Visually guided orientation in flies: case studies in computational neuroethology

Abstract

To understand the functioning of nervous systems and, in particular, how they control behaviour we must bridge many levels of complexity from molecules, cells and synapses to perception behaviour. Although experimental analysis is a precondition for understanding by nervous systems, it is in no way sufficient. The understanding is aided at all levels of complexity by modelling. Modelling proved to be an inevitable tool to test the experimentally established hypotheses. In this review it will by exemplified by three case studies that the appropriate level of modelling needs to be adjusted to the particular computational problems that are to be solved. (1) Specific features of the highly virtuosic pursuit behaviour of male flies can be understood on the basis of a phenomenological model that relates the visual input to the motor output. (2) The processing of retinal image motion as is experienced by freely moving animals can be understood on the basis of a model consisting of algorithmic components and components which represent a simple equivalent circuit of nerve cells. (3) Behaviourally relevant features of the reliability of encoding of visual motion information can be understood by modelling the transformation of postsynaptic potentials into sequences of spike trains.

Crossmodal Visual Input for Odor Tracking during Fly Flight

“…Once initiated, saccade dynamics are coordinated entirely by mechanosensory feedback [11], whereas flying straight and avoiding collisions require well-studied optomotor equilibrium reflexes [12]. Here, we show that saccades in the odor plume are fewer and smaller, but not altogether absent.…”

Method and software for using m-sequences to characterize parallel components of higher-order visual tracking behavior in Drosophila

“…The evidence from that work points generally to cortical mechanisms for feature extraction. In contrast, a half century of work in flies has shown that these animals accomplish similar feature extraction within the secondary and tertiary optic ganglia—the medulla, lobula, and lobula plate (Egelhaaf, 1985a,b,c; Reichardt et al, 1989; Egelhaaf et al, 1993, 2003; Kimmerle and Egelhaaf, 2000; Aptekar et al, 2012; Fox et al, 2014). As these systems become more tractable with the advent of genetic tools for lesioning and imaging specific subsets of cells within these parts of the fly brain, in addition to the completion of full-fledged wiring diagrams, the need for more nuanced behavioral tools is acute.…”

Electric imaging through active electrolocation: implication for the analysis of complex scenes

“…However, we here present some behavioural examples that strengthen the idea that all sensory processing must be seen as an ongoing and hence "active" process, where neither sensory images nor exploratory behaviours are fixed. Instead, animals make use of the changes in their sensory world that for instance occur whenever there is relative motion between fish and objects, something we here refer to as the electrical flow (Egelhaaf et al 2003;Karmeier et al 2006). In contrast to visual flow, however, electric flow is less suitable for orientation in complex electrical environments because electric images are superposition images (Migliaro et al 2005).…”