Abstract/Summary

The problem of inferring the structural connections from the functional connections obtained from the activity of the neuronal networks is one of the major challenges in neuroscience. Studies suggest that maximum entropy based Ising models can discount the effect of indirect interactions and provide good results in inferring the underlying structural connections in neuronal networks. Parameters of the kinetic formulation of the Ising models, kinetic Ising models, have been found to agree well with anatomical connectivity in in silico models of neuronal networks. Following this, Ising and kinetic Ising models have attracted attention in the area of connectivity studies. However, the performance of the Ising couplings and kinetic Ising couplings have not been evaluated in comparison with other functional con- nectivity metrics in the microscopic scale of neuronal networks for a varied set of network conditions and network structures. This thesis sets out to resolve this through a comparative evaluation of the ability of Ising cou- plings and kinetic Ising couplings to unravel the structural connections when compared to the widely used functional connectivity metrics of partial and cross-correlations in in silico networks. The thesis presents the finding that the network correlation level deter- mines the relative performance of the functional connectivity metrics in de- tecting the synaptic connections. At weak levels of network correlation, Ising couplings and kinetic Ising couplings yielded better performance when com- pared to partial and cross-correlations. Whereas at strong levels of network correlation, partial correlations detected more structural links when com- pared to other functional connectivity metrics in this study. This result was consistent across varying firing rates, network sizes, densities and topologies. Along with being directional and applicable in nonstationary cases, kinetic Ising couplings also displayed better performance when compared to Ising couplings. The findings of this thesis serve as a guide in selecting the right functional connectivity tool to reconstruct the structural connectivity.