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. Author manuscript; available in PMC: 2021 Dec 21.
Published in final edited form as: Curr Biol. 2020 Oct 15;30(24):4896–4909.e6. doi: 10.1016/j.cub.2020.09.045

Figure 7. Graded-to-binary conversion of sensory encodings improves the accuracy in yes-or-no detections.

Figure 7.

(A) Graded encodings convey fine-scale intensity information while binary encodings narrow the fine-scale (precise) encoding range.

(B) Diagram of the computational modeling of yes-or-no detection networks without (left) and with (right) the gated amplification mechanism (stimulus-dependent-recruitment of 58 neurons and the gating, see STAR Methods for details).

(C) Plots that show the sensory encoding values in yes-or-no detection tasks of a randomly selected, simulated larva at the noise level of 20%. Upper panel: simulations without the gated amplification mechanism; lower panel: simulations with the gate amplification mechanism. A noise level of 20% is defined as the standard deviation of the sensory encoding value that is 20% of the saturated encoding value. Each simulated larva performed 1,000 detections in response to 1,000 sensory stimulus intensities ranging from 0 to 100. Each detection generated a sensory encoding value, which is represented in the plots by a dot (- shown are those generated by the stimulus intensities between 20 and 80).

(D) The detection accuracies of the two networks at various noise levels. Each dot represents the detection accuracy at the indicated noise level. The detection accuracy at a noise level is calculated based on the detections from 1,000 simulated larvae responding to stimulus intensities ranging from 20 to 80. Fisher’s exact test.

(E) The ambiguous range of sensory encodings of the two networks at the noise level of 20%. Each dot represents the ambiguous range of a simulated larva. Unpaired t-test. The ambiguous encoding range is defined as the stimulus intensity range in which false detections appear, as shown in (C). Lines represent the means of ambiguous range.

(F) Gating of weak encodings by GABAergic inhibition and escalated amplification of stronger encodings by SON recruitment make graded nociceptive encodings more binary so that noxious stimuli are categorized for enhanced detection accuracy.

See also Figure S6.