Abstract
The comprehension of neuronal network functioning, from most basic mechanisms of signal transmission to complex patterns of memory and decision making, is at the basis of the modern research in experimental and computational neurophysiology. While mechanistic knowledge of neurons and synapses structure increased, the study of functional and effective networks is more complex, involving emergent phenomena, nonlinear responses, collective waves, correlation and causal interactions. Refined data analysis may help in inferring functional/effective interactions and connectivity from neuronal activity. The Transfer Entropy (TE) technique is, among other things, well suited to predict structural interactions between neurons, and to infer both effective and structural connectivity in small- and large-scale networks. To efficiently disentangle the excitatory and inhibitory neural activities, in the article we present a revised version of TE, split in two contributions and characterized by a suited delay time. The method is tested on in silico small neuronal networks, built to simulate the calcium activity as measured via calcium imaging in two-dimensional neuronal cultures. The inhibitory connections are well characterized, still preserving a high accuracy for excitatory connections prediction. The method could be applied to study effective and structural interactions in systems of excitable cells, both in physiological and in pathological conditions.
Highlights
Complex emergent dynamics in excitatory and inhibitory neuronal networks are at the basis of brain functions both in humans and lower-level organisms
In line with network analysis performed on experimental calcium, we focused our investigation on binary spike train signals opportunely extracted from down-sampled calcium data
We assess the performance of our algorithm on calcium fluorescence traces generated by simulating dynamics of a modeled neuronal network including 100 cells—80% excitatory and 20% inhibitory—connected via a spatial Gaussian kernel (Figure 1A, see Methods)
Summary
Complex emergent dynamics in excitatory and inhibitory neuronal networks are at the basis of brain functions both in humans and lower-level organisms. Structural connections naturally drive emergent dynamics that may result in different functional patterns depending on specific operating conditions, external stimuli, noise, and nonlinearities On this basis, neuronal assemblies at different scales are investigated both in terms of structural networks and in terms of functional and effective networks, describing correlations and causal interactions among cells, both in in silico and in experimental studies. The major drawbacks of those methods are the need for a double acquisition of network activity, successively blocking and non-blocking inhibitory interactions, the introduction of negative terms in the TE computation, and additional post-analysis based on TE features In this contribution, we present an alternative method to infer excitatory and inhibitory structural interactions among neurons based on the splitting of the TE index into two contributions and a proper selection of the delay between the time series. Our approach has potential impacts for a comprehensive investigation of organization principles of neural circuits both in physiological scenarios and in pathological conditions, including neurological diseases and disorders
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