Volume 8 Supplement 2

Sixteenth Annual Computational Neuroscience Meeting: CNS*2007

Open Access

A generic model for selective adaptation in networks of heterogeneous populations

BMC Neuroscience20078(Suppl 2):P183


Published: 6 July 2007

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.

Authors’ Affiliations

Department of Electrical Engineering
Faculty of Medicine


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


© Wallach et al; licensee BioMed Central Ltd. 2007

This article is published under license to BioMed Central Ltd.