Inferring network structure and local dynamics from neuronal patterns with quenched disorder
Section snippets
Results
The model. We consider a network of N nodes, labelled with the progressive discrete index i. Each node identifies a LIF neuron. Neurons have equal synapses and interact via the synaptic currents that are regulated by short-term plasticity. This model was proved capable of reproducing a large variety of dynamical regimes, from a-synchronous to quasi-synchronous regimes, encompassing bursting and chaotic behaviors [23], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39]. Each neuron is
Discussion and conclusion
Recovering functional and structural information from in vivo measurements of brain activity is a challenge of broad applied and fundamental relevance. Working in this framework we have here proposed, and successfully tested, a novel reconstruction method which aims at inferring information on the distributions of both firing rates and networks connectivity, from global activity measurements. The technique builds on a variant of the celebrated Leaky-Integrate and Fire (LIF) model, with
Experimental setup
All procedures involving mice were performed in accordance with directive 2010/63/EU on the protection of animals used for scientific purposes and approved by the Italian Minister of Health, authorization n.183/2016-PR. Mice were housed in clear plastic cages under a 12 h light/dark cycle and were given ad libitum access to water and food. We used a transgenic mouseline (C57BL/6J-Tg(Thy1GCaMP6f)GP5.17Dkim/J) (referred to as GCaMP6f mice) expressing a genetically-encoded fluorescent calcium
Author Credits
D.F. and R.L. designed the study and carried out the supervision. I.A., D.F., M. d.V., G. C. worked at the setting of the model. I.A. carried out the implementation of the final code and ran the numerical tests. A.L.A.M., E.C., A.S. carried out the experiments and worked at the handling of the raw data. D.F. wrote the paper. All authors contributed to the editing of the manuscript and to the interpretation of the results.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
This project received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreements No. 720270 (HBP SGA1), 785907 (HBP SGA2) and 654148 (Laserlab-Europe), and from the EU programme H2020 EXCELLENT SCIENCE - European Research Council (ERC) under grant agreement ID No. 692943 (BrainBIT).
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