Event Abstract

Stimulus driven correlation gain modulation in neuronal networks

  • 1 University of Freiburg, Bernstein Center Freiburg, Germany

In order to analyze the response of a neuronal network to an external source of correlated inputs it is useful to divide the input correlations into “between” and “within” correlations (Yim et al. 2011). The “between” correlations (B) are defined as the mean pairwise correlation between spike trains from the presynaptic pools of different neurons, whereas “within” correlations (W) refer to the mean pairwise correlation between spike trains belonging to the same presynaptic pool. In a random network the two types of correlations are same, however, in inhomogeneous networks W and B may differ strongly (Lindsay et al. 2012). From an abstract point of view, neurons in a recurrent network receive just two different sources of input, one coming from outside the network and the other coming from the network itself, each of these sources may have a different structure in terms W and B and the interplay between these two structures will define the correlation gain of the network. Correlation gain of a network can be adequately studied using a reduced two-neuron model. Here, we consider the case of two neurons receiving correlated inputs corresponding to the external and local network input. We show that the interplay between the correlation structure of local and external inputs provides a flexible mechanism to dynamically modulate (increase or decrease) network correlations. The correlations W and B are shaped by both the structure of the connectivity as well properties of the input. Thus, beyond proposing a mechanism to modulate correlations our findings clearly suggest that it is highly important to know the connectivity structure of the input projections as well that of the receiving network in order to correctly predict the impact of sensory and/or top-down inputs on the neural activity.

Acknowledgements

Work funded by the German Federal Ministry of Education and Research (BMBF 01GQ0420 to BCCN Freiburg and 01GQ0830 to BFNT Freiburg/Tuebingen) and the EU (Facets-ITN)

References

Yim M.Y., Aertsen A., Kumar A. (2011) Significance of input correlations in striatal function PLoS Computational Biology 7(11): e1002254. e1002254. doi: 10.1371/journal.pcbi.1002254

Lindsay G., Bujan A. F., Aertsen A., Kumar A. (2012) Membrane potential statistics reveal detailed correlation structure. Front. Comput. Neurosci. Conference Abstract: Bernstein Conference 2012.

Keywords: Correlation gain, Input correlations, Integrate and fire neuron, network structure

Conference: Bernstein Conference 2012, Munich, Germany, 12 Sep - 14 Sep, 2012.

Presentation Type: Poster

Topic: Other

Citation: F. Bujan A, Aertsen A and Kumar A (2012). Stimulus driven correlation gain modulation in neuronal networks. Front. Comput. Neurosci. Conference Abstract: Bernstein Conference 2012. doi: 10.3389/conf.fncom.2012.55.00147

Received: 18 Sep 2012; Published Online: 12 Sep 2012.

* Correspondence:
Mr. Alejandro F. Bujan, University of Freiburg, Bernstein Center Freiburg, Freiburg im Breisgau, Germany, alejandro.bujan@bcf.uni-freiburg.de
Dr. Arvind Kumar, University of Freiburg, Bernstein Center Freiburg, Freiburg im Breisgau, Germany, arvind.k.panchal@gmail.com

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