Event Abstract

Membrane potential statistics reveal detailed correlation structure

  • 1 Bernstein Center, University of Freiburg, Germany

Much focus has been placed on determining the causes and functional roles of pairwise-correlations that are observed amongst neurons (Cohen and Kohn, 2011). In the pursuit of an understanding of the impact of correlations on network activity, an important division amongst them can be made, that of 'within' versus 'between'. These types of correlations are structurally defined, with 'within' (W) referring to the amount of correlated activity within the pre-synaptic pool of neurons projecting to a given neuron, and 'between' (B) referring to the amount of correlation between two pre-synaptic pools, each projecting to a different
post-synaptic cell. This distinction is important because these two types of correlations have different functional consequences: the later can serve to propagate existing correlations while the former influences firing rates (see Bujan et al. 2012).

Here we studied these two types of correlations in recurrent networks of excitatory and inhibitory spiking neurons. We find that in random homogeneous networks the W and B are comparable. Interestingly, in inhomogeneous random networks W and B greatly differ depending on the details of the network structure. Biological neural networks can be highly heterogeneous and thus we expect that even in vivo there will be difference in the values of W and B. Despite recent advances in the labeling of pre- or post-synaptic contacts of a neuron, it may not be possible to get enough details about network connectivity to reveal the differences between W and B experimentally.Fortunately, as our simulations show, statistics (variance and correlations) of the intracellular membrane potential could provide a good estimate of the W and B. Thus, in principle, the statistics of intracellular membrane potential could provide crucial information about the structure of the network.

Acknowledgements

Work funded by the German Federal Ministry of Education and Research (BMBF 01GQ0420 to BCCN Freiburg and 01GQ0830 to BFNT Freiburg/Tübingen), the EU (Facets-ITN), and DAAD Study Scholarship funding to Grace Lindsay

References

Cohen and Kohn (2011) Measuring and interpreting neuronal correlations. Nature Neurosci. 14(7) 811-819.
Bujan et al. (2012) Structure of stimulus induced correlations in random networks with distance dependent connectivity. COSYNE Abstract II-17

Keywords: clustering, correlations, membrane potential, network structure

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

Presentation Type: Poster

Topic: Abstracts

Citation: Lindsay G, Bujan AF, Aertsen A and Kumar A (2012). Membrane potential statistics reveal detailed correlation structure. Front. Comput. Neurosci. Conference Abstract: Bernstein Conference 2012. doi: 10.3389/conf.fncom.2012.55.00135

Received: 11 May 2012; Published Online: 12 Sep 2012.

* Correspondence: Ms. Grace Lindsay, Bernstein Center, University of Freiburg, Freiburg, Germany, gracewlindsay@gmail.com

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