, C., Sarria, J.C.F. & Petersen, C.C.H. The excitatory neuronal network of the C2 barrel column in mouse primary somatosensory cortex. Neuron 61, 301–316 (2009).

    Article  CAS  PubMed  Google Scholar 

  47. Avermann, M., Tomm, C., Mateo, C., Gerstner, W. & Petersen, C.C.H. Microcircuits of excitatory and inhibitory neurons in layer 2/3 of mouse barrel cortex. J. Neurophysiol. 107, 3116–3134 (2012).

    Article  CAS  PubMed  Google Scholar 

  48. Jolivet, R., Rauch, A., Lüscher, H. & Gerstner, W. Predicting spike timing of neocortical pyramidal neurons by simple threshold models. J. Comput. Neurosci. 21, 35–49 (2006).

    Article  PubMed  Google Scholar 

  49. Truccolo, W., Eden, U.T., Fellows, M.R., Donoghue, J.P. & Brown, E.N. A point process framework for relating neural spiking activity to spiking history, neural ensemble and extrinsic covariate effects. J. Neurophysiol. 93, 1074–1089 (2005).

    Article  PubMed  Google Scholar 

  50. Pillow, J.W. et al. Spatio-temporal correlations and visual signaling in a complete neuronal population. Nature 454, 995–999 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Brette, R. et al. High-resolution intracellular recordings using a real-time computational model of the electrode. Neuron 59, 379–391 (2008).

    Article  CAS  PubMed  Google Scholar 

  52. Badel, L. et al. Dynamic I-V curves are reliable predictors of naturalistic pyramidal-neuron voltage traces. J. Neurophysiol. 99, 656–666 (2008).

    Article  PubMed  Google Scholar 

  53. Paninski, L., Pillow, J. & Simoncelli, E. Comparing integrate-and-fire models estimated using intracellular and extracellular data. Neurocomputing 65, 379–385 (2005).

    Article  Google Scholar 

  54. Brillinger, D.R. Maximum likelihood analysis of spike trains of interacting nerve cells. Biol. Cybern. 59, 189–200 (1988).

    Article  CAS  PubMed  Google Scholar 

  55. Paninski, L. Maximum likelihood estimation of cascade point-process neural encoding models. Network 15, 243–262 (2004).

    Article  PubMed  Google Scholar 

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Acknowledgements

We thank C.C.H. Petersen, B.N. Lundstrom, G. Hennequin and A. Seeholzer for helpful discussions. We are also grateful to S. Crochet for sharing in vivo recordings and to B.N. Lundstrom for sharing the data that inspired this work. Finally, we thank S. Naskar for his help with the in vitro recordings. This project was funded by the Swiss National Science Foundation (grant no. 200020 132871/1; C.P. and S.M.) and by the European Community's Seventh Framework Program (BrainScaleS, grant no. 269921; S.M. and R.N.).

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  1. School of Computer and Communication Sciences and School of Life Science, Brain Mind Institute, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Christian Pozzorini, Richard Naud, Skander Mensi & Wulfram Gerstner

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  1. Christian Pozzorini
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Contributions

C.P. and R.N. conceived the study. C.P. designed the experiments, analyzed the data, performed the modeling and wrote the initial draft of the manuscript. S.M. contributed to data analysis and modeling. W.G. supervised the project. All of the authors worked on the manuscript.

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Correspondence to Christian Pozzorini.

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Supplementary Figures 1–8, Supplementary Table 1, Supplementary Data Preprocessing and Supplementary Modeling (PDF 1911 kb)

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Pozzorini, C., Naud, R., Mensi, S. et al. Temporal whitening by power-law adaptation in neocortical neurons. Nat Neurosci 16, 942–948 (2013). https://doi.org/10.1038/nn.3431

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