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  • Poster presentation
  • Open Access

Successful prediction of a physiological circuit with known connectivity from spiking activity alone

  • 1Email author,
  • 2,
  • 2,
  • 2,
  • 3 and
  • 3
BMC Neuroscience201314 (Suppl 1) :P118

https://doi.org/10.1186/1471-2202-14-S1-P118

  • Published:

Keywords

  • Point Process
  • Spike Train
  • Effective Connectivity
  • Causal Network
  • Neural Network Dynamic

Identifying the structure and dynamics of synaptic interactions between neurons is the first step to understanding neural network dynamics. The presence of synaptic connections is traditionally inferred through the use of targeted stimulation and paired recordings or by post-hoc histology. More recently, causal network inference algorithms have been proposed to deduce connectivity directly from electrophysiological signals, such as extracellularly recorded spiking activity. These algorithms have not been validated on a neurophysiological data set for which the actual circuitry is known. Recent work has shown that traditional network inference algorithms based on linear models typically fail to identify the correct coupling of even a basic three-neuron circuit like the crab stomatogastric nervous system.

In this work, we show that point process models of observed spike trains can guide inference of relative connectivity estimates that match the known physiological connectivity of a three-neuron circuit up to a choice of threshold. We elucidate the necessary steps to derive faithful connectivity estimates from a model that incorporates the spike train nature of the data. We then apply the model to measure changes in the effective connectivity pattern in response to two pharmacological interventions, which affect both intrinsic neural dynamics and synaptic transmission.

Our results provide the first successful application of a network inference algorithm to a circuit for which the actual physiological synapses between neurons are known. The point process methodology presented here generalizes well to larger networks and can describe the statistics of neural populations. In general we show that advanced statistical models allow for the characterization of effective network structure, deciphering underlying network dynamics and estimating information-processing capabilities.

Declarations

Acknowledgements

The authors thank Anatoly Rinberg for supplying the CsCl data. FG is supported by the Swiss National Science Foundation (SNSF, 200020-132871). EM and GJG are supported by NIH grant MH 46742, and TK is supported by NIH grant F32 NS099590. UE is supported by NSF grant IIS 0643993 and NINDS grant R01 NS073118. MK and UE acknowledge joint support by NINDS grant R01 NS072023.

Authors’ Affiliations

(1)
Brain Mind Institute, Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, Switzerland
(2)
Biology Department and Volen Center, Brandeis University, Waltham, MA, USA
(3)
Department of Mathematics and Statistics, Boston University, Boston, MA, USA

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