RobustCircuit Project 7

Project 7 focuses on the role of heterogeneous ionic current differentiation and network connectivity for robustly patterned motor output from Drosophila wing depressor motoneurons.

Imprecision: Neuronal development leads to a motor circuit whose neurons differ in intrinsic properties (like ion channel composition). The electrical coupling amongst neurons is also variable.

Robustness: The circuit produces very robust network activity. This activity is splayed-out in time (supporting stable muscle activation during flight) and shows a specific preference in the sequence of neuronal activation.

Hypothesis: Variability of intrinsic neuronal properties and electrical coupling is not detrimental, but rather supports a stable network splay state for efficient activation of flight muscles with a potentially reduced requirement for complex regulatory control.

Project Summary

Motor control of complex behavior relies on the generation of specific activation patterns in specialized neuronal circuits [1-3]. Mature motocircuits, therefore, require a well-orchestrated combination of intrinsic neuronal properties, such as morphology [4], ion channel composition [5], and network structure [6]. In some cases, precision of these properties can be important for function, although variability in circuit components is tolerated to some extent [7]. In other cases, variability of intrinsic neuronal properties or circuit components may be advantageous in the context of trade-offs (like the cost required for more precision) or in fact be directly beneficial for robust circuit function. The goal of P7 is to distinguish between these possibilities within the framework of a simple central pattern generator that provides data constraints and testability of a computational modeling approach (together with P6). Specifically, we seek to understand how imprecisions in the development of the central pattern generator underlying fly wing beat oscillation (at the levels of intrinsic properties of the constituent neurons as well as network connectivity) affect the robust function of the mature circuit. In preliminary experiments for RobustCircuit we have identified specific instances for each of the two beneficial roles for variability of intrinsic neuronal properties or circuit components: (i) we hypothesize that imprecise neuronal function (based on ion channel noise) benefits re-establishment of a preferred network mode after perturbation, and (ii) we hypothesize that developmental imprecisions (including gap junctions between motoneurons) facilitate the self-organization of robust network motifs by lowering the ‘regulatory cost’ compared to a more precise genetic specification. Taken together, this project aims to uncover beneficial roles of variability based on mechanistic principles that are likely to generalize beyond the system at hand.

References

1. Pallasdies F, Norton P, Schleimer JH, Schreiber S. (2021). Neural optimization: Understanding trade-offs with Pareto theory. Curr Opin Neurobiol. 2021 Dec;71:84-91.
2. Contreras S.A., Schleimer J.-H., Gulledge A.T., Schreiber S. (2021): Activity-mediated accumulation of potassium induces a switch in firing pattern and neuronal excitability type. PLoS Comput Biol, https://doi.org/10.1101/2020.11.30.403782
3. Schleimer J.H., Hesse J., Contreras S.A., Schreiber S. (2021): Firing statistics in the bistable regime of neurons with homoclinic spike generation. Phys Rev E, 103, 012407.
4. Hesse J., Schreiber S. (2019): How to correctly quantify neuronal phase-response curves from noisy recordings. Journal of Computational Neuroscience, 47(1), 17-30.
5. Remme M.W.H., Rinzel J., Schreiber S. (2018): Function and energy consumption constrain neuronal biophysics in a canonical computation: Coincidence detection. PLoS Comput Biol 14(12): e1006612.
6. Schleimer J.-H., Schreiber S. (2018): Phase-response curves of ion channel gating kinetics. Math Meth Appl Sci 41, 8844-8858.