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. Author manuscript; available in PMC: 2022 Oct 13.
Published in final edited form as: Neural Comput. 2022 Sep 9;34(11):2205–2231. doi: 10.1162/neco_a_01540

Figure 1.

Figure 1

The flip-flop network. Protocerebral bilateral neurons (PBN) and flip-flop neurons (FF) both get input directly from the ipsilateral sensor. PBNs inhibit the contralateral FF. FF neurons activate the motor. For the spiking model, each neuron is comprised of 100 spiking components, recurrently connected so as to achieve an approximation of the rate-mode behaviour. The output from each neuron is a weighted sum of the output of its components. The final output controls the turning rate of the moth, and is computed as the left FF output minus the right FF output times a fixed scaling factor. Activity shown here is for the spiking network. See Fig. S1 for a comparison of activity propagation through the rate-based vs. spiking model given artificial input. See supplementary video for an animated version of this figure.