Abstract
Neuronal dynamics are fundamentally constrained by the underlying structural network architecture, yet much of the details of this synaptic connectivity are still unknown even in neuronal cultures in vitro. Here we extend a previous approach based on information theory, the Generalized Transfer Entropy, to the reconstruction of connectivity of simulated neuronal networks of both excitatory and inhibitory neurons. We show that, due to the model-free nature of the developed measure, both kinds of connections can be reliably inferred if the average firing rate between synchronous burst events exceeds a small minimum frequency. Furthermore, we suggest, based on systematic simulations, that even lower spontaneous inter-burst rates could be raised to meet the requirements of our reconstruction algorithm by applying a weak spatially homogeneous stimulation to the entire network. By combining multiple recordings of the same in silico network before and after pharmacologically blocking inhibitory synaptic transmission, we show then how it becomes possible to infer with high confidence the excitatory or inhibitory nature of each individual neuron.
Highlights
Important advances in the last decade have provided unprecedented detail on the structure and function of brain circuits [1,2] and even programs aiming at an exhaustive mapping of the brain connectome(s) have been announced [3,4,5,6]
These arguments call for highly controllable experimental frameworks in which the results and predictions of different functional connectivity analysis techniques can be reliably tested in different dynamic regimes
For this purpose we developed an extension of TE, termed Generalized Transfer Entropy (GTE)
Summary
Important advances in the last decade have provided unprecedented detail on the structure and function of brain circuits [1,2] and even programs aiming at an exhaustive mapping of the brain connectome(s) have been announced [3,4,5,6]. Functional imaging has provided non-invasive measures of brain activity, both at rest [8] and during the realization of specific tasks [2] These efforts have opened new perspectives in neuroscience and psychiatry, for instance to identify general principles underlying interactions between multi-scale brain circuits [9,10], to probe the implementation of complex cognitive processes [11,12], and to design novel clinical prognosis tools by linking brain pathologies with specific alterations of connectivity and function [13,14,15]. Particular care is required when interpreting data originating from non-invasive functional data-gathering approaches such as fMRI [23] These arguments call for highly controllable experimental frameworks in which the results and predictions of different functional connectivity analysis techniques can be reliably tested in different dynamic regimes
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