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Impact of network topology on inference of synaptic connectivity from multi-neuronal spike data simulated by a large-scale cortical network model

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Abstract

Many mechanisms of neural processing rely critically upon the synaptic connectivity between neurons. As our ability to simultaneously record from large populations of neurons expands, the ability to infer network connectivity from this data has become a major goal of computational neuroscience. To address this issue, we employed several different methods to infer synaptic connections from simulated spike data from a realistic local cortical network model. This approach allowed us to directly compare the accuracy of different methods in predicting synaptic connectivity. We compared the performance of model-free (coherence measure and transfer entropy) and model-based (coupled escape rate model) methods of connectivity inference, applying those methods to the simulated spike data from the model networks with different network topologies. Our results indicate that the accuracy of the inferred connectivity was higher for highly clustered, near regular, or small-world networks, while accuracy was lower for random networks, irrespective of which analysis method was employed. Among the employed methods, the model-based method performed best. This model performed with higher accuracy, was less sensitive to threshold changes, and required less data to make an accurate assessment of connectivity. Given that cortical connectivity tends to be highly clustered, our results outline a powerful analytical tool for inferring local synaptic connectivity from observations of spontaneous activity.

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Acknowledgements

This study is partially supported by Support Center for Advanced Telecommunications Technology Research (RK) and by JSPS KAKENHI Grant Number 21700334 (KK).

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Authors and Affiliations

  1. Department of Human and Computer Intelligence, Ritsumeikan University, 1-1-1 Nojihigashi, Kusatsu, Shiga, 525-8577, Japan

    Ryota Kobayashi & Katsunori Kitano

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  1. Ryota Kobayashi
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  2. Katsunori Kitano
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Correspondence to Katsunori Kitano.

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Figure S1

Raster displays during an episodic burst and dependence of firing rates and Cvs on locations. ( A ) Raster plots during an episodic burst for the various network topologies. Spatial distributions of the averaged firing rates ( B ) and Cvs ( C ). The unit of color scale in ( B ) is spike/s. (JPEG 584 kb)

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Kobayashi, R., Kitano, K. Impact of network topology on inference of synaptic connectivity from multi-neuronal spike data simulated by a large-scale cortical network model. J Comput Neurosci 35, 109–124 (2013). https://doi.org/10.1007/s10827-013-0443-y

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