Fig. 5: Stereotypy in simulations with the same input drives for both odors.
From: Multiple network properties overcome random connectivity to enable stereotypic sensory responses
a, b Stereotypy in total KC input (a) and total KC response (b) when the total input drives to both odors are equal. c Stereotypy in total KC response versus the range of PN spiking rates when the range of spiking rates is changed to the indicated values for one odor while it is maintained at 10–30 for the other. d Stereotypy in total KC response versus the number of active PNs when the number is changed to the indicated values for one odor while it is maintained at 25 for the other. e Similar to (c) except that the range of PN spiking rates is changed for both odors. f Similar to (d) except that the number of active PNs is changed for both odors. g, h Plots similar to (c) and (d), respectively, but with the modification that a linear transfer function, yi = mxi − t, is used to generate KC responses from their inputs. Here, yi is the response of the ith KC, xi is the input to the ith KC, t is the threshold, and m is chosen such that the total KC responses are equal to those seen in the default simulations. i Stereotypy in total KC response when the PN responses to the second odor are generated by shuffling the PN labels in the response to the first odor. In this condition, the total PN output, the number of active PNs, and the spiking range of active PNs are identical for the two odors. j Analytical model confirmed that there is no stereotypy, regardless of the number of KCs, when the PN responses to the two odors are shuffled versions of each other. In all panels, n = 100 iterations; error bars represent s.e.m.
