We next investigated the relationship between CC and the dispersion of patterns along the transverse axis. Interestingly, we found that CC increased at larger z dispersions ( Figure 5D; linear fit, slope = 0.037/μm). This behavior strongly differs from both random connectivity Venetoclax in vivo predictions, which exhibit a mostly constant CC (p = 3.3 × 10−5). This result means that the neurons in the triplet patterns with high CC values can be on different sagittal planes distributed
across the transverse axis. In conclusion, the chemical network has more clustered and transitive features than both random connectivity models predict and shows signs of spatial specificity. After demonstrating the existence of structured features in the electrical and chemical networks, we investigated the overlap of the two networks. Because the number of potential individual mixed triplet patterns is very large (n = 128), we instead performed a common neighbor analysis (Perin et al., 2011). This is a method for investigating higher-order connectivity, and, in this case, the relationship between different connection types. It examines the effect of a common connected neighbor on the probability of connections of a given pair. We
compared three probabilities: first, measured between pairs that have a common neighbor; second, measured between all other pairs (with no recorded common neighbor); and finally, predicted by the nonuniform random model, based on the distance between the pairs with common neighbor (and Vismodegib predicted by the nonuniform random model with ML position; Figure S6). The first comparison (pairs with common neighbor and all other pairs) offers an assessment of the higher-order structure within the data without the use
of an explicit model of connectivity, but only under the assumption of independent connection probabilities. To simplify, we restricted the pair probability types to three: no connection, electrical Resminostat connection, and chemical connection. First, the presence of an electrical common neighbor (Figure 6A, n = 137) led to a higher probability of an electrical connection and a reduced probability of no connection compared to the other pairs (χ2 test, p = 7.39 × 10−30 and 1.11 × 10−11, respectively) and to the nonuniform random prediction (Monte Carlo, p = 0.0003 and 0.0003, respectively). This is consistent with the results shown in Figures 4A and 4B and confirms the preference for electrical clustered connectivity without the use of an explicit model of connectivity. Next, we examined the effect of a mixed (electrical and chemical) common neighbor on the pair connection probability (Figure 6B, n = 37).
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