Temporal Coding Is Not Only About Cooperation—It Is Also About Competition

Part of the Springer Series in Cognitive and Neural Systems book series (SSCNS, volume 3)


Temporal coding is commonly understood as a cooperative mechanism, realizing a grouping of neural units into assemblies based on temporal correlation. However, restricting the view on temporal coding to this perspective may miss an essential aspect. For example, with only synchronizing couplings present, the well-known superposition catastrophe may arise through states of global synchrony. Therefore, also desynchronizing tendencies have to be included, preferably in a manner that introduces an appropriate form of competition. In the context of phase model oscillator networks with Hebbian couplings, already a study of a “second-simplest” choice of model reveals a surprising property: temporal coding introduces a “competition for coherence” among stored patterns without need to include inhibitory couplings. Here, we refer to the corresponding models as Patterned Coherence Models (PCMs). This denotation is chosen with regard to the tendency of such models to establish coherence of parts of the network that correspond to the stored patterns, a valuable property with respect to pattern recognition. We review and discuss recent progress in studying such models, concentrating on models that complement synchronization with so-called acceleration. The latter mechanism implies an increased phase velocity of neural units in case of stronger and/or more coherent input from the connected units. Assuming Hebbian storage of patterns, it is demonstrated that the inclusion of acceleration introduces a competition for coherence that has a profound and favorable effect on pattern recognition. Outlook is given on paths towards including inhibition, hierarchical processing, and zero-lag synchronization despite time delays. We also mention possible relations to recent neurophysiological experiments that study the recoding of excitatory drive into phase shifts.


Phase Dynamic Acceleration Mechanism Pattern Phase Gamma Oscillation Temporal Code 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The author is thankful to Christoph von der Malsburg for inspiring and helpful discussions on self-organization mechanisms in general and temporal coding in particular. Moreover, it is a pleasure to thank Danko Nikolić, Martha Havenith and Gordon Pipa from the Max-Plack-Institute for Brain Research, Frankfurt, for insightful discussions on temporal coding with respect to observed brain dynamics.


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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Frankfurt Institute for Advanced Studies (FIAS)Goethe-UniversitätFrankfurt am MainGermany

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