Abstract
A systematic assessment of global neural network connectivity through direct electrophysiological assays has remained technically infeasible, even in simpler systems like dissociated neuronal cultures. We introduce an improved algorithmic approach based on Transfer Entropy to reconstruct structural connectivity from network activity monitored through calcium imaging. We focus in this study on the inference of excitatory synaptic links. Based on information theory, our method requires no prior assumptions on the statistics of neuronal firing and neuronal connections. The performance of our algorithm is benchmarked on surrogate time series of calcium fluorescence generated by the simulated dynamics of a network with known ground-truth topology. We find that the functional network topology revealed by Transfer Entropy depends qualitatively on the time-dependent dynamic state of the network (bursting or non-bursting). Thus by conditioning with respect to the global mean activity, we improve the performance of our method. This allows us to focus the analysis to specific dynamical regimes of the network in which the inferred functional connectivity is shaped by monosynaptic excitatory connections, rather than by collective synchrony. Our method can discriminate between actual causal influences between neurons and spurious non-causal correlations due to light scattering artifacts, which inherently affect the quality of fluorescence imaging. Compared to other reconstruction strategies such as cross-correlation or Granger Causality methods, our method based on improved Transfer Entropy is remarkably more accurate. In particular, it provides a good estimation of the excitatory network clustering coefficient, allowing for discrimination between weakly and strongly clustered topologies. Finally, we demonstrate the applicability of our method to analyses of real recordings of in vitro disinhibited cortical cultures where we suggest that excitatory connections are characterized by an elevated level of clustering compared to a random graph (although not extreme) and can be markedly non-local.
Highlights
The identification of the topological features of neuronal circuits is an essential step towards understanding neuronal computation and function
After a brief presentation of the qualitative similarity between real calcium fluorescence data from neuronal cultures and simulated data, we introduce numerical simulations showing that networks with very different clustering levels can lead to matching bursting dynamics
We show that only signals recorded during inter-burst periods convey elevated information about the underlying structural topology
Summary
The identification of the topological features of neuronal circuits is an essential step towards understanding neuronal computation and function. Even in the case of cultures of dissociated neurons, in which neuronal connections develop de novo during the formation and maturation of the network, very few details are known about the statistical features of this connectivity, which might reflect signatures of self-organized critical activity [9,10,11]. Neuronal cultures have emerged in recent years as simple, yet versatile model systems [12,13] in the quest for uncovering neuronal connectivity [14,15] and dynamics [16,17,18,19]. The activity of hundreds to thousands of cells in in vitro cultured neuronal networks can be simultaneously monitored using calcium fluorescence imaging techniques [14,21,22]. The poor signalto-noise ratio makes the detection of elementary firing events difficult
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