Total spiking probability edges: A cross-correlation based method for effective connectivity estimation of cortical spiking neurons

Maximum entropy models provide functional connectivity estimates in neural networks

“…The finding that functional connections as obtained by CFP follow the rules of spike timing dependent plasticity 16 suggests that thus inferred functional connectivity at least in part reflects synaptic connections between neurons (see Supplementary information for an analysis that supports this connection). Model studies showed that cross-correlation based analyses are far more sensitive to excitatory connections than to inhibitory ones 12 , 17 – 19 , with possible exceptions of sparse networks 20 , or networks with high background activity 21 .…”

Thresholding Functional Connectivity Matrices to Recover the Topological Properties of Large-Scale Neuronal Networks

“…Any thresholding method should keep only statistically significant connections, i.e., those due to an active connectivity between the considered nodes. In this work, we obtained CMs by means of the Total Spiking Probability Edge (TSPE) algorithm, a correlation-based method, which allows discriminating inhibitory and excitatory connections (De Blasi et al, 2019). Since structural connectivity properties are preserved in the functional CM (Bullmore and Sporns, 2009), a reliable thresholding algorithm should maintain the significant connections of the functional graph, keeping unchanged the topological properties of the structural network as well as the balance of excitatory and inhibitory links.…”

“…Taken two spike trains X and Y, we randomly shuffled the timing of each spike in the Y train, keeping constant the total number of spikes (i.e., constant MFR) but varying the interspike temporal interval (i.e., ISI). Once the Y spike train is shuffled, we computed the temporal correlation (De Blasi et al, 2019) between the X-and Y-shuffled spike trains, obtaining a null-case value of connectivity between the neuron X and Y. By iterating such operation a number of time Nshuffling, we were able to obtain a distribution of values that quantify the strength of the functional connectivity between X and Y while the two neurons were not functionally connected.…”

“…One of the principal drawbacks of threshold-based algorithms is that to be conservative (i.e., to not introduce many FPs), a large number of connections is arbitrarily discarded, generating graphs with a small number of connections and altered topological properties. In this work, we tested the DDT method to recover the significant connections of functional CMs obtained by applying a cross-correlation algorithm (De Blasi et al, 2019) to synthetic spike trains generated by the simulation of large-scale neuronal network models. The goodness of the DDT algorithm was evaluated comparing the TCM with the structural one, known a priori since part of the in silico model.…”

Somatostatin interneurons activated by 5-HT2Areceptor suppress slow oscillations in medial entorhinal cortex

“…Units with a spiking rate smaller than 0.3 spikes/s were discarded from the analysis. We used total spiking probability edges (TPSE) algorithm (https://github.com/biomemsLAB/TSPE) (De Blasi et al, 2019) to identify in a computationally efficient manner putative inhibitory connections between units and all clusters recorded. The parameters used were: ▪ d = 0, ▪ neg_wins = [2, 3, 4, 5, 6, 7, 8], ▪ co_wins = 0, ▪ pos_wins = [2, 3, 4, 5, 6], ▪ FLAG_NORM = 1. The connectivity vectors of each unit resulting from TSPE were sorted by inhibition strength.…”