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Modelling the cellular mechanisms of fly optic flow processing

MPG-Autoren
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Cuntz,  Hermann       
Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Max Planck Society;
Cuntz Lab, Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Max Planck Society;

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Zitation

Cuntz, H., Haag, J., & Borst, A. (2014). Modelling the cellular mechanisms of fly optic flow processing. In H. Cuntz, M. W. H. Remme, & B. Torben-Nielsen (Eds.), The computing dendrite: From structure to function (pp. 259-275). New York: Springer. doi:10.1007/978-1-4614-8094-5_16.


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-0024-E2CA-A
Zusammenfassung
Understanding the computational power of individual neurons is one of the major tasks in neuroscience. Complex calculations in dendrites and axons were identified in recent years in a great number of different systems. The small set of around 60 lobula plate tangential cells (LPTCs) in the fly visual system is a prime example where such computations and their underlying mechanisms are well understood. In this chapter we review recent findings resulting from detailed modelling studies based on experimental data from LPTCs. These studies have shown that the network connectivity is sufficient to explain the morphology of LPTCs to a large extent. Furthermore, an extensive network ubiquitously but highly selectively connects LPTC dendrites and axons with each other and is responsible for sophisticated optic flow calculations. The electrotonic features of LPTCs are affected by this network. We describe how a selective dendritic network between Horizontal System (HS) and Centrifugal Horizontal (CH) cells implements a specialised spatial filter operating on the moving images encoded in the dendrites. We also show how an axonal network renders the axon terminals of Vertical System (VS) cells in the fly selective to rotational optic flow. In summary, fly LPTCs are a prime example to show that the embedding and wiring within the network is crucial to understand morphology and computation.