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Detecting Coherence in Neuronal Data

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Part of the Physics of Neural Networks book series (NEURAL NETWORKS)

Synopsis

Spatially and temporally coherent activities emerge in many neuronal systems. The analysis of such responses in most cases is based on simple correlation techniques which cannot detect nonlinear relationships. In this contribution I review new approaches for the nonlinear analysis of coherent activities in neuronal signals. On the one hand I present model free approaches which are based on the general notion of statistical dependency and which apply to the neurophysiological observables spike activities and local field potentials, respectively. These approaches quantify coherence in information theoretical terms and can help to characterize the underlying dynamics. I show that the contributions to statistical dependency can be analyzed in a time resolved way and present a new method which allows identification of coherent episodes in a background of stochastic activity. On the other hand I present a model-dependent approach which is particularly well suited for the analysis of neuronal assemblies exhibiting emergent burst activities. It assumes a simple form of the network dynamics for which the parameters can be directly determined from experimental spike trains. This Ansatz deals with the fact that observable spike trains only stochastically reflect an underlying network dynamics. Despite the mathematical simplicity of this approach it determines important characteristics like memory and switching behavior of the underlying network synchronization dynamics. The methods are illustrated by multiunit activity and local field potential data from the visual cortex of the cat. Both approaches independently reveal that these data reflect a network dynamics which switches between essentially two states, an oscillatory and a stochastic one. The episodes within which oscillations and synchronizations occur are identified with high time resolution by either method from local field potentials as well as from multiunit activities. It turns out that synchronization across a cortical distance is quite a rare event and occurs within temporally coherent episodes only.

Keywords

  • Mutual Information
  • Firing Rate
  • Network Dynamic
  • Spike Train
  • Local Field Potential

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.

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© 1994 Springer-Verlag New York, Inc.

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Pawelzik, K. (1994). Detecting Coherence in Neuronal Data. In: Domany, E., van Hemmen, J.L., Schulten, K. (eds) Models of Neural Networks. Physics of Neural Networks. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-4320-5_7

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  • DOI: https://doi.org/10.1007/978-1-4612-4320-5_7

  • Publisher Name: Springer, New York, NY

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