Skip to main content

A generic model for selective adaptation in networks of heterogeneous populations

Adaptation is a biologically ubiquitous process whereby features of the system's responsiveness change as a result of persistent input. Most often, the kinetics of the change are monotonic and depend upon the input frequency. Adaptation in neural systems is inherently selective to the input characteristics; not only between sensory modalities, but even within a given modality, the system is capable of reducing its sensitivity to frequent input while preserving (or even enhancing) its sensitivity to the rare (e.g. [14]). In-vivo analyses suggest that a within-modality selective adaptation does not require concrete, precise point-to-point wiring (which would be a trivial yet non-physiological realization) [5]. Indeed, theoretical considerations indicate that, for the case of a single neuron, selective adaptation can be explained in terms of synaptic population dynamics (e.g. [6]). In-vitro analyses in networks of cortical neurons show that, beyond temporal dynamics, differences between topologies of excitatory and inhibitory sub-networks account for the full range of selective adaptation phenomena, including increased sensitivity to the rare [7]. Formal descriptions of selective adaptation are hindered by the problem of representing these different topologies in an analytically useful manner. In this study we offer a formalism that expresses topologies of connectivity in terms of temporal input gain modulation. Using this technique, we are able to formulate a generic analytic model for selective adaptation, which reconstructs all the major experimentally observed phenomena, offers predictions for further experimental analyses, and caters for a rigorous characterization of adaptation in general, and selective adaptation in particular.


  1. Tiitinen H, May P, Reinikainen K, Naatanen R: Attentive novelty detection in humans is governed by pre-attentive sensory memory. Nature. 1994, 372: 90-92. 10.1038/372090a0.

    Article  PubMed  CAS  Google Scholar 

  2. Dragoi V, Sharma J, Sur M: Adaptation-induced plasticity of orientation tuning in adult visual cortex. Neuron. 2000, 28: 287-298. 10.1016/S0896-6273(00)00103-3.

    Article  PubMed  CAS  Google Scholar 

  3. Naatanen R, Tervaniemi M, Sussman E, Paavilainen P, Winkler I: "Primitive intelligence" in the auditory cortex. Trends Neurosci. 2001, 24: 283-288. 10.1016/S0166-2236(00)01790-2.

    Article  PubMed  CAS  Google Scholar 

  4. Opitz B, Rinne T, Mecklinger A, von Cramon DY, Schroger E: Differential contribution of frontal and temporal cortices to auditory change detection: fMRI and ERP results. Neuroimage. 2002, 15: 167-174. 10.1006/nimg.2001.0970.

    Article  PubMed  Google Scholar 

  5. Ulanovsky N, Las L, Nelken I: Processing of low-probability sounds by cortical neurons. Nat Neurosci. 2003, 6: 391-398. 10.1038/nn1032.

    Article  PubMed  CAS  Google Scholar 

  6. Abbott LF, Varela JA, Sen K, Nelson SB: Synaptic depression and cortical gain control. Science. 1997, 275: 220-224. 10.1126/science.275.5297.221.

    Article  PubMed  CAS  Google Scholar 

  7. Eytan D, Brenner N, Marom S: Selective adaptation in networks of cortical neurons. J Neurosci. 2003, 23: 9349-9356.

    PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Ron Meir.

Rights and permissions

Open Access This article is published under license to BioMed Central Ltd. This is an Open Access article is distributed under the terms of the Creative Commons Attribution License ( ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and Permissions

About this article

Cite this article

Wallach, A., Eytan, D., Marom, S. et al. A generic model for selective adaptation in networks of heterogeneous populations. BMC Neurosci 8 (Suppl 2), P183 (2007).

Download citation

  • Published:

  • DOI:


  • Cortical Neuron
  • Sensory Modality
  • Neural System
  • Single Neuron
  • Formal Description