The high number of neurons contributes to the robustness of the locust flight-CPG against parameter variation

Abstract

Real pattern-generating networks often consist of more neurons than necessary for the production of a certain rhythm. We investigated the question of whether these neurons contribute to the robustness of a pattern-generating system of using the central pattern generator (CPG) for flight of the locust, generating the deafferented activity pattern of wing elevator and wing depressor motoneurons, as an example of a rhythm-generating system. The neuronal network was reconstructed, based on the known connectivity of the interneurons in the flight CPG, using a biologically orientated network simulator (BioSim 3.0). This simulator allows a physiologically realistic simulation of particular neurons as well as the synaptic connections between them. The flight CPG consists of at least five cyclic loops. The simulation shows that each of them is in principle able to produce a rhythm comparable to the rhythm produced by the whole network, i.e. the ‘deafferented’ flight pattern of elevator and depressor motoneurons. Varying the parameter ‘synaptic strength’ in each of these loops and in the complete system shows that this parameter can be changed within certain ranges without loosing the ability to produce oscillations. These ranges are much smaller in each of the subloops than in the whole network. This result demonstrates that the robustness of the system is increased by supranumerary neurons and connections. Changing the active properties of the simulated neurons so that they are able to produce plateau potentials has no effect on the robustness of the simulated network.