A benefit of the multilevel architecture could be to amplify the effect of mechanosensory cues on nociceptive cues, increasing the sensitivity to relatively weak bimodal cues. To explore this idea further, we used a simple model to ask whether a two‐level network with two levels of convergence (multilevel convergence; MC) can be more sensitive to relatively weak bimodal events than a network with only early convergence (EC). a, Schematic for a simple model of early and multilevel convergence networks (see Methods for details). We considered an early convergence and a multilevel convergence two‐layer feed‐forward circuit with two inputs, corresponding to MD IV (E Noci ) and chordotonal (E Mech ), and one output corresponding to rolling occurrence. We modelled steady‐state firing rates, where the output of each model neuron is a sigmoidal logistic function (with a lower threshold and upper saturation) of a weighted sum of its inputs. One pathway remains only chordotonal, while the other mixes modalities only early, or at both levels, depending on weights (w M and w B ). b–d, Solutions for w M and w B are found using a constrained optimization procedure that maximizes sensitivity to weak bimodal inputs (b, c) within two experimentally observed unimodal target outputs (d): (1) that mechanosensory stimulus alone never evokes rolling; and (2) that the strongest MD IV stimulus alone evokes only 30% output (Fig. 1e, f and see Methods for details). e, Example deviation h from target outputs (see Methods) for multiple values of w M and w B for one set of thresholds (θ p = 40; θ M = 50; θ B = 75). f, Values of w M and w B that maximize sensitivity S (dots) while also satisfying h < 3 (grey area). g, Early and multilevel solutions (if they exist) for other thresholds. Solutions exist if one or both θ M or θ B is high (keeping θ p = 40). Note that although the optimal sensitivity multilevel convergence solution could have w M = 0 and thus effectively be an early convergence solution, this does not occur; multilevel convergence solutions are always more sensitive when they exist. Multilevel convergence solutions with w B = 0 are not shown since they do not exhibit multilevel convergence. h, Sensitivity of the optimal multilevel convergence solution as a function of the same thresholds as in Extended Data Fig. 8g, normalized by the most sensitive multilevel convergence solution found. Multilevel convergence solutions are more sensitive to relatively weak multimodal stimuli than early convergence solutions. Across all thresholds tested, the most sensitive circuits occur for θ M = 50 and θ B = 75 (Extended Data Fig. 8k). For such parameters, no early convergence solutions satisfy the unimodal constraints, hence multilevel convergence solutions are overall the most sensitive. i, j, Example early convergence and multilevel convergence solutions for w B and w B that satisfy the condition in d for one set of neuronal firing thresholds (θ p = 40; θ M = 50; θ B = 75). k, Subtracting the output of early convergence from the multilevel convergence circuit. The multilevel convergence circuit triggers more rolling than the early convergence circuit in response to relatively weak bimodal cues and strong unimodal MD IV cues, but less rolling than the early convergence circuit to strong unimodal chordotonal cues (top left corner). The multilevel convergence circuit thus offers a more complex response function with greater sensitivity and selectivity for multimodal and strong unimodal MD IV cues, but not chordotonal cues, and could enable enhanced selection of rolling to more threatening events and reduced selection of rolling to less threatening events.