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Tuesday, July 21 • 6:15pm - 6:45pm
W1 S5: Inference of topology and the nature of synapses in neuronal networks

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Workshop on Methods of Information Theory in Computational Neuroscience

Fernando da Silva Borges
Federal University of ABC

"Inference of topology and the nature of synapses in neuronal networks"

The characterization of neuronal connectivity is one of the most important matters in neuroscience. In this work, we show that a recently proposed informational quantity, the causal mutual information, employed with an appropriate methodology, can be used not only to correctly infer the direction of the underlying physical synapses, but also to identify their excitatory or inhibitory nature, considering easy to handle and measure bivariate time series. The success of our approach relies on a surprising property found in neuronal networks by which non adjacent neurons do "understand" each other (positive mutual information), however, this exchange of information is not capable of causing effect (zero transfer entropy). Remarkably, inhibitory connections, responsible for enhancing synchronization, transfer more information than excitatory connections, known to enhance entropy in the network. We also demonstrate that our methodology can be used to correctly infer directionality of synapses even in the presence of dynamic and observational Gaussian noise, and is also successful in providing the effective directionality of intermodular connectivity, when only mean fields can be measured.

avatar for Fernando S Borges

Fernando S Borges

Postdoctoral research, Department of Physiology & Pharmacology, SUNY Downstate Health Sciences
Research in computational neuroscience with 28 publications in peer reviewed journals, and PI/co-PI in 7 research grants. Lectured undergraduate courses, and organized Courses on Computational Modeling. Investigates neural network models with research mainly focused on neuronal synchronization... Read More →

Tuesday July 21, 2020 6:15pm - 6:45pm CEST
Crowdcast (W01)