Encyclopedia of Computational Neuroscience

2015 Edition
| Editors: Dieter Jaeger, Ranu Jung

Spike-Frequency Adaptation

  • Jan BendaEmail author
  • Joel Tabak
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-6675-8_339

Definition

When stimulated with a constant stimulus, many neurons initially respond with a high spike frequency that then decays down to a lower steady-state frequency (Fig. 1a). This dynamics of the spike-frequency response is referred to as “spike-frequency adaptation”. Spike-frequency adaptation is a process that is slower than the dynamics of action-potential generation. Spike-frequency adaptation by this definition is an aspect of the neuron’s super-threshold firing regime, although the mechanisms causing spike-frequency adaptation could also be at work in the neuron’s subthreshold regime.
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References

  1. Augustin M, Ladenbauer J, Obermayer K (2013) How adaptation shapes spike rate oscillations in recurrent neuronal networks. Front Comput Neurosci 7:9PubMedCentralPubMedGoogle Scholar
  2. Benda J, Hennig RM (2008) Dynamics of intensity invariance in a primary auditory interneuron. J Comput Neurosci 24:113–136PubMedGoogle Scholar
  3. Benda J, Herz AVM (2003) A universal model for spike-frequency adaptation. Neural Comput 15:2523–2564PubMedGoogle Scholar
  4. Benda J, Longtin A, Maler L (2005) Spike-frequency adaptation separates transient communication signals from background oscillations. J Neurosci 25:2312–2321PubMedGoogle Scholar
  5. Benda J, Maler L, Longtin A (2010) Linear versus nonlinear signal transmission in neuron models with adaptation-currents or dynamic thresholds. J Neurophysiol 104:2806–2820PubMedGoogle Scholar
  6. Brette R, Gerstner W (2005) Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. J Neurophysiol 94:3637–3642PubMedGoogle Scholar
  7. Brown DA, Adams PR (1980) Muscarinic suppression of a novel voltage-sensitive K+ current in a vertebrate neuron. Nature 183:673–676Google Scholar
  8. Butts D, Feller M, Shatz C, Rokhsar D (1999) Retinal waves are governed by collective network properties. J Neurosci 19:3580–3593PubMedGoogle Scholar
  9. Chacron MJ, Longtin A, Maler L (2001) Negative interspike interval correlations increase the neuronal capacity for encoding time-dependent stimuli. J Neurosci 21:5328–5343PubMedGoogle Scholar
  10. Chacron MJ, Longtin A, St-Hilaire M, Maler L (2000) Suprathreshold stochastic firing dynamics with memory in P-type electroreceptors. Phys Rev Lett 85:1576–1579PubMedGoogle Scholar
  11. Clarke SE, Naud R, Longtin A, Maler L (2013) Speed-invariant encoding of looming object distance requires power law spike rate adaptation. Proc Natl Acad Sci U S A 110:13624–13629PubMedCentralPubMedGoogle Scholar
  12. Compte A, Sanchez-Vives M, McCormick D, Wang XJ (2003) Cellular and network mechanisms of slow oscillatory activity (<1 Hz) and wave propagations in a cortical network model. J Neurophysiol 89:2707–2725PubMedGoogle Scholar
  13. Ermentrout B (1998) Linearization of f-I curves by adaptation. Neural Comput 10:1721–1729PubMedGoogle Scholar
  14. Ermentrout B, Pascal M, Gutkin B (2001) The effects of spike frequency adaptation and negative feedback on the synchronization of neural oscillators. Neural Comput 13:1285–1310PubMedGoogle Scholar
  15. Fisch K, Schwalger T, Lindner B, Herz AVM, Benda J (2012) Channel noise from both slow adaptation currents and fast currents is required to explain spike-response variability in a sensory neuron. J Neurosci 32:17332–17344PubMedGoogle Scholar
  16. Gigante G, Mattia M, Giudice PD (2007) Diverse population-bursting modes of adapting spiking neurons. Phys Rev Lett 98:148101PubMedGoogle Scholar
  17. Giugliano M, Darbon P, Arsiero M, Lüscher HR, Streit J (2004) Single-neuron discharge properties and network activity in dissociated cultures of neocortex. J Neurophysiol 92:977–996PubMedGoogle Scholar
  18. Gollisch T, Herz AVM (2004) Input-driven components of spike-frequency adaptation can be unmasked in vivo. J Neurosci 24:7435–7444PubMedGoogle Scholar
  19. Hildebrandt KJ, Benda J, Hennig RM (2009) The origin of adaptation in the auditory pathway of locusts is specific to cell type and function. J Neurosci 29:2626–2636PubMedGoogle Scholar
  20. Hildebrandt KJ, Benda J, Hennig RM (2011) Multiple arithmetic operations in a single neuron: the recruitment of adaptation processes in the cricket auditory pathway depends on sensory context. J Neurosci 31:14142–14150PubMedGoogle Scholar
  21. Izhikevich EM (2003) Simple model of spiking neurons. IEEE Trans Neural Netw 14:1569–1572PubMedGoogle Scholar
  22. Kosmidis E, Pierrefiche O, Vibert JF (2004) Respiratory-like rhythmic activity can be produced by an excitatory network of non-pacemaker neuron models. J Neurophysiol 92:686–699PubMedGoogle Scholar
  23. Liu YH, Wang XJ (2001) Spike-frequency adaptation of a generalized leaky integrate-and-fire model neuron. J Comput Neurosci 10:25–45PubMedGoogle Scholar
  24. Nesse W, Borisyuk A, Bressloff P (2008) Fluctuation-driven rhythmogenesis in an excitatory neuronal network with slow adaptation. J Comput Neurosci 25:317–333PubMedGoogle Scholar
  25. Peron S, Gabbiani F (2009) Spike frequency adaptation mediates looming stimulus selectivity in a collision-detecting neuron. Nat Neurosci 12:318–326PubMedCentralPubMedGoogle Scholar
  26. Prescott SA, Ratté S, Sejnowski TJ (2006) Nonlinear interaction between shunting and adaptation controls a switch between integration and coincidence detection in pyramidal neurons. J Neurosci 26:9084–9097PubMedCentralPubMedGoogle Scholar
  27. Sah P (1996) Ca2+-activated K+ currents in neurones: types, physiological roles and modulation. Trends Neurosci 19:150–154PubMedGoogle Scholar
  28. Schwalger T, Fisch K, Benda J, Lindner B (2010) How noisy adaptation of neurons shapes interspike interval histograms and correlations. PLoS Comput Biol 6:e1001026PubMedCentralPubMedGoogle Scholar
  29. Sobel EC, Tank DW (1994) In vivo Ca2+ dynamics in a cricket auditory neuron: an example of chemical computation. Science 263:823–826PubMedGoogle Scholar
  30. Sutherland C, Doiron B, Longtin A (2009) Feedback-induced gain control in stochastic spiking networks. Biol Cybern 100:475–489PubMedGoogle Scholar
  31. Tabak J, Mascagni M, Bertram R (2010) Mechanism for the universal pattern of activity in developing neuronal networks. J Neurophysiol 103:2208–2221PubMedCentralPubMedGoogle Scholar
  32. Tabak J, Senn W, O’Donovan M, Rinzel J (2000) Modeling of spontaneous activity in the developing spinal cord using activity-dependent depression in an excitatory network. J Neurosci 20:3041–3056PubMedGoogle Scholar
  33. Tsodyks M, Uziel A, Markram H (2000) Synchrony generation in recurrent networks with frequency-dependent synapses. J Neurosci 20:RC50PubMedGoogle Scholar
  34. van Vreeswijk C, Hansel D (2001) Patterns of synchrony in neural networks with spike adaptation. Neural Comput 13:959–992PubMedGoogle Scholar
  35. Wang XJ (1998) Calcium coding and adaptive temporal computation in cortical pyramidal neurons. J Neurophysiol 79:1549–1566PubMedGoogle Scholar
  36. Wiedman U, Luthi A (2003) Timing of network synchronization by refractory mechanisms. J Neurophysiol 90:3902–3911Google Scholar
  37. Wilson H, Cowan J (1972) Excitatory and inhibitory interactions in localized populations of model neurons. Biophys J 12:1–24PubMedCentralPubMedGoogle Scholar
  38. Xu Z, Payne JR, Nelson ME (1996) Logarithmic time course of sensory adaptation in electrosensory afferent nerve fibers in a weakly electric fish. J Neurophysiol 76:2020–2032PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Institute for NeurobiologyEberhard Karls UniversityTübingenGermany
  2. 2.Florida State UniversityTallahasseeUSA