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Extraction of network topology from multielectrode recordings: is there a smallworld effect?
(2011)

Felipe Gerhard
Gordon Pipa
Bruss Lima
Sergio Neuenschwander
Wulfram Gerstner
 The simultaneous recording of the activity of many neurons poses challenges for multivariate data analysis. Here, we propose a general scheme of reconstruction of the functional network from spike train recordings. Effective, causal interactions are estimated by fitting generalized linear models on the neural responses, incorporating effects of the neurons’ selfhistory, of input from other neurons in the recorded network and of modulation by an external stimulus. The coupling terms arising from synaptic input can be transformed by thresholding into a binary connectivity matrix which is directed. Each link between two neurons represents a causal influence from one neuron to the other, given the observation of all other neurons from the population. The resulting graph is analyzed with respect to smallworld and scalefree properties using quantitative measures for directed networks. Such graphtheoretic analyses have been performed on many complex dynamic networks, including the connectivity structure between different brain areas. Only few studies have attempted to look at the structure of cortical neural networks on the level of individual neurons. Here, using multielectrode recordings from the visual system of the awake monkey, we find that cortical networks lack scalefree behavior, but show a small, but significant smallworld structure. Assuming a simple distancedependent probabilistic wiring between neurons, we find that this connectivity structure can account for all of the networks’ observed smallworldness. Moreover, for multielectrode recordings the sampling of neurons is not uniform across the population. We show that the smallworldness obtained by such a localized subsampling overestimates the strength of the true smallworld structure of the network. This bias is likely to be present in all previous experiments based on multielectrode recordings.

FIAS Scientific Report
(2011)