Transfer entropy reconstruction and labeling of neuronal connections from simulated calcium imaging

• Published in: PloS one Vol. 9; no. 6; p. e98842
• Main Authors: Orlandi, Javier G, Stetter, Olav, Soriano, Jordi, Geisel, Theo, Battaglia, Demian
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 06.06.2014; Public Library of Science (PLoS)
• Subjects: Algorismes computacionals; Bioinformatics; Bioinformàtica; Biology and Life Sciences; Calcium; Calcium - metabolism; Calcium imaging; Calcium signalling; Computational Biology - methods; Computer algorithms; Computer and Information Sciences; Computer simulation; Connectivity; Entropy; Firing rate; Image Processing, Computer-Assisted - methods; Information theory; Models, Biological; Nerve Net - cytology; Nerve Net - physiology; Neural networks; Neural networks (Neurobiology); Neurochemistry; Neurons; Neurons - cytology; Neurons - metabolism; Neuroquímica; Neurosciences; Optical Imaging; Pharmacology; Physical Sciences; Reconstruction; Simulació per ordinador; Sinapsi; Studies; Synapses; Synaptic transmission; Topologia; Topology; Xarxes neuronals (Neurobiologia); Germany
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0098842

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.