Skip to main content

Advertisement

Log in

Sensitivity of firing rate to input fluctuations depends on time scale separation between fast and slow variables in single neurons

  • Published:
Journal of Computational Neuroscience Aims and scope Submit manuscript

Abstract

Neuronal responses are often characterized by the firing rate as a function of the stimulus mean, or the fI curve. We introduce a novel classification of neurons into Types A, B−, and B+ according to how fI curves are modulated by input fluctuations. In Type A neurons, the fI curves display little sensitivity to input fluctuations when the mean current is large. In contrast, Type B neurons display sensitivity to fluctuations throughout the entire range of input means. Type B− neurons do not fire repetitively for any constant input, whereas Type B+ neurons do. We show that Type B+ behavior results from a separation of time scales between a slow and fast variable. A voltage-dependent time constant for the recovery variable can facilitate sensitivity to input fluctuations. Type B+ firing rates can be approximated using a simple “energy barrier” model.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Arfken, G. B., & Weber, H. -J. (1995). Mathematical methods for physicists (4th ed.). San Diego: Academic.

    Google Scholar 

  • Arsiero, M., Luscher, H. R., Lundstrom, B. N., & Giugliano, M. (2007). The impact of input fluctuations on the frequency-current relationships of layer 5 pyramidal neurons in the rat medial prefrontal cortex. The Journal of Neuroscience, 27, 3274–3284. doi:10.1523/JNEUROSCI.4937-06.2007.

    Article  PubMed  CAS  Google Scholar 

  • Benda, J., Longtin, A., & Maler, L. (2005). Spike-frequency adaptation separates transient communication signals from background oscillations. The Journal of Neuroscience, 25, 2312–2321. doi:10.1523/JNEUROSCI.4795-04.2005.

    Article  PubMed  CAS  Google Scholar 

  • Chance, F. S., Abbott, L. F., & Reyes, A. D. (2002). Gain modulation from background synaptic input. Neuron, 35, 773–782. doi:10.1016/S0896-6273(02)00820-6.

    Article  PubMed  CAS  Google Scholar 

  • Connor, J. A., & Stevens, C. F. (1971). Prediction of repetitive firing behaviour from voltage clamp data on an isolated neurone soma. The Journal of Physiology, 213, 31–53.

    PubMed  CAS  Google Scholar 

  • Dayan, P., & Abbott, L. F. (2001). Theoretical neuroscience : Computational and mathematical modeling of neural systems. Cambridge, MA: Massachusetts Institute of Technology Press.

    Google Scholar 

  • Destexhe, A., Rudolph, M., Fellous, J. M., & Sejnowski, T. J. (2001). Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience, 107, 13–24. doi:10.1016/S0306-4522(01)00344-X.

    Article  PubMed  CAS  Google Scholar 

  • Destexhe, A., Rudolph, M., & Pare, D. (2003). The high-conductance state of neocortical neurons in vivo. Nature Reviews. Neuroscience, 4, 739–751. doi:10.1038/nrn1198.

    Article  PubMed  CAS  Google Scholar 

  • DeVille, R. E., Vanden-Eijnden, E., & Muratov, C. B. (2005). Two distinct mechanisms of coherence in randomly perturbed dynamical systems. Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, 72, 031105. doi:10.1103/PhysRevE.72.031105.

    Google Scholar 

  • Ermentrout, B. (1998). Linearization of F–I curves by adaptation. Neural Computation, 10, 1721–1729. doi:10.1162/089976698300017106.

    Article  PubMed  CAS  Google Scholar 

  • Fairhall, A. L., Lewen, G. D., Bialek, W., & de Ruyter Van Steveninck, R. R. (2001). Efficiency and ambiguity in an adaptive neural code. Nature, 412, 787–792. doi:10.1038/35090500.

    Article  PubMed  CAS  Google Scholar 

  • Fellous, J. M., Rudolph, M., Destexhe, A., & Sejnowski, T. J. (2003). Synaptic background noise controls the input/output characteristics of single cells in an in vitro model of in vivo activity. Neuroscience, 122, 811–829. doi:10.1016/j.neuroscience.2003.08.027.

    Article  PubMed  CAS  Google Scholar 

  • Fleidervish, I. A., Friedman, A., & Gutnick, M. J. (1996). Slow inactivation of Na+ current and slow cumulative spike adaptation in mouse and guinea-pig neocortical neurones in slices. The Journal of Physiology, 493(Pt 1), 83–97.

    PubMed  CAS  Google Scholar 

  • Gerstner, W., & Kistler, W. M. (2002). Spiking neuron models : Single neurons, populations, plasticity. Cambridge, UK: Cambridge University Press.

    Google Scholar 

  • Gutkin, B. S., & Ermentrout, G. B. (1998). Dynamics of membrane excitability determine interspike interval variability: a link between spike generation mechanisms and cortical spike train statistics. Neural Computation, 10, 1047–1065. doi:10.1162/089976698300017331.

    Article  PubMed  CAS  Google Scholar 

  • Higgs, M. H., Slee, S. J., & Spain, W. J. (2006). Diversity of gain modulation by noise in neocortical neurons: regulation by the slow afterhyperpolarization conductance. The Journal of Neuroscience, 26, 8787–8799. doi:10.1523/JNEUROSCI.1792-06.2006.

    Article  PubMed  CAS  Google Scholar 

  • Hodgkin, A. L. (1948). The local electric changes associated with repetitive action in a non-medullated axon. The Journal of Physiology, 107, 165–181.

    PubMed  CAS  Google Scholar 

  • Hodgkin, A. L., & Huxley, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of Physiology, 117, 500–544.

    PubMed  CAS  Google Scholar 

  • Hong, S., Aguera y Arcas, B., & Fairhall, A. L. (2007). Single neuron computation: from dynamical system to feature detector. Neural Computation, 19, 3133–3172. doi:10.1162/neco.2007.19.12.3133.

    Article  PubMed  Google Scholar 

  • Hong, S., Lundstrom, B. N., & Fairhall, A. L. (2008). Intrinsic gain modulation and adaptive neural coding. PLoS Computational Biology, 4, e1000119. doi:10.1371/journal.pcbi.1000119.

    Article  PubMed  Google Scholar 

  • Izhikevich, E. M. (2007). Dynamical systems in neuroscience : The geometry of excitability and bursting. Cambridge, MA: MIT Press.

    Google Scholar 

  • Koch, C. (1999). Biophysics of computation: information processing in single neurons. New York: Oxford University Press.

    Google Scholar 

  • Konig, P., Engel, A. K., & Singer, W. (1996). Integrator or coincidence detector? The role of the cortical neuron revisited. Trends in Neurosciences, 19, 130–137. doi:10.1016/S0166-2236(96)80019-1.

    Article  PubMed  CAS  Google Scholar 

  • Lundstrom, B. N., & Fairhall, A. L. (2006). Decoding stimulus variance from a distributional neural code of interspike intervals. The Journal of Neuroscience, 26, 9030–9037. doi:10.1523/JNEUROSCI.0225-06.2006.

    Article  PubMed  CAS  Google Scholar 

  • Lundstrom, B. N., Hong, S., Higgs, M. H., & Fairhall, A. L. (2008). Two computational regimes of a single-compartment neuron separated by a planar boundary in conductance space. Neural Comput, 20, 1239–1260.

    Article  PubMed  Google Scholar 

  • Moreno, R., de la Rocha, J., Renart, A., & Parga, N. (2002). Response of spiking neurons to correlated inputs. Physical Review Letters, 89, 288101. doi:10.1103/PhysRevLett.89.288101.

    Article  PubMed  Google Scholar 

  • Morris, C., & Lecar, H. (1981). Voltage oscillations in the barnacle giant muscle fiber. Biophysical Journal, 35, 193–213. doi:10.1016/S0006-3495(81)84782-0.

    Article  PubMed  CAS  Google Scholar 

  • Prescott, S. A., Ratte, S., De Koninck, Y., & Sejnowski, T. J. (2006). Nonlinear interaction between shunting and adaptation controls a switch between integration and coincidence detection in pyramidal neurons. The Journal of Neuroscience, 26, 9084–9097. doi:10.1523/JNEUROSCI.1388-06.2006.

    Article  PubMed  CAS  Google Scholar 

  • Rauch, A., La Camera, G., Luscher, H. R., Senn, W., & Fusi, S. (2003). Neocortical pyramidal cells respond as integrate-and-fire neurons to in vivo-like input currents. Journal of Neurophysiology, 90, 1598–1612. doi:10.1152/jn.00293.2003.

    Article  PubMed  Google Scholar 

  • Richardson, M. J. (2004). Effects of synaptic conductance on the voltage distribution and firing rate of spiking neurons. Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, 69, 051918. doi:10.1103/PhysRevE.69.051918.

    Google Scholar 

  • Rinzel, J., & Ermentrout, B. (1998). Analysis of neural excitability and oscillations. In C. Koch, I. Segev, & (Eds.), (pp. 251–291, 2nd ed.). Cambridge, Massachusetts: MIT Press.

    Google Scholar 

  • Robinson, H. P., & Harsch, A. (2002). Stages of spike time variability during neuronal responses to transient inputs. Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, 66, 061902. doi:10.1103/PhysRevE.66.061902.

    Google Scholar 

  • Rudolph, M., & Destexhe, A. (2005). An extended analytic expression for the membrane potential distribution of conductance-based synaptic noise. Neural Computation, 17, 2301–2315. doi:10.1162/0899766054796932.

    Article  PubMed  CAS  Google Scholar 

  • Rudolph, M., & Destexhe, A. (2006). On the use of analytical expressions for the voltage distribution to analyze intracellular recordings. Neural Computation, 18, 2917–2922. doi:10.1162/neco.2006.18.12.2917.

    Article  PubMed  Google Scholar 

  • Rush, M. E., & Rinzel, J. (1995). The potassium A-current, low firing rates and rebound excitation in Hodgkin–Huxley models. Bulletin of Mathematical Biology, 57, 899–929.

    PubMed  CAS  Google Scholar 

  • Shadlen, M. N., & Newsome, W. T. (1994). Noise, neural codes and cortical organization. Current Opinion in Neurobiology, 4, 569–579. doi:10.1016/0959-4388(94)90059-0.

    Article  PubMed  CAS  Google Scholar 

  • Slee, S. J., Higgs, M. H., Fairhall, A. L., & Spain, W. J. (2005). Two-dimensional time coding in the auditory brainstem. The Journal of Neuroscience, 25, 9978–9988. doi:10.1523/JNEUROSCI.2666-05.2005.

    Article  PubMed  CAS  Google Scholar 

  • Strogatz, S. H. (1994). Nonlinear dynamics and Chaos: with applications to physics, biology, chemistry, and engineering. Reading, Mass.: Addison-Wesley.

    Google Scholar 

  • Tateno, T., & Pakdaman, K. (2004). Random dynamics of the Morris–Lecar neural model. Chaos (Woodbury, N.Y.), 14, 511–530. doi:doi:10.1063/1.1756118.

    Article  Google Scholar 

  • VanRullen, R., Guyonneau, R., & Thorpe, S. J. (2005). Spike times make sense. Trends in Neurosciences, 28, 1–4. doi:10.1016/j.tins.2004.10.010.

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgments

We thank Bard Ermentrout for helpful conversations during initial stages of the project at the Marine Biological Laboratory’s Methods in Computational Neuroscience 2007 course, Matthew Higgs and Michele Giugliano for helpful discussions and providing data for a figure, and Randy Powers and Sungho Hong for comments on a draft of this manuscript.

Funding

This work was supported by a Burroughs-Wellcome Careers at the Scientific Interface grant and a McKnight Scholar Award; BNL was supported by grant number F30NS055650 from the National Institute of Neurological Disorders and Stroke, the Medical Scientist Training Program at UW supported by the National Institute of General Medical Sciences, and an ARCS fellowship; WJS was supported by a VA Merit Review.

Author contributions

Conceived of, designed, and performed the simulations: BL. Analyzed the data: BL MF LS AF. Wrote the paper: BL MF WS AF. Developed the conceptual framework: BL MF LS WS AF.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Brian Nils Lundstrom.

Additional information

Action Editor: Bard Ermentrout

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lundstrom, B.N., Famulare, M., Sorensen, L.B. et al. Sensitivity of firing rate to input fluctuations depends on time scale separation between fast and slow variables in single neurons. J Comput Neurosci 27, 277–290 (2009). https://doi.org/10.1007/s10827-009-0142-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10827-009-0142-x

Keywords

Navigation