TY - JOUR
T1 - Inference of topology and the nature of synapses, and the flow of information in neuronal networks
AU - Borges, F. S.
AU - Lameu, Ewandson L.
AU - Iarosz, Kelly C.
AU - Protachevicz, Paulo R.
AU - Caldas, Iberê L.
AU - Viana, Ricardo L.
AU - Macau, Elbert E. N.
AU - Batista, Antonio M.
AU - Baptista, Murilo da Silva
N1 - ACKNOWLEDGEMENTS
CAPES, DFG-IRTG 1740/2, Fundacao Araucaria, Newton Fund, CNPq (154705/2016-0, 311467/2014-8), FAPESP (2011/19296-1, 2015/07311-7, 2016/16148-5, 2016/23398-8, 2015/50122-0), EPSRC-EP/I032606.
PY - 2018/2
Y1 - 2018/2
N2 - The characterisation 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 synchronisation, 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 inter modular connectivity, when only mean fields can be measured.
AB - The characterisation 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 synchronisation, 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 inter modular connectivity, when only mean fields can be measured.
U2 - 10.1103/PhysRevE.97.022303
DO - 10.1103/PhysRevE.97.022303
M3 - Article
VL - 97
SP - 1
EP - 7
JO - Physical Review. E, Statistical, Nonlinear and Soft Matter Physics
JF - Physical Review. E, Statistical, Nonlinear and Soft Matter Physics
SN - 1539-3755
IS - 2
M1 - 022303
ER -